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Sensitivity of productivity and deep drainage of wheat cropping systems in a Mediterranean environment to changes in CO2, temperature and precipitation
Abstract
Both anticipated climate change and dryland salinity pose a strategic threat to the sustainability of about 6 Mha of agricultural land in Western Australia (WA). These phenomena require an integrated analysis to estimate their potential impacts and initiate strategic thinking about adaptation. Water loss below the root zone, i.e. deep drainage, is the primary cause of sub-soil salt mobilisation leading to surface soil salinity in areas cleared of natural vegetation in Australia; hence deep drainage is an important externality of agricultural production. The purpose of this paper is to show how changes in CO2 concentration, temperature and precipitation may affect agricultural production and deep drainage.
... Changing temperature has important effect on the phenology of cotton and affects cotton quality and yield (Luo et al., 2016). Higher temperatures can accelerate maturity and reduce the accumulation time of dry matter, thus reducing yields (Van Ittersum et al., 2003). However, in high latitudes where the growing season is currently limited, rising temperatures may help increase crop yields. ...
... But changes in climatic conditions (such as temperature and precipitation amount) may offset this positive effect (Hatfield and Prueger, 2015;Nasim et al., 2016). Many studies have assessed the impact of climate change on cotton growth (Chen et al., 2015;Ureta et al., 2020). Current methods to study climate change impacts include free-air CO 2 enrichment (Zhang et al., 2017), statistical analysis (Ureta et al., 2020), and crop model simulation (Chen et al., 2019). ...
... Many studies have assessed the impact of climate change on cotton growth (Chen et al., 2015;Ureta et al., 2020). Current methods to study climate change impacts include free-air CO 2 enrichment (Zhang et al., 2017), statistical analysis (Ureta et al., 2020), and crop model simulation (Chen et al., 2019). Statistical analysis methods were mainly used to analyze the relationship between actual observed historical long time series climate elements and crop growth indicators. ...
CONTEXT: Cotton is the most widely planted fiber crop in the world and plays an important role in the national economy. Climate change alters the environmental elements of crop growth such as sunlight, moisture, and soil through changes in temperature and precipitation, which in turn affects the phenology period, growth potential, and cropping system of cotton, and ultimately affects yields.
OBJECTIVE: Studying the feedback of cotton growth to climate change is of great practical significance for cotton planting planning and yield increase and stabilization. Crop growth is affected by both climate and management measures, but most studies often ignore the impact of management measures, resulting in the inability to accurately assess the impact of climate. And existing research is mostly based on single sites or some provinces, lacking regional research on the main cotton producing areas in China.
METHODS: This study calibrated the AquaCrop model using data from 40 sites from three major cotton growing regions in China collected over different time periods between 1978 and 2018 and simulated the climate change impact on cotton for the subsequent years.
RESULTS AND CONCLUSIONS: The model showed good applicability in simulating the growth process of cotton in the study areas. The above-ground biomass (Bio), potential yield (Py) and water use efficiency (WUE) of cotton under the influence of climate change showed an increasing trend of 98% (39/40), 98%, and 93% of the sites, while the actual evapotranspiration (ETa ) mainly decreased. Elevated CO2
concentration increased cotton yield. The main climatic variables affecting cotton growth were wind speed, solar radiation, sunshine hours, and minimum and maximum temperature. Climate variables explained the changes in ET , Bio, Py, and WUE by 65.1–95.5%, 9.7–74.5%, 14.8–68.3%, and 15.2–90.4%, respectively. Overall, cotton growth showed close relation to changes in Was , Rs , Tmin , and Tmax. The study quantitatively analyzed and identified the main climatic factors affecting cotton growth and found that climate change and elevated CO2 concentrations have a positive impact on cotton production.
SIGNIFICANCE: The simulations conducted in this study using multi-site data provided reliable regional information that can help develop future management strategies to maintain current and meet future demand for cotton in China, and provide recommendations and guidance for environmental regulation and sustainable discovery of the cotton growing process.
... Hence it makes sense to assess the impact of future climate on wheat production particularly in Jordan, which also represents the MENA region with a dry Mediterranean climate. Soils with different water holding capacities may affect wheat yield differently (Luo et al., 2009;Yang et al., 2014) and so may the sites with different precipitation and soil water levels (Kimball et al., 1995;Long et al., 2004;Mitchell et al., 2001;Sommer et al., 2013;van Ittersum et al., 2003). The degree of reduction in transpiration due to elevated CO 2 is another variable which will impact the yield and water requirement of crops (Ludwig and Asseng, 2006;Singh et al., 2014a;Sommer et al., 2013). ...
... This indicates that the increasing temperatures are mainly responsible for the crop maturity to hasten. The literature is replete with reporting of faster rate of crop growth and reduction in crop cycle due to increase in temperature (Anwar et al., 2015;Cooper et al., 2009;Ludwig and Asseng, 2006;Mitchell et al., 1993;Sadras and Monzon, 2006;van Ittersum et al., 2003). For RCP4.5 scenario, there was 6.4% and 5% decline in the days to maturity for the 2010s decade at Maru and Mushaqar respectively, and until the end of the century this trend continues culminating in 16.5% and 12% decline at Maru and Mushaqar respectively, in the last decade of the century (Table 2). ...
... An advance of 6-8 days in wheat maturity per°C temperature increase in the Mediterranean environment of Western Australia, having similar latitude as in our study, though in southern hemisphere has been reported by van Ittersum et al. (2003). Increasing air temperature by 3°C accelerated crop development and in turn shortened the time to maturity by 13 days in wheat (Asseng et al., 2004). ...
Different aspects of climate change, such as increased temperature, changed rainfall and higher atmospheric CO2 concentration, all have different effects on crop yields. Process-based crop models are the most widely used tools for estimating future crop yield responses to climate change. We applied APSIM crop simulation model in a dry Mediterranean climate with Jordan as sentinel site to assess impact of climate change on wheat production at decadal level considering two climate change scenarios of representative concentration pathways (RCP) viz., RCP4.5 and RCP8.5. Impact of climatic variables alone was negative on grain yield but this adverse effect was negated when elevated atmospheric CO2 concentrations were also considered in the simulations. Crop cycle of wheat was reduced by a fortnight for RCP4.5 scenario and by a month for RCP8.5 scenario at the approach of end of the century. On an average, a grain yield increase of 5 to 11% in near future i.e., 2010s–2030s decades, 12 to 16% in mid future i.e., 2040s–2060s decades and 9 to 16% in end of century period can be expected for moderate climate change scenario (RCP4.5) and 6 to 15% in near future, 13 to 19% in mid future and 7 to 20% increase in end of century period for a drastic climate change scenario (RCP8.5) based on different soils. Positive impact of elevated CO2 is more pronounced in soils with lower water holding capacity with moderate increase in temperatures. Elevated CO2 had greater positive effect on transpiration use efficiency (TUE) than negative effect of elevated mean temperatures. The change in TUE was in near perfect direct relationship with elevated CO2 levels (R2 > 0.99) and every 100-ppm atmospheric CO2 increase resulted in TUE increase by 2 kg ha− 1 mm− 1. Thereby, in this environment yield gains are expected in future and farmers can benefit from growing wheat.
... Hence it makes sense to assess the impact of future climate on wheat production particularly in Jordan, which also represents the MENA region with a dry Mediterranean climate. Soils with different water holding capacities may affect wheat yield differently (Luo et al., 2009;Yang et al., 2014) and so may the sites with different precipitation and soil water levels (Kimball et al., 1995;Long et al., 2004;Mitchell et al., 2001;Sommer et al., 2013;van Ittersum et al., 2003). The degree of reduction in transpiration due to elevated CO 2 is another variable which will impact the yield and water requirement of crops (Ludwig and Asseng, 2006;Singh et al., 2014a;Sommer et al., 2013). ...
... This indicates that the increasing temperatures are mainly responsible for the crop maturity to hasten. The literature is replete with reporting of faster rate of crop growth and reduction in crop cycle due to increase in temperature (Anwar et al., 2015;Cooper et al., 2009;Ludwig and Asseng, 2006;Mitchell et al., 1993;Sadras and Monzon, 2006;van Ittersum et al., 2003). For RCP4.5 scenario, there was 6.4% and 5% decline in the days to maturity for the 2010s decade at Maru and Mushaqar respectively, and until the end of the century this trend continues culminating in 16.5% and 12% decline at Maru and Mushaqar respectively, in the last decade of the century (Table 2). ...
... An advance of 6-8 days in wheat maturity per°C temperature increase in the Mediterranean environment of Western Australia, having similar latitude as in our study, though in southern hemisphere has been reported by van Ittersum et al. (2003). Increasing air temperature by 3°C accelerated crop development and in turn shortened the time to maturity by 13 days in wheat (Asseng et al., 2004). ...
Different aspects of climate change, such as increased temperature, changed rainfall and higher atmospheric CO2 concentration, all have different effects on crop yields. Process-based crop models are the most widely used tools for estimating future crop yield responses to climate change. We applied APSIM crop simulation model in a dry Mediterranean climate with Jordan as sentinel site to assess impact of climate change on wheat production at decadal level considering two climate change scenarios of representative concentration pathways (RCP) viz., RCP4.5 and RCP8.5. Impact of climatic variables alone was negative on grain yield but this adverse effect was negated when elevated atmospheric CO2 concentrations were also considered in the simulations. Crop cycle of wheat was reduced by a fortnight for RCP4.5 scenario and by a month for RCP8.5 scenario at the approach of end of the century. On an average, a grain yield increase of 5 to 11% in near future i.e., 2010s-2030s decades, 12 to 16% in mid future i.e., 2040s-2060s decades and 9 to 16% in end of century period can be expected for moderate climate change scenario (RCP4.5) and 6 to 15% in near future, 13 to 19% in mid future and 7 to 20% increase in end of century period for a drastic climate change scenario (RCP8.5) based on different soils. Positive impact of elevated CO2 is more pronounced in soils with lower water holding capacity with moderate increase in temperatures. Elevated CO2 had greater positive effect on transpiration use efficiency (TUE) than negative effect of elevated mean temperatures. The change in TUE was in near perfect direct relationship with elevated CO2 levels (R(2)>0.99) and every 100-ppm atmospheric CO2 increase resulted in TUE increase by 2kgha(-1)mm(-1). Thereby, in this environment yield gains are expected in future and farmers can benefit from growing wheat.
... Groundwater flow models are commonly used to study complex interactions in groundwater systems for evaluating recharge, discharge, aquifer storage processes, and sustainable yield, and predicting effects of management measures, thus enabling water managers and decision makers to make informed decisions for sustainable development and management of groundwater resources (Zhou & Li 2011;Rossman & Zlotnik 2013). Over the years, several groundwater flow models such as the United States Geological Survey (USGS) three-dimensional finite difference model (Trescott 1975), the USGS MODFLOW (McDonald & Harbaugh 1988), FEMWATER (Lin et al. 1997), HST3D (Kipp 1997), SEEP2D (Jones 1999), SUTRA (Voss & Provost 2002), and FEFLOW (Diersch 2014) have been developed. These simulation models use different numerical discretization schemes, i.e., finite difference (FD), finite element (FE), and finite volume (FV) for solution of the groundwater flow equation. ...
... The lower irrigation associated with lower ET cr under elevated CO 2 in Scenario-I resulted in lower D d . In Mediterranean climatic conditions, van Ittersum et al. (2003) also reported a decrease in deep drainage from a wheat field under increasing temperature and elevated CO 2 concentration. Ficklin et al. (2010) also reported a decrease in deep drainage under elevated CO 2 and temperature, and projected a decline in cumulative groundwater recharge for alfalfa, almonds, and tomato crops. ...
Increasing CO2 concentration, temperature rise, and changes in rainfall due to climate change are expected to influence groundwater resources in irrigated agricultural regions. A simulation study using AquaCrop and MODFLOW models was undertaken to assess the combined effects of increasing CO2 concentrations, temperature, and rainfall changes on groundwater behavior in a rice–wheat cropping region of northwest India. Simulations were carried out for the 2016–2099 period under two scenarios: increasing CO2 concentrations corresponding to different RCPs (Scenario-I) and at a constant CO2 concentration of 369.4 ppm (Scenario-II). The results indicate that elevated CO2 negates the effect of rising temperature on evapotranspiration (ET) and water demand, and thus, lower ET is simulated under Scenario-I than Scenario-II for different RCPs during the future periods. The lower projected ET resulted in lower rice (2.3%–6.3%) and wheat (1.4%–16.1%) irrigation demand under Scenario-I than under Scenario-II. Of all RCPs, the lowest groundwater level (GWL) decline of 9.2, 20.5, and 24.4 m from the reference GWL (18.85 m) at the end of the early, mid-, and end-century periods, respectively, is projected under RCP8.5 and Scenario-I. Simulation results indicate that CO2 concentration plays an important role while assessing climate change effects on groundwater in irrigated agricultural systems.
HIGHLIGHTS
Quantified crop water budgeting components and their effect on groundwater levels in response to elevated CO2 concentration.;
Rice irrigation requirement (IR) would decrease slightly while wheat IR significantly increased in future periods both in elevated CO2 and constant CO2 concentration scenarios.;
Groundwater level is projected to decline less in elevated CO2 than in constant CO2 concentration scenario.;
... The optimum temperature for the ripening of rice is 21-22 0 C. At temperature below 21 0 C translocation was usually decelerated, while at temperature above 22 0 C the respiration rate was accelerated and the grain-filling period shortened. Van and Howden (2003) mentioned that temperature effect on yield varied between the two soils. An increase in annual temperature of up to 3 0 C had a positive effect on the clay soil (up to 15%), and slightly negative effect on the sandy soil. ...
... During June-October most of the areas of Bangladesh experiences deficit rainfall condition except coastal area, south-eastern parts and Sylhet area experience excess rainfall condition. Van and Howden (2003) reported that a 20% increase in precipitation in summer/autumn and a 35% decrease in winter/spring resulted in an 18% (Moora), 16% (Wongan Hills) and 14% (Merredin) decrease in precipitation oil a yearly basis due to differences i n precipitation distribution within the year. As a result, yield decreased less than in the scenario with a 25% decrease in precipitation across the whole year. ...
Climate change has an effect on farm ecosystem, leading to loss of crop production
and food shortage of the country. The main purpose of the study was to determine of
the small farmer awareness regarding effect of climate change on farm ecosystem.
Data were collected from two villages (Uzankashier Char & Vatipara under
Bhanganamari Union) of Gauripur Upazila under Mymensingh district during 10
April to 12 May 2013. The sample size of the study was 80 small farmers and it was
drawn from a population of 560 using simple random sampling technique. The study
followed a mixed-method of research design. Both structured and semi-structured
questionnaires were prepared for collecting quantitative and qualitative data. The
major climatic variables of the study were very much extreme in nature and having
imbalanced changing trends. An overall awareness score was calculated to measure
the awareness of the small farmers regarding effect of climate change on farm
ecosystem. The awareness of small farmers on effect of climate change on farm
ecosystem was the dependent variable and was measured by using a 4-point rating
scale. Three components of farm ecosystem such as biotic, abiotic and micro climate
of the farm were considered in measuring the variable. Appropriate score such as 3
for highly aware, 2 for moderately aware, 1 for slightly aware and 0 for not aware
were assigned and finally added together to compute extent of awareness of small
farmers. A vast majority (88.8 percent) of the farmers were moderately aware
regarding effects of climate change on entire farm ecosystem, while 11.2 percent were
slightly aware and none of the farmers were found with highly aware about effect of
climate change of farm ecosystem. The step-wise multiple regression analyses
showed that access to information source, social mobility, knowledge on climate
change issues, formal education and access to modern ICT devices of the farmers had
positive influence on building awareness regarding effect of climate change on farm
ecosystem. Different types of adaptive measures were practiced by the farmers of the
study area. Generally eco-friendly pest management strategy was highly practiced in
the study area.
... Notably, late sowing practices greatly enhance oats forage production in both the near and far future under both RCPs. Firstly, late sowing effectively mitigates drought stress as well as the freeze injury caused by extreme low temperatures during the early growth phase (Ittersum et al., 2003;Zhao et al., 2005;Li et al., 2019). Furthermore, late sowing aligns more favorably with the increased rainfall and temperature patterns, particularly from June to October, thereby capitalizing on the enhanced benefits of these climatic factors, consistent with prior research (Tang et al., 2018a;Tang et al., 2018b;Zhang et al., 2019;Li et al., 2022). ...
Adjusting the sowing date represents an effective strategy to mitigate the adverse effects of climate change on crop production across the world. However, its effect on oats (Avena sativa) forage production on the Tibetan Plateau is still unclear. This study aimed to determine the optimal sowing window for forage oats in the semi-arid area of the Tibetan Plateau by assessing forage yield and water productivity (WP) across historical and future climate scenarios. APSIM-oats was calibrated and validated using two-year field data on oats phenology, biomass yield, and soil moisture. Five oats sowing windows were designed in the scenario analysis, namely, early sowing 20 days (April 23), early sowing 10 days (May 3), conventional sowing (May 13), late sowing 10 days (May 23), and late sowing 20 days (June 2). Under historical climate conditions, conventional sowing (as practiced by farmers) resulted in the highest biomass yield and WP. However, future climate trends led to an average decrease of 16.0 % and 13.5 % in oats forage yield and WP, respectively, averaged over RCP4.5 and RCP8.5. Nevertheless, late sowing mitigated the adverse effects of the climate risk. In comparison to conventional sowing, delaying sowing by 20 days increased oats biomass yield, ET, and WP in both near and far future under both RCPs. Rainfall during the growing season of oats demonstrated a significant positive correlation with biomass yield under both RCPs, emphasizing the importance of synchronizing the sowing date with the rainfall pattern to minimize oats yield penalty. Therefore, late sowing of oats in the Tibetan Plateau region proved to be an effective approach to counteract the negative impacts of climate change and minimize oats yield losses. This study provides valuable insights for forage oats production management in the semi-arid areas of the Tibetan Plateau and offers strategies to cope with future climate risks.
... Temperature stress, heat stress, drought stress, water stress, and CO2 impact are calculated by Eqs. (7) -(12), referred to [15], Asseng, Foster [17], Bindi, Fibbi [18], Ewert, Rodriguez [19], Priestley and Taylor [20], Ritchie, Godwin [21], Van Ittersum, Howden [22], and Woli, Jones [23], as cited in Zhao, Liu [24] ( ...
In this study, the SIMPLECrop model was applied to simulate maize biomass and yield in 2 crop seasons, Autumn - Winter 2020 (AW) and Winter - Spring 2020 - 2021 (WS), in Cho Moi district, An Giang province, Vietnam (10°23'47''N, 105°27'41''E). The research aimed to analyze the effects of climate variabilities, particularly increased temperature, on maize growth and yield. The growth period for the Winter - Spring 2020 - 2021 season was 67 days, which was 1 day longer than the AW season (Autumn - Winter 2020). Four cultivar parameters, namely Tsum, HI, I50A, and I50B, were employed for the calibration process to fine-tune the model. Sensitivity analysis using Morris and FAST methods revealed that RUE and Tbase had the highest sensitivity and significant impact on the SIMPLECrop model. These 2 parameters showed strong interactions and played a crucial role in influencing model outcomes. The evaluation of the model’s performance resulted in RRMSE values ranging from 4.8 to 6.3 % and NSE values between 0.86 and 0.93, indicating good agreement between model predictions and observed data. Regarding the impact of temperature increase, a 5 °C temperature rise led to a reduction in stover biomass ranging from 5.2 % (Autumn - Winter 2020) to 19.3 % (Winter - Spring 2020 - 2021) and a decrease in yield by 11.3 % (Autumn - Winter 2020) and 27.0 % (Winter - Spring 2020 - 2021). Simulating an increase in CO2 concentration alone, varying from 50, 100, 150, 200 to 250 ppm, resulted in increased biomass and yield for maize. The most substantial increases were observed at 250 ppm CO2, with approximately 2.5 % higher biomass and 7.7 to 9.1 % greater yield. However, under more severe heat stress (5 °C increase), the positive effects of elevated CO2 were mitigated, resulting in a reduced increase in biomass and yield, approximately 3 - 5 %. These findings highlight the importance of considering temperature and CO2 interactions when assessing crop responses to climate variability. HIGHLIGHTS The SIMPLECrop model was rigorously validated for maize in An Giang Province, Vietnam. Validation metrics, including RRMSE and NSE, demonstrated good-fit model performance, with RRMSE values ranging from 4.8 to 6.3 % and NSE values between 0.86 and 0.93 for open field conditions. Sensitivity analysis using Morris and FAST methods identified that two parameters, RUE (Radiation Use Efficiency) and Tbase (Base Temperature), consistently exhibited the highest sensitivity within the SIMPLECrop model. These parameters played a pivotal role in influencing model outcomes and understanding maize responses to climate variability. The study examined the combined impact of increased temperature and elevated CO2 on maize. A 5 °C temperature rise led to significant stover biomass reductions, varying from 5.2 % (AW season) to a substantial 19.3 % (WS season), along with yield declines of 11.3 % (AW) and a significant 27.0 % (WS). Elevated CO2, ranging from 50 to 250 ppm, had a positive influence on maize biomass and yield. However, under more intense heat stress (5 °C increase), the favorable CO2 effects diminished. This underscores the necessity of considering temperature and CO2 interactions when analyzing crop responses to climate variability. GRAPHICAL ABSTRACT
... However, a study conducted by Meng et al. (2017) on canola and spring wheat in Canada revealed that average crop yields are positively correlated with growing season degree days and pre-growing season precipitation, while they are negatively affected by extremely high growing season temperatures. Similarly, research by Van Ittersum et al. (2003) on wheat in Australia, conducted under rainfed and water-and nitrogen-limited conditions, found that seasonal temperature increases of up to 2 °C increased yields by avoiding end-of-season water and heat stress. ...
Understanding the impact of drought on agriculture is critical for reducing drought-related yield losses in the Koshi River Basin, Nepal. This study used the Standardized Potential Evapotranspiration Index (SPEI) to analyze drought during the crop growing season. The Lagrange interpolation method was employed to identify expected crop yields and to quantify crop yield losses in the Koshi River Basin, Nepal. Finally, the correlation method was applied to observe the relationship between yield loss and drought in the basin. Spatially, the growing seasons of maize, rice, and wheat in the hill region exhibited a more pronounced increase in the drought trend compared to the mountain and Terai regions. Similarly, the maize growing season demonstrated a more significant increasing drought trend than rice and wheat across all regions. However, the wheat growing season in the mountain exhibited an increasing drought trend. The correlations between the detrended SPEI and crop yield loss revealed that maize yields in the Terai and mountain regions are sensitive to drought in both the early stages (from sowing to tasseling) and later stages (milking, seed ripening) in the hill. Similarly, rice yields are sensitive to drought from the plant elongation to ripening in both the hill and Terai regions. Wheat yield analysis showed a weak positive relationship in all regions. The findings of our study can enhance understanding of drought conditions and their impact on crop growth stages, thereby assisting local farmers and agricultural institutions in managing crop yield loss.
... where TT i is the cumulative mean temperature of ith day, ∆TT is the daily added mean temperature, T is the daily average temperature (TMAX + TMIN)/2, and T base is the base temperature for crop growth and phenological development [7,18]. Temperature stress, heat stress, drought stress, water stress, and CO 2 impact are calculated regarding the suggestions of [7,[20][21][22][23][24][25][26]. ...
Soybean Glicine max. (L.) Merr. is one of the most major food crops. In some areas, its responses to different climates have not been well studied, particularly in tropical countries where other crops are more dominant. Accordingly, we adopted the SIMPLE crop model to investigate the responses of soybeans to the climate. We conducted two experiments on crop growth in the Summer–Autumn season of 2020, and Winter–Spring 2021 in the Hoa Binh Commune, in the Mekong Delta, Vietnam, which is an area that is vulnerable to climate change impacts, to obtain data for our model input and assessment. The assessment was concerned with the effects of climate variables (temperature and CO2) on soybean biomass and yield. The results indicated that the SIMPLE model performed well in simulating soybean yields, with an RRMSE of 9–10% overall. The drought stress results showed a negative impact on the growth and development of soybeans, although drought stress due to less rainfall seemed more serious in Spring–Winter 2021 than in Summer–Autumn 2020. This study figured out the trend that higher temperatures can shorten biomass development and lead to yield reduction. In addition, soybeans grown under high CO2 concentrations of 600 ppm gave a higher biomass and a greater yield than in the case with 350 ppm. In conclusion, climate variance can affect the soybean yield, which can be well investigated using the SIMPLE model.
... Temperature stress, heat stress, drought stress, water stress, and CO2 impact are calculated by Eqs. (7) - (12), referred to as cited in Zhao et al. [14]; Asseng et al. [18]; Bindi et al. [19], Ewert et al. [20]; Priestley and Taylor [21]; Ritchie et al. [22]; Ittersum et al. [23]; and Woli et al. [24]. ...
Rice is the essential food crop of An Giang Province. Vietnam and the whole world are facing several problems hindering climate change, such as increased temperature and CO2 concentration that many manufacturers’ companies and managers need to estimate output to make production plans or adjust policies. In this study, the model known as SIMPLE was applied to simulate the biomass and yield of rice in 2 crop seasons Autumn - Winter 2020 (AW) and Winter - Spring 2020 - 2021 (WS), in Cho Moi district, An Giang province, Vietnam (10° 23' 47"N, 105° 27' 41"E) and analyzed the effects of climate variabilities and scenarios on simulation results. Heat stress showed a relatively negative impact on the growth and development of rice in AW more seriously than WS due to climate variabilities. Climate change scenario RCP8.5 (RCP - Representative Concentration Pathway) has predicted that atmosphere temperature may increase above 4 °C and CO2 concentration to reach 900 ppm by the end of the 21st century. As a result, from the model, for every 100 ppm CO2 concentration increase, the cumulative rice biomass increased by 8 and 10 % in AW and WS, respectively. Moreover, conditions assumed from the model that increased 5 °C caused a decrease in cumulative biomass up to 7.2 % in AW season compared to 3.1 % in WS season. However, with responses of 5 °C increasing in the model, rice yield decreased relatively rapidly from 8.5 % in AW and 7 % WS. HIGHLIGHTS The model known as SIMPLE has been used in this study RMSE of our model differs from the observed yield from 4.2 % (Winter-Spring crop-WS) to 5.5 % (Autumn-Winter crop-AW) For every 100-ppm CO2 concentration increased, the cumulative rice biomass increased by 8 and 10 % in AW and WS, respectively Increasing 5 °C, rice yield decreased 8.5 % in AW and 7 % WS Sensitivity analysis showed that RUE (Radiation Use Efficiency) has the most influencing factor on rice yield GRAPHICAL ABSTRACT
... It should be noted that these effects are without taking into account factors such as diseases and pests. Van Ittersum et al. (2003) investigated the effect of changes in atmospheric CO 2 concentration, temperature and rainfall on wheat yield and deep penetration values in a region of Australia. One of the results of this study was that wheat yield increases linearly by 10-16% with every 100 units (about 28%) increase in CO 2 concentration up to 700 ppm. ...
In this study, the effect of climate change on planting date and growth duration of rainfed wheat in the west and northwest parts of Iran has been investigated. The occurrence of climate change in the region was first evaluated for the base period (1992–2018) using two nonparametric tests of Mann–Kendall and Sen's slope estimator. Then, the climatic parameters of maximum temperature, minimum temperature and precipitation were simulated under RCP4.5 scenario for the period 2019–2039 based on downscaled output data of the Community Climate System Model (CCSM4) using LARS WG software. The growth period was obtained using a linear multiple regression model, which was selected based on R-square and accounted for 87% of its total variation. The results predicted that the average annual temperature will increase by 2 °C, while the average annual precipitation will increase by 30% by the end of 2039. Planting dates were determined based on two indices combining temperature and precipitation for the base and future periods. The results showed that climate change effects at the 2039 horizon will shorten by 18 days the wheat growth period and the appropriate planting time for rainfed wheat will be reduced by 2–19 days.
... The decision support systems for agro-technology transfer (DSSAT) and Agricultural Production System Simulator (APSIM) allow stakeholders to combine technical knowledge, phenology, crop growth, development and production function with economic and environmental impacts to facilitate economic analysis and risk assessment of farming enterprises [18,19]. Crop model development and validation at the experiment level is most important for improving the understanding of fundamental processes of crop production, supporting strategic agricultural management decisions and ensuring crop productivity [20][21][22][23]. The DASSAT and APSIM have the capability to simulate wheat crop growth in response to nitrogen and genotypes more efficiently than various field experiments [24,25]. ...
Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.
... The increased level of atmosphere GHG emission has already affected the biophysical process of agroecosystems (Bindi and Howden 2004). For example, most cereals and pulses need specific day-night temperature for their growth and development, while the fluctuation of temperature as a result of global warming is shortening the length of life span which ultimately reduces the crops' yield (Tubiello et al. 2000;vanIttersum et al. 2003). In the case of root and tuber crops, the increasing CO 2 may increase the ground sinks as a result of available carbon and apoplastic loading of phloem (Bindi and Howden 2004;Sheoran et al. 2021), whereas due to global warming, the increasing temperature may shorten the growing season, which leads to a decline in the yield (Wolf 2002). ...
Both rice and wheat in the ‘rice-wheat systems’ (RWCs) of South-Asia and China, feed more than 3.1 billion people. It is the most productive and vital agricultural systems worldwide to meet the food safety of the growing population. Although, the RWCs have great concern for food security, however, one of the foremost complications in the systems is soils are puddled with repeated tillage by the traditional way for transplanting rice seedlings which lead to decline soil physical and chemical properties. Besides these, repeated tillages for puddling create a hard plough-pan layer at the root-zone of the rice plant that creates poor infiltration and water-logging for the next dry season crop particularly wheat. Farmers in the systems generally use excessive synthetic fertilizers and pesticides for getting higher yield for both rice and wheat. As a result, repeated tillage and also imbalance application of inorganic fertilizers and pesticides increase the production cost as well as influence greenhouse gas emission (GHGs). Since the systems have several hostile effects on the environment due to traditional farming, while it is already confirmed that the systems are the key source of food production more than 3.1 billion people in the countries of South and South-East. Therefore, it will not be a wise decision to replace the system from the regions. In the meantime, researchers have recommended numerous advanced technologies in the RWCS for sustainable rice and wheat production. The chapter discusses cost-effective and ecologically friendly technologies for RWCs of South-Asia for food and environmental security.
... They showed that a rise in temperature and potential evapotranspiration (PET) would decrease crop growing season and CWD. The negative effect of rising temperatures outweighs the positive effect of rising CO 2 levels on crop production (Van Ittersum et al. 2003). Brouziyne et al. (2018) used a hydrological model and downscaled GCMs to quantify climate change impact on CWD of winter wheat and sunflower in northwestern Morocco. ...
Climate change has caused a shift in aridity, particularly in the world’s dry regions, affecting several sectors, predominantly the agricultural and water resources. This research examined the climate change effects on crop water demand (CWD) in Syria during 1951–2010. Given the lack of observed data, this analysis relied on Global Precipitation Climatology Center (GPCC) precipitation and Climatic Research Unit (CRU) temperature. Potential evapotranspiration (PET) at each grid was estimated using the Penman–Monteith model and the CWD using the FAO-56 method. The analysis revealed that CWD in Syria increased during 1981 − 2010 compared to that during 1951 − 1980. The increase in CWD was found for grapes, tobacco, barley, and cotton, whereas the maximum changes were during April and May. The most remarkable changes in CWD were for barley, between − 20 and 40 mm. It showed a decreased CWD in the south and a rise in the north (0 − 40 mm). The CWD for wheat showed a decline in most parts of the country, except in the north. The increase in CWD for barley and wheat caused an increase in agricultural water stress in the region. Agriculture planning needs to be developed according to the expected future climate changes to maintain the agricultural production in the region.
... Although a complete assessment of climate change risk should include both impact and adaptation, there are only limited adaptation evaluation studies [10,11]. Some of the earlier studies of climate change's impact on crop yield were done by setting a hypothetical decrease in rainfall and increase in temperatures in the future climate [9,12,13]. However, due to complex interactions among weather variables, physico-bio-chemical responses to changing atmospheric composition, and radiative forcing of the climate system, the temporal pattern and variability of the current climate will not be the same as the one in the future climate [14,15]. ...
Rising air temperature and change in rainfall patterns are expected to have impact on agricultural production. The impact of climate change on wheat production was investigated and agronomic adaptation strategies were evaluated for two emission scenarios of Representative Concentration Pathway (RCP4.5 and RCP8.5) and three projection periods (2030, 2050 and 2070) using a climate model ensemble in the bio-physical model Agricultural Process SIMulator (APSIM). Early and late maturing wheat varieties were tested under six sowing time scenarios. Under RCP4.5, growing season rainfall would decrease by 9%, 15% and 19% in 2030, 2050 and 2070, respectively, and temperature would increase by 0.7 °C, 1.2 °C and 1.4 °C, respectively. For RCP4.5, the wheat yield would decrease by 9%, 15% and 19% in 2030, 2050 and 2070, respectively. Under RCP8.5, the yield would decrease by 9%, 18% and 27%, respectively. Short-season cultivars would be suitable for the low-rainfall environments and long-season cultivars for the high-rainfall environments. In 2050, for RCP4.5 at a low-rainfall site, the yield of early maturing variety would decrease by 11% and 31%, while at a high-rainfall site, these values would show a 9% decrease and 1% increase, respectively. At the low rainfall site, yield reduction for early sown variety would be 14% and 23% when late sown, while late maturing wheat would have a much higher yield reduction. At the higher rainfall site, yield reduction for early and late sown early maturing variety would be 3% and 15%, while for late-maturing wheat these values would be only 1% and 2%. Generally, the future climate is expected to have significant impact on wheat yield and changes in agronomic practices can mitigate the impacts on yield.
... The three DSSAT wheat models are embedded into the DSSAT platform using the same inputs for climate and the same sub-models for soil water and soil N. The models differ in their approach in simulating crop development and growth and particularly heat stress. The selected wheat models have been widely used to study diverse cropping systems around the world (Jones et al 2003, Van Ittersum et al 2003, Asseng and Turner 2007, White et al 2011, Lazzaretti et al 2015, Kassie et al 2016, Ruane et al 2016, van Bussel et al 2016. None of the three models showed a bias towards the extremes in larger multi-model comparison (e.g. ...
Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.
... Besides to the elevated CO 2 , higher temperatures increase the rates of transpiration and crop water demand of wheat crops (Eduardo et al., 2013). Higher temperatures also affect grain yield of wheat crop by accelerating phenology and reducing biomass production (Van Ittersum et al., 2003) and negatively affects through heat stress (Fulco, 2006). Highest temperatures increase the rates of grain filling, but reduce the duration of grain filling and ultimately grain yield of wheat crop especially reducing grain quality by affecting dough making qualities (Asseng et al., 2011;Steven et al., 2008). ...
Climate change is a recent challenge on crop production and productivity in the world. The objective of this paper is to review the major effects of climate change on the production and productivity of wheat in the high lands of Ethiopia. Effects of climate change on wheat would be mainly through changes in [CO 2 ], temperature, rainfall, length of growing period, actual growth rate and increased evapo-transpiration, which may lead to reduce yield or complete crop failure. Moreover, flower fertilization and grain set are highly sensitive to heat stress during mid-anthesis. In C 3 crops like wheat, the elevated CO 2 level is expected to increase productivity as a result of higher CO 2 diffusion through stomata leading to a higher photosynthesis rate. But, elevated [CO 2 ] may have negative effects on the grain-quality of wheat in terms of protein, lipids, number of mitochondria and nitrogen contents. Unlike CO 2 , elevated temperature affects crop production negatively by increasing rate of respiration; hastening plant growth and development; increasing photorespiration of wheat, reducing photosynthetic efficiency due to O 2 interrupts the photosynthetic path way instead of CO 2 , increasing rate of water loss by increasing evapo-transpiration and decreasing nutrient use-efficiency through increased rate of decomposition and mineralization. As a result, wheat area is forecast to be displaced by other crop types. In order to tackle this issue, major mitigation and adaptation measures for example promoting area closures and conservation agriculture-based (CA), agroforestry practices, efficient use of energy sources, etc. should be practiced and given special attention by the communities as well as the government to solve the effects of climate change on wheat production and productivity in the country.
... It would also negatively influence soil biota (Falloon and Betts, 2010). Although reduced groundwater recharge leads to an increase in salinization, due to inflow of seawater (Montanarella, 2007), increase in rainfall due to climate change might lead to higher salinization due to higher water loss from the root zones of the plants (Van Ittersum et al., 2003). ...
The process of accumulation of salt in soil (salinization) is a global problem. This chapter starts by introducing the concept of soil salinization, its meaning, causes, and sources. Although there could be various causes for it at the local level, on a global scale two of the most prominent causes of soil salinization are climate change and lithology of the drainage basin. Effects of climate change are understood in terms of changes in temperature that leads to the creation of saline soils and precipitation that might cause the aggravation of the problem of salinity in an already saline area. It is important to note the anthropogenic influence of climate change on the salinity of the soil. After the influence of climate change on salinity is elucidated, the chapter explores the influence of salinity on the crop yield, the ways to alleviate the problem of salinization, and the management of saline soils.
... Long duration cultivar with early sowing will be helpful to compensate the temperature-related changes like shorter crop duration (Debaeke et al., 2017). Early flowering varieties would be suitable to resist heat shock by allowing grain filling in cooler and wetter period (Van Ittersum et al., 2003;Debaeke, 2004). ...
... To isolate the impact of changes in rainfall and temperature from those of CO 2 concentration, two sets of simulations were run. In the first set, CO 2 was set at a constant value of 350 ppm (assumed to be the CO 2 concentration when the model was developed; Van Ittersum et al. 2003) for all 117 years of simulation to examine the effects of rainfall and temperature, independent of changes in CO 2 concentration, on wheat yield. In the second set, CO 2 Table 1 Characteristics of the soils used for the simulations of water-limited wheat yield with their Western Australian (WA) Soil Group, Australian Soil Classification (Isbell 1996), plant available water capacity (PAWC, mm) and the proportion of these soils in the Western Australian wheat belt concentration was altered annually from 296 in 1900 to 400 ppm in 2016 using the spline smoothed ice core data and Mauna Loa observations data of MacFarling Meure et al. (2006) (available at http://scrippsco2.ucsd.edu/data/atmospheric_co2/icecore_merged_products). ...
Climate change has likely impacted crop yield potential in major rain-fed crop-growing regions. However, the impact on the spatial pattern across regions is unclear. Here, the wheat belt of Western Australia was used as a case study to investigate the effect of historical climate change on the spatial patterns of water-limited crop yield. We used 117 years (1900–2016) of observed daily climate data on ~ 5 km × ~ 5 km grids to map and quantify the spatial-temporal changes in water-limited wheat yield simulated by the APSIM model. The climate data were split into four periods based on distinct changes in rainfall (Period 1, 1900–1934; Period 2, 1935–1974; Period 3, 1975–1999; and Period 4, 2000–2016). The results showed that the observed decreases in rainfall shifted the regional wheat yield potential towards the southwest of the wheat belt by an average of 70 km between the first and last periods. Observed increases in CO2 counteracted this by about half of this movement. Actual wheat yields achieved by farmers have not decreased, thanks to improvements in crop genetics and management, but the simulated decrease in water-limited yields has meant that actual yields in this region are not as high as they might have been. Future climate change is likely to continue to impact on water-limited crop yield and its spatial pattern in Western Australia. Cropping systems will need to continually evolve to cope with a changing climate, and every aspect of agronomy and genetics needs to be considered. Without continuing improvements, there will likely be a decrease in wheat yield across this cropping region.
... Through the linking of crop growth with soil processes, APSIM is particularly suited for the evaluation of likely impacts of management practices on the soil resource and crop productivity. The model has been used successfully in the search for strategies for more efficient production, improved risk management, crop adaptation and sustainable production (Keating et al. 2003;Van Ittersum, Howden, and Asseng 2003;Kisaka et al. 2015). APSIM-sorghum are widely calibrated against independent crop dataset for resource-constrained and risky environmental condition of semi-arid smallholder farming systems (Whitbread et al. 2010;Akinseye et al. 2017). ...
The Agricultural Production Systems simulator (APSIM) model was calibrated and evaluated using two improved sorghum varieties conducted in an experiment designed in a randomized complete block, 2014–2016 at two research stations in Nigeria. The results show that the model replicated the observed yield accounting for yield differences and variations in phenological development between the two sorghum cultivars. For early-maturing cultivar (ICSV-400), the model indicated by low accuracy with root means square error (RMSE) for biomass and grain yields of 20.3% and 23.7%. Meanwhile, Improved-Deko (medium-maturing) cultivar shows the model was calibrated with low RMSE (11.1% for biomass and 13.9% for grain). Also, the model captured yield response to varying Nitrogen (N) fertilizer applications in the three agroecological zones simulated. The N-fertilizer increased simulated grain yield by 26–52% for ICSV-400 and 19–50% for Improved-Deko compared to unfertilized treatment in Sudano-Sahelian zone. The insignificant yield differences between N-fertilizer rates of 60 and 100 kgha⁻¹ suggests 60 kgNha⁻¹ as the optimal rate for Sudano-Sahelian zone. Similarly, grain yield increased by 23–57% for ICSV-400 and 19–59% for Improved Deko compared to unfertilized N-treatment while the optimal mean grain yield was simulated at 80 kgNha⁻¹ in the Sudan savanna zone. In the northern Guinea savanna, mean simulated grain yield increased by 8–20% for ICSV-400 and 12–23% for Improved-Deko when N-fertilizer was applied compared to unfertilized treatment. Optimum grain yield was obtained at 40 kgha⁻¹. Our study suggests a review of blanket recommended fertilizer rates across semi-arid environments for sorghum to maximize productivity and eliminate fertilizer losses, means of adaptation strategies to climate variability.
... The scenarios are created by adjusting all observations in the base-case dataset by the amounts shown in Table 1. This approach to the development of climate scenarios -also used by van Ittersum et al. (2003); Ludwig and Asseng (2006); Bryan et al. (2010);Bryan et al. (2011); Paudel and Hatch (2012); Thamo et al. (2017a) -changes the minimum and maximum temperatures, and the intensity but not frequency of precipitation. ...
Agricultural research on climate change generally follows two themes: (i) impact and adaptation or (ii) mitigation and emissions. Despite both being simultaneously relevant to future agricultural systems, the two are usually studied separately. By contrast, this study jointly compares the potential impacts of climate change and the effects of mitigation policy on farming systems in the central region of Western Australia’s grainbelt, using the results of several biophysical models integrated into a whole‐farm bioeconomic model. In particular, we focus on the potential for interactions between climate impacts and mitigation activities. Results suggest that, in the study area, farm profitability is much more sensitive to changes in climate than to a mitigation policy involving a carbon price on agricultural emissions. Climate change reduces the profitability of agricultural production and, as a result, reduces the opportunity cost of reforesting land for carbon sequestration. Nonetheless, the financial attractiveness of reforestation does not necessarily improve because climate change also reduces tree growth and, therefore, the income from sequestration. Consequently, at least for the study area, climate change has the potential to reduce the amount of abatement obtainable from sequestration – a result potentially relevant to the debate about the desirability of sequestration as a mitigation option.
... In an agricultural system, crop productivity varies with varying climatic and edaphic conditions (Lal et al., 1993, Probert et al., 1995, Asseng et al., 2002, Ittersum et al., 2003. Models have been developed to best understand yield gaps and optimization of yield potential (Singh and Virmani, 1996, Gerke et al., 1999, Hartkamp et al., 2002, Bannayan et al., 2003, Yu et al., 2006, Gijsman et al., 2007, Saseendran et al., 2009, Persson et al., 2010, White et al., 2011a. ...
[ Reference and PDF available at : http://hdl.handle.net/11589/160122 ] Coupling hydrologic and crop models is increasingly becoming an important task when addressing agro-hydrologic systems studies. Either for resources conservation or cropping systems improvement, the complex interactions between hydrologic regime and crop management components requires an integrative approach in order to be fully understood. Nevertheless, the literature offers limited resources on models’ coupling that targets environmental scientists. Indeed, major of guides are are destined primarily for computer specialists and make them hard to encompass and apply. To address this gap, we present an extensive research to crop and hydrologic models coupling that targets earth agro-hydrologic modeling studies in its integrative complexity. The primary focus is to understand the relationship between agricultural intensification and its impacts on hydrologic balance. We provided documentations, classifications, applications and references of the available technologies and trends of development. We applied the results of the investigation by coupling the DREAM hydrologic model with DSSAT crop model. Both models were upgraded either on their code source (DREAM) or operational base (DSSAT) for interoperability and parallelization. The resulting model operates at a grid base and daily step. The model is applied southern Italy to analyze the effect of fertilizer application on runoff generation between 2000 and 2013. The results of the study show a significant impacts of nitrogen application on water yield. Indeed, nearly 71.5 thousand cubic-meter of rain water for every kilogram of nitrogen and per hectare is lost as a reduction of runoff coefficient. Furthermore, a significant correlation between the nitrogen applications amount and runoff is found at a yearly basis with Pearson’s coefficient of 0.93.
... A comparison of the three (CERES, CROPSIM, and NWheat) models is shown in Kassie et al. (2016). The three wheat crop simulation models are embedded within the Decision Support System for Agrotechnology Transfer, DSSAT (Hoogenboom et al., 2004) and have been widely used to study cropping systems under different environments (Asseng et al., 2004;Asseng and Turner, 2007;Asseng et al., 2002Asseng et al., , 2000Lazzaretti et al., 2015;Ruane et al., 2016;van Bussel et al., 2016;Van Ittersum et al., 2003). The selected models were initially derived from the CERES wheat model (Ritchie et al., 1985), with biomass accumulation being simulated using the radiation use efficiency concept (i.e. ...
Mexico's 3.3 million tons current wheat production is projected to decline due to climate change. To counteract these negative impacts, we explored a range of plausible adaptation measures including change in crop management (early sowing and nitrogen fertilizer applications), crop genetic traits (early vigor, late flowering and heat tolerance) and wheat growing area expansion. Adaptation measures were simulated individually and in various combinations with a multi-crop model and multi-Global Climate Model ensemble across representative wheat growing regions and aggregated to national wheat production. Under both baseline (current) and future climate scenarios, most of the suggested individual and combined genetic traits resulted in a positive impact on irrigated wheat but were less beneficial in rainfed systems, with the largest responses observed with late flowering and increased N fertilizer. Increased N fertilizer applications on its own, but particularly combined with crop genetic traits showed the highest yield increase in the baseline, with further positive impacts in the future scenarios. Yield benefits from new crop genetic traits combined with increased N fertilizer applications could add about 672,000 t year ⁻¹ to national wheat production, after losing 200,000 t year ⁻¹ due to climate change by 2050s. Most effectively, expanding wheat to include all areas where wheat was previously grown during the last two decades could add 1.5 million t year ⁻¹ now and 1.2 million t year ⁻¹ in the future. Breeding for new crop genetic traits will reduce some of the negative impacts from future climate change, but improved cultivars need to be implemented with suitable crop management, especially N fertilizer management.
... CO 2 impact on the biomass growth rate. Similar to other studies (Bindi et al., 1996;Ewert et al., 2002;Van Ittersum et al., 2003), RUE increases linearly (linear is used for simplicity, but it is likely to be curvilinear) until the concentration of CO 2 is 700 ppm. When CO 2 concentration is higher than 700 ppm, RUE is kept constant due to likely saturation of RUE to elevated CO 2 . ...
Crop models are important tools for assessing the impact of climate change on crop production. While multiple models have been developed over the past decades for the major food and fiber crops such as wheat, maize, soybean, rice, and cotton, there are few or none for many other crops. The goal of this study was to develop a simple generic crop model (SIMPLE) that could be easily modified for any crop to simulate development, crop growth and yield. The crop model SIMPLE includes 13 parameters to specify a crop type, with four of these for cultivar characteristics. Commonly available inputs that are required for the crop model SIMPLE include daily weather data, crop management, and soil water holding parameters. The initial SIMPLE model was calibrated and evaluated for 14 different annual crops using observations for biomass growth, solar radiation interception, and yield from 25 detailed field experiments for a total of 70 treatments from 17 sites, resulting in a RRMSE of 25.4% for final yield. A sensitivity analysis comparing a C3, C4 and a legume crop showed an expected response to a gradual increase in temperature and atmospheric CO2 concentrations. A regional gridded simulation for US potatoes reproduced the general observed patterns of spatial yield variability. Because the model is simple, it has several limitations, including the lack of response to vernalization and photoperiod effect on phenology. The model includes water, but no nutrient dynamics. However, an advantage of the model simplicity is that it can be easily adapted and evaluated for any new crop, based on literature data and field experiments, or general crop data such as sowing and harvest dates and yield statistics. The model is available in several simulation frameworks including a stand-alone version in R, Excel and as part of DSSAT.
... Temperature is the main variable that regulates the rate of crop development (Nasim et al., 2016). Higher temperatures have a positive effect on grain yield in the cooler and wetter southern part of Western Australia because a faster crop development caused by warmer temperatures moves the grain filling period into a wetter part of the season (van Ittersum et al., 2003). However in most cases, an increase of temperature accelerates crop development, reducing accumulation of assimilates and grain filling , which was also the case in our study. ...
... If soil moisture does increase under eCO 2 , it will have various consequences at the ecosystem level: 1) Groundwater will increase in humid climates (Beaulieu et al., 2010) and in those areas where hydrothermal coefficient is > 0.8. Especially in areas where evapotranspiration outbalances precipitation, the estimated 10% higher soil moisture (Leuzinger and Körner, 2007;van Ittersum et al., 2003) may double groundwater formation. Consequently, 2) leaching cations (mainly Ca 2+ , Mg 2+ ) and anions (NO 3 − , SO 4 2− ) will decrease nutrient availability in the topsoil Siemens et al., 2012). ...
Atmospheric change encompassing a rising carbon dioxide (CO2 ) concentration is one component of Global Change that affects various ecosystem processes and functions. The effects of elevated CO2 (eCO2 ) on belowground processes are incompletely understood due to complex interactions among various ecosystem fluxes and components such as net primary productivity, carbon (C) inputs to soil, and the living and dead soil C and nutrient pools. Here we summarize the literature on the impacts of eCO2 of on 1) cycling of C and nitrogen (N), 2) microbial growth and enzyme activities, 3) turnover of soil organic matter (SOM) and induced priming effects including N mobilization/immobilization processes, and 4) associated nutrient mobilization from organic sources, 5) water budget with consequences for soil moisture, 6) formation and leaching of pedogenic carbonates, as well as 7) mobilization of nutrients and nonessential elements through accelerated weathering. We show that all effects in soil are indirect: they are mediated by plants through increased net primary production and C inputs by roots that foster intensive competition between plants and microorganisms for nutrients. Higher belowground C input from plants under eCO2 is compensated by faster C turnover due to accelerated microbial growth, metabolism and respiration, higher enzymatic activities, and priming of soil C, N and P pools. We compare the effects of eCO2 on pool size and associated fluxes in: soil C stocks vs. belowground C input, microbial biomass vs. CO2 soil efflux vs. various microbial activities and functions, dissolved organic matter content vs. its production, nutrient stocks vs. fluxes etc. Based on these comparisons, we generalize that eCO2 will have little impacts on pool size but will strongly accelerate the fluxes in biologically active and stable pools and consequently will accelerates biogeochemical cycles of C, nutrients and nonessential elements.
... If soil moisture does increase under eCO 2 , it will have various consequences at the ecosystem level: 1) Groundwater will increase in humid climates (Beaulieu et al., 2010) and in those areas where hydrothermal coefficient is > 0.8. Especially in areas where evapotranspiration outbalances precipitation, the estimated 10% higher soil moisture (Leuzinger and Körner, 2007;van Ittersum et al., 2003) may double groundwater formation. Consequently, 2) leaching cations (mainly Ca 2+ , Mg 2+ ) and anions (NO 3 − , SO 4 2− ) will decrease nutrient availability in the topsoil Siemens et al., 2012). ...
... If soil moisture does increase under eCO 2 , it will have various consequences at the ecosystem level: 1) Groundwater will increase in humid climates (Beaulieu et al., 2010) and in those areas where hydrothermal coefficient is > 0.8. Especially in areas where evapotranspiration outbalances precipitation, the estimated 10% higher soil moisture (Leuzinger and Körner, 2007;van Ittersum et al., 2003) may double groundwater formation. Consequently, 2) leaching cations (mainly Ca 2+ , Mg 2+ ) and anions (NO 3 − , SO 4 2− ) will decrease nutrient availability in the topsoil Siemens et al., 2012). ...
... Temperature in NWheat affects phenology, biomass accumulation, CO 2 assimilation, leaf senescence during grain filling, rate of grain filling, and N demand to grain and vapor pressure deficit (Zheng et al., 2015). Both models run on a daily time step using the radiation use efficiency approach for crop biomass accumulation, and have been widely used to study cropping systems under different environments Asseng and Turner, 2007;Asseng et al., 2002Asseng et al., , 2000Lazzaretti et al., 2015;Ruane et al., 2016;van Bussel et al., 2016;Van Ittersum et al., 2003). ...
Wheat is one of the most important cereal crops in Mexico, but the impact of future climate change on production is not known. To quantify the impact of future climate change together with its uncertainty, two wheat crop models were executed in parallel, using two scaling methods, five Global Climate Models (GCMs) and two main Representative Concentration Pathways (RCPs) for the 2050s. Simulated outputs varied among crop models, scaling methods, GCMs, and RCPs; however, they all projected a general decline in wheat yields by the 2050s. Despite the growth-stimulating effect of elevated CO2 concentrations, consistent yield declines were simulated across most of the main wheat growing regions of Mexico due to the projected increase in temperature. Exceptions occurred in some cooler areas, where temperature improved sub-optimal conditions, and in a few areas where rainfall increased, but these increases only provided negligible contributions to national production. Larger and more variable yield declines were projected for rainfed wheat due to current and projected spatial variability of temperature and rainfall patterns. Rainfed wheat, however, only contributes about 6% of Mexico’s wheat production. When aggregating the simulated climate change impacts, considering temperature increase, rainfall change, and elevated atmospheric CO2 concentrations for irrigated and rainfed wheat cropping systems, national wheat production for Mexico is projected to decline between 6.9% for RCP 4.5 and 7.9% for RCP 8.5. Model uncertainty (combined for crop and climate models) in simulated yield changes, and across two scaling methods, was smaller than temporal and spatial variability in both RCPs. Spatial variability tends to be the largest in both future scenarios. To maintain or increase future wheat production in Mexico, adaptation strategies, particularly to increasing temperatures affecting irrigated wheat, or expanding the cropping area, will be necessary.
... It has since witnessed a broad applicability to a wide range of systems management and has been extensively used (e.g. Nelson et al, 1998;Ludwig and Asseng, 2006;van Ittersum et al, 2003;Probert et al, 1998). The capability to simulate crop growth in response to low soil P is one of its more recent capabilities , providing opportunity to simulate crop production in the tropics where soil P nutrition affects crop yield and efficient use of applied mineral fertilizers. ...
An increasing human population and decreasing fallow periods have resulted in a rapid
decline in soil productivity in the semi-arid region of Ghana, which is characterized by
low-input subsistence agriculture. Soils are inherently poor and contain little to support
crop production. Attempts by smallholders to increase production have resulted in the
concentration of nutrients in the homestead fields through the use of animal manure and
crop residues from the distant bush farms. This has contributed to spatial variability in
soil nutrients and soil organic carbon (SOC).
The study area was classified into land-use trajectories based on a rural rapid
appraisal technique with the aid of the farmers in the community and by remote sensing
quick-bird imagery. The influence of land-use trajectories on soil nutrient stocks was
evaluated. Spatial distribution of soils and soil properties and the factors influencing
their distribution were assessed in a landscape of 1.5 km2
selected within the study area.
Data on soil chemical and physical properties collected were analyzed with
geostatistical techniques for their spatial dependency. The Agricultural Production
Systems sIMulator (APSIM), a crop simulation model, was calibrated for sorghum
(Sorghum bicolor (L.) Moench) and evaluated for yield response to inorganic nitrogen
(N) and phosphorous (P) fertilizer treatments in two farm types (homestead fields and
bush farms).
Land-use trajectories are revealed to have influenced the nutrient stock of the
soils in the study area. Furthermore, the impact of farmers’ management activities on
nutrient stocks was significant. Though a non-parametric test revealed distinct soil
types, considerable variability could be observed within individual soils based on their
chemical and physical properties. The distribution of soil parameters in the selected
landscape was influenced by the soils, farmers’ management practices and topography.
APSIM predicted the grain yield response of sorghum to both N and P application with
an overall modified internal coefficient of efficiency of 0.64. A gradual decline in grain
yield was observed over the 29-year simulation period in both the homestead fields and
the bush farms, with yields being much lower in the latter. If crop residues were
returned to the fields, half the mineral N fertilizer was needed in the homestead fields to
produce the average grain yields produced on the bush farm with full fertilization.
Temporal variability in grain yield was consistently higher with the removal of crop
residues, irrespective of farm type. APSIM is responsive to both organic and inorganic
fertilizer applications in the study area and also highlights the essential role of crop
residues and inorganic fertilizer in influencing the temporal variability in sorghum grain
production and hence the impact of farmers’ management practices on food security.
This is evident in the rapid decline in soil organic carbon accompanied by a decline in
grain yield after 29 years of cropping. The use of inorganic fertilizer and incorporation
of crop residues (organic matter) are critical for attaining food security in the study area.
... In the conditions where escaping and avoiding strategies are not applicable, where water resources are scarce and the likelihood of high temperatures increases during the most susceptible phenological phases, varieties and species with increased resistance to heat shocks and drought could be preferred when available. For instance, earlier flowering varieties could be adopted to allow grain filling to occur in the cooler and wetter parts of the year (van Ittersum et al., 2003;Debaeke, 2004). The substitution of irrigated maize by moderately irrigated or rainfed crops (e.g. ...
Climate-smart cropping systems should be designed with three objectives: reducing greenhouse gas (GHG) emissions, adapting to changing and fluctuating climate and environment, and securing food production sustainably. Agriculture can improve the net GHG emissions balance via three levers: less N2O, CH4 and CO2 emissions, more carbon storage, and green energy production (agrifuels, biogas). Reducing the application of mineral N fertilizer is the main option for reducing N2O emissions either directly or by increasing the proportion of legumes in the rotation. The most promising options for mitigating CH4 emissions in paddy fields are based on mid-season drainage or intermittent irrigation. The second option is storing more carbon in soil and biomass by promoting no-tillage (less fuel, crop residues), sowing cover crops, introducing or maintaining grasslands and promoting agroforestry. Breeding for varieties better adapted to thermal shocks and drought is mainly suggested as long-term adaptation to climate change. Short-term strategies have been identified from current practices to take advantage of more favorable growing conditions or to offset negative impacts: shifting sowing dates, changing species, cultivars and crop rotations, modifying soil management and fertilization, introducing or expanding irrigation. Some crops could also move to more suitable locations. Model-based tools and site-specific technologies should be developed to optimize, support and secure farmer's decisions in a context of uncertainty and hazards. Most of the adaptation and mitigation options are going in the same way but tradeoffs will have to be addressed (e.g. increasing the part of legumes will be possible only with significant breeding efforts). This will be a challenge for designing cropping systems in a multifunctional perspective.
... Thus, in the next decades the effect of climate change on agriculture in the Mediterranean area will likely lead to increasing plant water stress, decreasing crop yields, and increasing yield variability (Kapetanaki and Rosenzweig, 1997;Maracchi et al., 2005;Giannakopoulos et al., 2009;IPCC, 2014). In particular, the negative impact of climate change on cereal yields will be due to: (i) heat stress; (ii) increased plant water demand with higher transpiration rate; (iii) shortened growing period and anticipated maturity (Porter and Gawith, 1999;Rötter and van de Geijn, 1999;Tubiello et al., 2000;van Ittersum et al., 2003;Parry et al., 2005;Moriondo et al., 2011a;Giannakopoulos et al., 2009). ...
The CERES-Wheat crop model was used to simulate grain yields, kernel weights and anthesis dates for three Italian durum wheat varieties (Creso, Duilio and Simeto) under climate change projections at two typical Mediterranean environments (Ussana and Benatzu sites) located in Southern Sardinia (Italy). The model was calibrated and validated in a previous modelling study using long-term weather data from the same experimental sites and agronomic data-sets of the same sites and varieties over the period 1973–2004. To assess the responses of durum wheat varieties to climate changes, 48 synthetic climates based on the combination of increasing temperature and decreasing rainfall were used to represent paths of possible future climate change. The simulated impacts of climate projections on durum wheat varieties at both sites were: grain yield reduction, slightly increasing kernel weight, and earlier anthesis dates. The late variety Creso showed a larger grain yield reduction compared to the early genotypes Duilio and Simeto. Anticipation of time to flowering was larger at Ussana (medium-low fertility soil) than at Benatzu (high fertility soil) with no differences between varieties. Earlier anthesis response was due to temperature increase rather than rainfall reduction, since in the CERES-Wheat model as well as in the majority of crop growth models water availability has no effect on crop development rate. Predictions for kernel weight were more uncertain with a slightly increasing trend in response to increasing temperatures and decreasing rainfall. The CERES-Wheat crop model seems to capture fairly well the greater resilience shown by early genotypes in Mediterranean rainfed conditions. In general, the CERES-Wheat model showed results in line with the findings from real experiments in different pedoclimatic conditions. For these reasons, CERES-Wheat appeared to be reliable when used to evaluate plant responses to projected climate change conditions and can represent a useful tool for developing adaptation strategies and measures such as the choice and selection of adapted genotypes to tackle the negative impact of climate change.
... The positive effect of avoidance of summer stress had already been observed via simulation with different GCM inputs at a location of Southern Italy (Donatelli et al 1998). Ludwig and Asseng, (2010) and Van Ittersum et al (2003) in previous simulation studies have shown that in drier environments earlier flowering varieties often increase potential yield, therefore, in a warm and drying climate, it might be beneficial to develop earlier flowering varieties; while in a warming climate, later flowering varieties are likely to increase grain yield provided that sufficient soil moisture is available. In agreement with our results, Semenov et al (2014) postulated that in Southern Europe, agronomic practices such as the sowing date are used to ensure booting and flowering occur before drought is excessive. ...
At the present time, climate change causing increasing temperature, dryness and CO 2 has exposed negative impacts on crops. In this study, four independent chambers were built to establish the expectation of different temperatures between the chambers. The experiment was carried out from January to March 2021 at An Giang University experimental area. Corn variety “Gold 58” was grown in 42 pots (34x28x28cm) in a chamber, 2 plants/pot. Temperature and CO 2 were hourly recorded. Plant height, leaf number, stover biomass were measured every 10 days period. The results showed that days to maturity in 4 chambers ranged from 62 to 67 days and accumulated temperature from transplanting or sowing to maturity (T sum ) varied from 1976 to 2077 ⁰ C d. The average of CO2 concentration of 10 days period in the chambers varied from 527.5 to 558.3 ppm at daytime and 626.1 to 744.4 ppm at night-time (highest in chamber 1). Plant height at harvest in chamber 1 was 306.7 ± 11.5 cm, while it was decreased by 6.1%; 11.7% in chambers 3 and 4. Total biomass above the ground in chamber 2, 3, 4 also significantly declined by 25.2%; 31.6% and 36.4% at harvest, respectively. Fruit yield also reduced by 14.3%, 34.9% and 34.1% respectively compared to chamber 1. Observed versus simulated comparison by our crop-model (based on R language programing) resulted in RRMSE value less than 8.2%. NSE index (Nash Sutcliffe Efficiency) of the models greater than 0.75 show that the models have high reliability.
Introduction2 Barley (Hordeum vulgare L.) is considered as the second most important grain crop after wheat, due to 1.75 million hectares harvested areas and 3.2 million tons’ production in Iran. The irrigated fields are contributed up to 45% of total barley harvested areas (equivalent to 1.7 million ha) and 70% of total barley production (equivalent to 2.2 million tons). Based on the statistics reported in recent years, about 2.5 million tons of barley imported from other countries. According to the impossibility of extending the barley cultivated areas and even the necessity of reducing fields in some parts of the country, increasing productivity per unit area of cultivated lands is recognized as the only practical way to boost the production of barley in Iran. In this regard, this study was conducted to estimate barley yield gap (Yg) and the potential of increasing barley production in irrigated condition as the first step to promote the yield and production of barley over the country. Materials and Methods Firstly, the main production zones of barley are determined; the zones which were contributed in more than 85% of barley production. The Designated climatic zones (DCZs) were identified using GYGA climatic zones (Global Yield Gap Atlas) and the distribution of barley harvested area raster layers. Subsequently, the Reference weather Stations (RWSs) within the DCZs were selected based on the values of the harvested area, and the types of soil in each of RWSs were determined by using of HC-27 soil map. SSM-iCrop2 as a crop simulation model has been employed to estimate the potential yield (Yp) in the RWSs of cultivated areas, which has previously been parameterized and evaluated, and the results have indicated the robustness of the model for simulating barley yield over the country. For estimating Yg, the data of actual yield (Ya) and the agronomic management data for estimating Yp during 15 growing seasons (2000-2014), were collected at RWSs scale. Using A bottomup approach, the yield, and production gap values were calculated at RWSs and subsequently aggregated to DCZs and finally, extended from DCZ to country-level according to the spatial distribution of crop area and climate zones. Results and Discussion Based on GYGA protocol, 48 RWSs within 12 DCZs of irrigated barley harvested areas were demonstrated. Aggregation from the RWSs results to DCZs illustrated that the average of potential yield in DCZs of irrigated barley was estimated 7090 kg.ha-1 and the range varied from 5283 to 8286 kg.ha-1. Nevertheless, the Ya range in these climate zones was calculated between 1406 and 3723 with an average of 3009 kg.ha-1. According to the results, the DCZs which confronted to higher temperatures during the growing season have lower yields and also a significant reverse correlation between the potential yield and the growth length period (R2 = 0.88 and p ≤0.01) were shown. The correlation between total received daily solar radiation during the growing and Yp in the DCZs was significant, positively season (R2 = 0.98 and p ≤0.01). At present, the range of difference between actual and potential yield varies between 3237 to 4697 kg.ha-1 with an average of 4081 kg.ha-1 (equivalent to 58% yield gap). In other words, just around 24 to 50 percent (on an average of 42 percent) of estimated Yp in irrigated barley fields can be attainable. According to the irrigated barley harvested areas, the actual and potential production gap are calculated about 2.21 and 2.99 million tons in the country, respectively, and under the best management condition can lead the production to be about 4.17 million tons. Conclusion According to the results, it was demonstrated about 58% relative yield gap between the averages of actual yield (3008 kg.ha-1) and potential yield (7090 kg.ha-1), which can be reduced by improving the production management in irrigated barley cultivated areas. For this reason, the current production of barley in irrigated lands can be increased from 2.12 to 4.17 million tons. This increase in production (1.96 million tons) could provide a significant part of the country's need to the barley and bring the country closer to achieve full selfsufficiency. Keywords: Actual yield, Crop simulation model, Global atlas, Potential yield
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We provide an overview of the state of knowledge on the climate change impacts on German crop production and generate model-based, quantitative and spatially differentiated simulations of the yield changes of the most important German arable crops, up to the middle of the century. To simulate yields, we use several agroecosystem models and provide a meta-analysis of the related scientific literature. In addition, we consider the effects of specific weather conditions such as heat and drought periods on yields in the past. In order to assess the future development, we use the data of different climate projections . On average, with regional differences, the simulations show no decline in yields until the middle of the century and no increase in yield variability. We observe a decrease in the effectiveness of the CO2 fertilization effect for yield increases of winter wheat over time. The yields of silage maize benefit the least from CO2 fertilization. For the past, we identify yield losses due to extreme summer and spring drought for almost all crops as well as due to heat events for winter wheat and partly for oilseed rape. Heat-related yield losses increase for winter wheat with increasing CO2 concentrations. However, we cannot identify an unambiguous increase in yield losses due to extreme drought or waterlogging in the future. Uncertainties in the results exist, amongst other reasons, due to a wide range of future precipitation development in the underlying climate models, in particular with regard to the reliability of the precipitation projection in spring. The simulations do not consider adaptation of production to climate change as well as negative yield effects due to potential increase in storms, hail storms, heavy rain or harmful organisms.
The impacts of global climate warming on maize yield vary regionally. However, less is known about how soil modulates regionally-specific impacts and soil properties that are able to alleviate adverse impacts of climate warming on maize productivity. In this study, we investigated the impacts of multiple soil inherent properties on the sensitivity of maize yield (SY,T) to growing season temperature across China. Our results show that a 1°C warming resulted in the largest yield decline (11.2±6.1%) in the mid-eastern region, but the moderate yield increase (1.5±2.9%) in the northeastern region. Spatial variability in soil properties explained around 72% of the variation in SY,T. Soil organic carbon (SOC) content positively contributed the greatest extent (28.9%) to spatial variation of SY,T, followed by field capacity (9.7%). Beneficial impacts of increasing SOC content were pronounced in the northeastern region where SOC content (11.9±4.3 g kg-1) was much higher than other regions. Other soil properties (e.g plant wilting point, sand content, bulk density, and saturated water content) were generally negatively correlated with SY,T. This study is the first one to answer how soil inherent properties can modulate the negative impacts of climate warming on maize yield in China. Our findings highlight the importance of SOC in alleviating adverse global warming impacts on maize productivity. To ensure food security for a rapidly increasing population under a changing climate, appropriate Feng et al (2022). Soil properties resulting in superior maize yields upon climate warming. Agronomy for Sustainable Development. Accepted for publication 1 August 2022. farming management practices that improve SOC content could reduce risk of adverse effects of global climate warming through a gain in yield stability and more resilient production in China's maize belt.
BACKGROUND
Oat (Avena sativa L.) is recognized for its impressive productivity in marginal environments, and the sowing rate is an important crop management practice that potentially enhances oat productivity. Previous studies have reported the effect of sowing rate on oat yield; however, the results from such studies are inconsistent. Thus, based on 43 studies across eight countries, this study aimed to assess changes in hay and grain yields in response to sowing rate and, in combination with a boosted regression tree, to evaluate and rank the dominant factors (e.g. climate conditions, soil conditions, and sowing rate) affecting changes in hay and grain yields of oat.
RESULTS
The results revealed that increasing the sowing rate significantly increased the response ratio of grain yields and hay yields by averages of 7.3% and 7.9% respectively. However, the response ratios of grain yields and hay yields in response to changes in sowing rate were affected by different factors. Climate condition and mean annual precipitation primarily affected the response ratios of hay yields, whereas the sowing rate dominated changes in the response ratios of grain yields, with the response ratios of grain yields peaking at a sowing rate of 85 kg ha⁻¹.
CONCLUSION
Optimizing the sowing rate with site‐specific environmental conditions could be a potential strategy for profitable oat production, given that oat can be produced under marginal environments (e.g. cool–wet climates and soil with low fertility). © 2022 Society of Chemical Industry.
Climate is the average of weather situation in a particular area, which affects all parts of ecosystem. Due to industrialization and urbanization, forests are cutting down and converted into living societies. This change in ecosystem disturbs the balance of ecosystem from decomposers to producers and consumers. Important part of ecosystem is plants (producers) that are energy providers. This alteration affects productivity and sustainability of plants. Wheat is staple food, which is highly affected by temperature and CO2 elevation. It not only affects wheat yield but also make wheat vulnerable to several diseases. High temperature causes a high rate of transpiration, which causes drought that ultimately leads to low productivity. A model was designed on drought conditions and result showed that global warming causes serious drought in 60% of wheat-growing areas of the world. Currently, drought affects 15% of wheat productivity. It was predicted that every 2°C shift of temperature can cause severe water shortage in the coming 20 to 30 years. Water shortage at milking and grain filling stage will affect yield. This chapter includes factors affecting climate, impact on wheat growth, yield, and elevation of carbon dioxide, impact on disease severity, prediction model for temperature rise, and CO2 curve in 2050.
With an approach combining crop modelling and biotechnology to assess the performance of three durum wheat cultivars (Creso, Duilio, Simeto) in a climate change context, weather and agronomic datasets over the period 1973–2004 from two sites, Benatzu and Ussana (Southern Sardinia, Itay), were used and the model responses were interpreted considering the role of DREB genes in the genotype performance with a focus on drought conditions. The CERES-Wheat crop model was calibrated and validated for grain yield, earliness and kernel weight. Forty-eight synthetic scenarios were used: 6 scenarios with increasing maximum air temperature; 6 scenarios with decreasing rainfall; 36 scenarios combining increasing temperature and decreasing rainfall. The simulated effects on yields, anthesis and kernel weights resulted in yield reduction, increasing kernel weight, and shortened growth duration in both sites. Creso (late cultivar) was the most sensitive to simulated climate conditions. Simeto and Duilio (early cultivars) showed lower simulated yield reductions and a larger anticipation of anthesis date. Observed data showed the same responses for the three cultivars in both sites. The CERES-Wheat model proved to be effective in representing reality and can be used in crop breeding programs with a molecular approach aiming at developing molecular markers for the resistance to drought stress.
CONTEXT:
Cotton is one of the most widely planted fiber crops in the world. Its growth, development, and yield are influenced by elevated atmospheric CO2 concentrations, temperature increases, and seasonal rainfall patterns. However, due to differences in research methods (such as crop models, climate models, and climate scenarios), there are uncertainties in the magnitude and direction of the impact of future climate change on cotton yields in existing studies.
OBJECTIVE
To comprehensively assess the potential impact of climate change and adaptation on yieldand analyze the associated uncertainties, 27 published studies (including 1353 samples) were used to establish a meta database of changes in future cotton yield.
METHODS
The responses of cotton relative yield change to changes in mean temperature, minimum temperature, maximum temperature, precipitation, and CO2 concentration were studied using local polynomial (Loess) regression in a full dataset. A linear mixed-effect model was then used to explore the quantitative relationship between them in a restricted dataset.
RESULTS AND CONCLUSIONS
Using the established full dataset, we found that when the average temperature exceeded 4.3 °C, or the average precipitation decreased or increased too much (>200%), the simulated cotton yield decreased. Elevated CO2 concentrations and appropriate management measures could alleviate the negative impact of climate change. By establishing a linear mixed-effect model, we found that temperature, precipitation, CO2 concentration changes, adaptive measures, study area, climate models, and climate scenarios had significant impacts on cotton yield change. For every 1 °C increase in average temperature, the cotton yield decreased by 1.64%. For every 1% increase in precipitation and 1 ppm increase in CO2 concentration, cotton yield increased by 0.09% and 0.05%, respectively. Cotton yield under adaptation measures was 8.97% higher than without any adaptation.
SIGNIFICANCE
The first systematic meta-analysis of the impact of climate change on cotton yield in major cotton-planting regionswas conducted herein. The findings provide new information for policies related to the impact of climate change and enhance our understanding of the future resilience of the cotton system.
The changing climate over the past few decades has been a major challenge for sustainable wheat production. Climate change includes factors such as increased carbon dioxide (CO2) concentration, changes in precipitation, and change in growing seasons. Climate change has a lot of impact on agricultural production. Crop phenology greatly changes due to climate change, particularly in warmer climates. Anthropogenic activities result in the emission of long-lived greenhouse gases (LLGHGs) and other short-lived climate pollutants (SLCPs). Developing countries are more vulnerable to climate change due to increase in the concentration of greenhouse gases (GHGs) such as CO2, methane (CH4), and nitrous oxide (N2O) produced due to anthropogenic activities. Fossil fuels are also among the main contributors of greenhouse effect due to the emission of CO2. Elevated CO2, high temperatures, and drought condition are affecting biomass and grain yield of crops. So, there is a need for breeding strategies to get more wheat varieties tolerant to high temperatures and drought conditions.
The study was carried out during two seasons 2016/2017 and 2017/2018 at Scientific Agriculture Research Center in Aleppo, General Commission for Scientific Agricultural Research GCSAR, Syria to estimate narrow and board sense heritability, additive and dominance variances, dominance degree, expected genetic advance, genotypic and phenotypic correlations and path analysis between grain yield and study traits in interspecific hybrids of durum wheat. Nine parents were planted in the first season 2016/2017 in AL-Sofera location, five of them were primitive wheat (2 genotypes of T.dicoccum, 2 genotypes of T.carthlicum and one genotype of T.polonicum) which were used as male parents, while three cultivated varieties beside one line from ICARDA were used as female parents. North Carokina II design was used for crossing to produce 20 crosses groups (5×4). The genotypes were planted in Randomized Complete Block Design (RCBD) with two replications in Hemaima Station. Data was collected for phenological traits (No. of days to heading, No. of days to physiological maturity and grain filling period), morphological traits (plant height, spike length, peduncle length and awns length), and yield components (1000-kernels weight, number of grains per spike and grain weight/spike). The results showed significant differences among genotypes for all studied traits, additive gene action controlled all traits, the genes that controlled all traits showed partial dominance, board sense heritability was high for all traits, whereas the heritability in narrow sense was high for most of the traits except peduncle length, and awns length were mid. A high value for expected genetic advance associated with high narrow sense heritability were recorded for plant height, spike length, grain filling period, grain weight/spike, and thousand kernels weight. A positive high significant genotypic and phenotypic correlations was recorded between grain yield with (thousand kernels weight, number of grains per spike, grain weight/spike and awns length) whereas that correlation was negative and high significant with No. of days to heading. The grain weight/spike was the most studied trait that contribute in grain yield with positive direct effect (0.74) followed by awns length with positive direct effect (0.34), then number of days to heading with negative direct effect (-0.33) and finally, thousand kernels weight with positive direct effect (0.17), as for indirect effect the number of grains per spike was the most studied traits that contribute in grain yield (0.63) followed by thousand kernels weight (0.55) through the grain weight/spike. This study confirmed the importance of each (grain weight/spike, thousand kernels weight, awns length and early heading) as selection criterion for development drought tolerance Genotypes.
Future rapid increases in atmospheric CO2 concentration [CO2] are expected, with values likely to reach ~550 ppm by mid‐century. This implies that every terrestrial plant will be exposed to nearly 40% more of one of the key resources determining plant growth. In this review we highlight selected areas of plant interactions with elevated [CO2] (e[CO2]), where recently published experiments challenge long‐held, simplified views. Focusing on crops, especially in more extreme and variable growing conditions, we highlight uncertainties associated with four specific areas: (1) While it is long known that photosynthesis can acclimate to e[CO2], such acclimation is not consistently observed in field experiments. The influence of sink‐source relations and nitrogen (N) limitation on acclimation is investigated and current knowledge about whether stomatal function or mesophyll conductance (gm) acclimate independently is summarised. (2) We show how the response of N uptake to e[CO2] is highly variable, even for one cultivar grown within the same field site, and how decreases in N concentrations ([N]) are observed consistently. Potential mechanisms contributing to [N] decreases under e[CO2] are discussed and proposed solutions are addressed. (3) Based on recent results from crop field experiments in highly variable, non‐irrigated, water‐limited environments, we challenge the previous opinion that the relative CO2 effect is greater under drier environmental conditions. (4) Finally, we summarise how changes in growth and nutrient concentrations due to e[CO2] will influence relationships between crops and weeds, herbivores and pathogens in agricultural systems.
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Global climate change could be harmful to agriculture. In particular, water availability and irrigation development under changed climatic conditions already pose a growing problem for crop production in the Mediterranean region. Wheat is the major significant crop in terms of food security. Therefore, in relation to these issues, this review gives an overview of climate change effects on wheat production in the Mediterranean environment of Turkey. Future climate data generated by a general circulation model (e.g., CGCM2) and regional climate models (e.g., RCM/MRI, CCSR-NIES and TERCH-RAMS) have been used to quantify the wheat growth and the soil-water-balance around the Eastern Mediterranean region of Turkey. The effects of climate change on the water demand and yield of wheat were predicted using the detailed crop growth subroutine of the SWAP (Soil-Water-Atmosphere-Plant). The Soil evaporation was estimated using the E-DiGOR (Evaporation and Drainage investigations at Ground of Ordinary Rainfed-areas) model. This review revealed that the changes in climatic conditions and CO2 concentration have caused parallel changes in the wheat yield. A close correspondence between measured and simulated yield data was obtained. The grain yield increased by about 24.7% (measured) and 21.9% (modelled) under a two-fold CO2 concentration and the current climatic conditions. However, this increase in the yield was counteracted by a temperature rise of 3 °C. Wheat biomass decreases under the future climatic conditions and the enhanced CO2 concentration, regardless of the model used. Without CO2 effects, grain yield also decreases for all the models. By contrast, the combined impact of elevated CO2 and increased temperature on grain yield of wheat was positive, but varied with the climatic models. Among the models, the CCSR-NIES and TERCH-RAMS denote the highest (24.9%) and lowest (6.3%) increases in grain yield respectively. The duration of the regular crop-growing season for wheat was 24, 21, and 27 days shorter as calculated for the future, mainly caused by the projected air temperature rise of 2.2, 2.4, and 3 °C for a growing period by the 2070s for CGCM2, CCSR-NIES and TERCH-RAMS respectively. The experimental results show large increases in the water use efficiency of wheat, due to the increases in CO2 concentration and air temperature. Despite the increased evaporative demand of the atmosphere, the increases in water use efficiency can be attributed to the shorter growing days and a reduction in the transpiration due to stomata closure. Unlike reference evapotranspiration and potential soil evaporation, actual evaporation from bare soils was estimated to reduce by 16.5% in response to a decrease in rainfall and consequently soil wetness in the future, regardless of the increases in the evaporative demand. It can be concluded that to maintain wheat production in the future, the water stress must be managed by proper irrigation management techniques.
The Huang-Huai-Hai (3H) Plain is one of the most important winter wheat production areas in China. In this paper, the relationship between climate factors, growth duration and yield of winter wheat from 1980 to 2013 in the 3H Plain was examined to reveal the responses of winter wheat growth duration and yield to global warming. The results showed that daily average, maximum and minimum air temperatures during the winter wheat growing season increased by 0.43, 0.35 and 0.54 °C every 10 years, respectively, on average across the whole region in the past 30 years, although temperatures varied with different spatial distributions. Historical changes in winter wheat phenology have been observed across the 3H Plain since 1990s in that the vegetative growth period of winter wheat shortened by 4.6 days, the reproductive growth period increased by 1.8 days and the whole growth period was shortened by 2.9 days per 10-year period. The changes in the growth period were significantly negatively correlated with minimum temperature increases. The 1 °C minimum temperature increase shortened the vegetative growth period of winter wheat by 4.5 days. Accounting for agricultural technical improvement and other non-climatic factors, an average temperature increase of 1 °C enhanced winter wheat yield by 2.1% in the northern plain and decreased yield by 4.0% in the southern plain. We found an average temperature during the winter wheat growth season of 8.6 °C as the yield variation threshold, and the yield would decrease above this threshold in this region.
Following an overview of climate change and global crop productivity, the book is divided into 4 sections: the problem-changing biosphere (climatic change and variability, and agricultural contributions to greenhouse gas emissions); crop ecosystem responses to climatic change (rice, maize and sorghum, soyabean, cotton, root and tuberous crops, vegetable crops, tree crops, productive grasslands, rangelands, crassulacean acid metabolism crops, crop-weed interactions, pests and population dynamics, soil organic matter dynamics, and interactive effects of ozone, ultraviolet-B radiation, sulfur dioxide and carbon dioxide); mitigation strategies (crop breeding strategies for the 21st century, and role of biotechnology in crop productivity in a changing environment); and economic and social impacts (global, regional and local food production and trade in a changing environment).
The recent increase in soil erosion in Britain is the result of continued intensification of farming and a major land-use change from spring-planted to autumn-planted cereals. Field studies of erosion and computer models are the basis of this attempt to predict changes of rates under future climate conditions. Increases in winter rainfall, summer storm frequency, the area of irrigated land, and the introduction of new erosion-susceptible crops such as maize, will increase erosion rates. Off-farm impacts of flooding and pollution will continue to pose a greater threat, at least in the short term, than soil loss or yield reduction as a result of soil thinning. We require more detailed information on future climate, land-use change and economic conditions to make prediction of erosion rates less speculative.
High rates of deep drainage (water loss below the root-zone) in Western
Australia are contributing to groundwater recharge and secondary salinity.
However, quantifying potential drainage through measurements is hampered by
the high degree of complexity of these systems as a result of diverse soil
types, a range of crops, different rainfall regions, and in particular the
inherent season-to-season variability. Simulation models can provide the
appropriate means to extrapolate across time and space. The Agricultural
Production Systems Simulator (APSIM) was used to analyse deep drainage under
wheat crops in the Mediterranean climate of the central Western Australian
wheatbelt. In addition to rigorous model testing elsewhere, comparisons
between simulated and observed soil water loss, evapotranspiration, and deep
drainage for different soil types and seasons confirmed the reasonable
performance of the APSIM model.
The APSIM model was run with historical weather records (70–90 years)
across 2 transects from the coast (high rainfall zone) to the eastern edge of
the wheatbelt (low rainfall zone). Soils were classified as 5 major types:
deep sand, deep loamy sand, acid loamy sand, shallow duplex (waterlogging),
and clay soil (non-waterlogging). Simulations were carried out on these soil
types with historical weather records, assuming current crop management and
cultivars. Soil water profiles were reset each year to the lower limit of
plant-available water, assuming maximum water use in the previous crop.
Results stressed the high degree of seasonal variability of deep drainage
ranging from 0 to 386 mm at Moora in the high rainfall region (461 mm/year
average rainfall), from 0 to 296 mm at Wongan Hills in the medium rainfall
region (386 mm/year average rainfall), and from 0 to 234 mm at Merredin in
the low rainfall region (310 mm/year average rainfall). The largest
amounts of drainage occurred in soils with lowest extractable water-holding
capacities. Estimates of annual drainage varied with soil type and location.
For example, average (s.d.) annual drainage at Moora, Wongan Hills, and
Merredin was 134 (73), 90 (61), and 36 (43) mm on a sand, and 57 (64), 26
(43), and 4 (18) mm on a clay soil, respectively. These values are an order of
magnitude higher than drainage reported elsewhere under native vegetation.
When not resetting the soil each year, carry-over of water left behind in the
soil reduced the water storage capacity in the subsequent year, increasing
long-term average deep drainage, depending on soil type and rainfall region.
The analyses revealed the extent of the excess water problem that currently
threatens the sustainability of the wheat-based farming systems in Western
Australia.
The influence of elevated CO 2 (350, 550 or 900 litre litre -1) and N supply ranging from deficient to excess (0-133 mg N kg -1 soil week -1) on leaf N concentration and shoot growth of wheat cv. Hartog, was investigated. Shoot growth was 30% greater at 550 litre litre -1 compared to ambient CO 2 at all levels of N supply. At 900 litre litre -1 CO 2 concentration, no increase in shoot growth was observed at low N supply. However, growth more than doubled at high N supply (67 mg N kg -1). Growth effects were closely matched by changes in sink development. It is suggested that sink strength, mediated through N supply, controlled the shoot growth response to elevated CO 2. Shoot N concentration was lower at each level of CO 2 enrichment and the greatest effect (30% reduction) occurred at 900 litre CO 2 litre -1 and 33 mg N kg -1. The effect of high CO 2 on shoot N concentration diminished as N supply increased and, at the highest N addition rate, only a 7% reduction in shoot N concentration was evident. Changes in foliar N concentration due to CO 2 enrichment were closely correlated with lower soluble protein concentration, accounting for 58% of the total leaf N reduction. Ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) levels were also reduced at high CO 2 and N was allocated away from Rubisco and into other soluble proteins at high CO 2 when N supply was low. Non-structural carbohydrate concentration (dry weight basis) was greatest at 900 litre CO 2 litre -1 and low N supply and may have reduced Rubisco concentration via a feed-back response. Critical foliar N concentrations (N concentrations at 90% of maximum shoot growth) were reduced from 43 mg g -1 at ambient CO 2, to 39 and 38 mg g -1 at 550 and 900 litre CO 2 litre -1, respectively. Elevated CO 2 and N supplies of 0-17 mg N kg -1 reduced flour protein concentration by 9-13%.
Wheat ( Triticum aestivum) cv. Hartog and Rosella were grown at CO 2 concentrations of 280 l litre -1 (representing the pre-industrial CO 2 concentration), 350 l litre -1 (ambient) or 900 l litre -1 (an extreme projection of atmospheric CO 2 concentration). The plants were grown in naturally lit glasshouses in 7 litre pots containing soil to which basal nutrients had been added and the pH adjusted to 6.5. Hartog yielded 2.4 g of grain per plant when grown at 280 l CO 2 litre -1. This yield was increased by 38% and 75% at CO 2 concentrations of 350 and 900 l litre -1, respectively. These changes were due to increase in both grain number and individual grain weight as the level of CO 2 was raised. The yield of Rosella was unaffected by altering the CO 2 concentration. Increasing the CO 2 concentration reduced grain protein concentration of cv. Hartog from 17.4% at 280 l CO 2 litre -1 to 16.5% and 16% at CO 2 concentrations of 350 and 900 l litre -1, respectively. The grain protein concentration of cv. Rosella was reduced from 10.7% to 10.2% by increasing the CO 2 concentration from 280 l litre -1 to 350 l litre -1; however, an additional increase in the CO 2 concentration to 900 l litre -1 had no effect on grain protein concentration. In Hartog flour, the highest proportion of polymeric protein in the flour (7.7%) occurred at 280 l CO 2 litre -1. This was reduced to 6.3% at 350 l CO 2 litre -1 but then increased again to 7.0% at 900 l CO 2 litre -1. These changes in concentration of polymeric protein were correlated ( r=0.58) with changes in mixing properties. The mixing time required to produce optimum dough strength was greatest at 900 l CO 2 litre -1 (181 s), then 141 s and 151 s at 350 and 280 l CO 2 litre -1, respectively. These changes in mixing time could not be explained by changes in grain protein concentration. The proportion of 'B' starch granules (<10 m diameter) increased from 25% of total weight of starch at 280 l CO 2 litre -1 to 30% at CO 2 concentrations 350 and 900 l litre -1. There were generally no effects of CO 2 concentration on dough mixing properties or starch granule size distribution for Rosella.
Climate models indicate that there will be an increase in both average annual temperature (+4.5oC) and rainfall in the Midwestern U.S. by the year 2050 which may result in warmer, wetter conditions. Perhaps the most important factor will be less predictable weather patterns that will emerge, increasing the frequency of extreme weather events such as heavy downpours of precipitation, late season frosts and droughts. For example, July rainfall may increase 20% and might come in just two rainfall events. This study combines expertise from several disciplinary areas with modeling strategies to assess the impact of global climate change on midwestern agriculture. Predictions of warmer summers, wetter springs, and more extreme events indicate that the cropping system may need to be adjusted to effectively conserve soil, maintain timely planting, avoid early season frost damage, and respond to warmer growing conditions. Climate projections (HADCM2), crop growth models (DSSAT), the Water Erosion Prediction Project (WEPP) model as well as net farm returns computations are used to study some of the choices farmers will have. Management practices to be evaluated in this study include altering the crop mix and changing the time of planting. Planting of cover crops and reducing the amount of tillage performed may also be viable alternatives to reduce the amount of soil loss resulting from more intense storm events. This paper will address some of the key findings from the project to date, and emphasize the extent to which extreme events under climate change raise special concerns about soil erosion. It will offer insights into the conditions midwestern farms may face and offer alternatives for preserving the quality of the soil resource. As yet we do not account for improvements in crop genetics.
High rates of drainage and leaching of nitrates in deep sands in Western Australia are contributing to groundwater recharge and soil acidification in this region. Strategies are being sought to increase water and nitrogen (N) use in the legume-based cropping systems. Choice of appropriate management strategies is complicated by the diversity of soil types, the range of crops, and the inherent season to season variability. Simulation models provide the means to extrapolate beyond the bounds of experimental data if accurate predictions of key processes can be demonstrated. This paper evaluates the accuracy of predictions of soil water content, evapotranspiration, drainage, inorganic N content in soil, nitrate (NO3-) leaching, wheat growth, N uptake, and grain yields obtained from the Agricultural Production Systems Simulator (APSIM) model when this was initialised with appropriate information on soil properties and wheat varieties commonly grown on deep sands in the 500 mm rainfall zone west of Moora in Western Australia. The model was found to give good predictions of soil water content, evapotranspiration, deep drainage, and overall NO3- leaching. Temporal changes in inorganic N in soil were simulated, although the small concentrations in soil inorganic N precluded close matching of paired observed and predicted values. Crop growth and N uptake were closely predicted up to anthesis, but a poor fit between observed and predicted crop growth and N uptake was noted post anthesis. Reasons for the discrepancies between modelled and observed values are outlined. The model was run with historical weather data (81 years) and different initial soil water and inorganic soil N profiles to assess the probability of drainage and NO3- leaching, and the grain yield potentials for wheat grown on deep sands in the region west of Moora. Simulation showed that the soil water and the soil inorganic N content at the beginning of each season had no effect on grain yield, implying that pre-seed soil NO3- was largely lost from the soil by leaching. There was a 50% probability that 141 mm of winter rainfall could drain below 1.5 m and a 50% probability that 53 kg N/ha could be leached under wheat following a lupin crop, where initial soil water contents and soil NO3- contents used in the model were those measured in a deep sand after late March rainfall. Simulated application of N fertiliser at sowing increased both grain yield and NO3- leaching. Splitting the N application between the time of sowing and 40 days after sowing decreased NO3- leaching, increased N uptake by wheat, and increased grain yield, findings which are consistent with agronomic practice. The high drainage and leaching potential of these soils were identified as the main reasons why predicted yields did not approach the French and Schultz potential yield estimates based on 20 kg grain yield per mm of rainfall. When the available cater was reduced by simulated drainage, simulated grain yields for the fertilised treatments approached the potential yield line.
A computer simulation model to analyse risks of soil erosion to long-term crop production is described. The model, called PERFECT, simulates interactions between soil type, climate, fallow management strategy and crop sequence. It contains six main modules; data input, water balance, crop growth, crop residue, erosion and model output. Modules are arranged in a framework that allows alternative modules to be used as required for the potential range of applications. The model contains dynamic crop growth models for wheat, sorghum and sunflower. Validation of PERFECT against small catchment and contour bay data collected throughout Queensland showed that PERFECT explained up to 84% of the variation in total available soil water, 89% of the variation in daily runoff, and up to 75% of the variation in grain yield. Average annual soil erosion was accurately predicted but daily erosion totals were less accurate due to the exclusion of rainfall intensity in erosion prediction. Variability in climate dominates agricultural production in the subtropical region of Australia. The validated model can be coupled with long-term climate and soils databases to simulate probabilities of production and erosion risks due to climatic variability. It provides a method to determine the impact of soil erosion on long-term productivity.
The likely consequences of future high levels of atmospheric CO 2 concentration on wheat ( Triticum aestivum L.) grain nutritional and baking quality were determined.
Two free‐air CO 2 enrichment (FACE; 550 mmol mol ⁻¹ ) experiments were conducted at ample (Wet) and limiting (Dry) levels of irrigation, and a further two experiments at ample (High‐N) and limiting (Low‐N) nitrogen concentrations. Harvested grain samples were subjected to a battery of nutritional and bread‐making quality tests.
The Dry treatment improved grain quality slightly (protein +2%; bread loaf volume +3%). By contrast, Low‐N decreased quality drastically (protein −36%; loaf volume −26%). At ample water and N, FACE decreased quality slightly (protein −5%; loaf volume −2%) in the irrigation experiments and there was no change in the nitrogen experiments. At Low‐N, FACE tended to make the deleterious effects of Low‐N worse (protein −33% and −39%, at ambient CO 2 and FACE, respectively; loaf volume −22% and −29% at ambient CO 2 and FACE, respectively).
The data suggest that future elevated CO 2 concentrations will exacerbate the deleterious effects of low soil nitrogen on grain quality, but with ample nitrogen fertilizer, the effects will be minor.
In Western Australia, an abundance of salt within the deeply-weathered soil profiles and the clearing of native vegetation have resulted in unparalleled hydrological changes and extensive salinisation. Groundwater levels have risen by more than 30 m, and aquifers now occur where none existed before clearing. Currently, an area consisting of more than 1.8 million hectares (9.4 percent) of cleared farmland in Western Australia is salt affected. The salt-affected area is expected to double in size within the next 25 years and double again before reaching a new equilibrium.
Salinity in streams is increasing at a rate of 10–90 mg/L each year. As a result, large areas of remnant vegetation and its contained biological diversity are threatened. Salinity management should be based on a sound knowledge of hydrogeological systems. Land managers should have access to cost-effective methods of treatment and packages of biophysical information that can be used to design and predict the impact of physical and economic management systems. To date, too few cost-effective methods exist. Furthermore, a complex hydrogeology has contributed to low success rates of predictions.
The fate of Western Australia's agriculture, water resources, and natural environment depends on acknowledging the lessons of the past and investing in the future. Priority areas for hydrologic research should be identified, and management methods that are currently available need to be incorporated into new farming systems.
Our central goal is to determine the importance of including both mean and variability changes in climate change scenarios in an agricultural context. By adapting and applying a stochastic weather generator, we first tested the sensitivity of the CERES-Wheat model to combinations of mean and variability changes of temperature and precipitation for two locations in Kansas. With a 2C increase in temperature with daily (and interannual) variance doubled, yields were further reduced compared to the mean only change. In contrast, the negative effects of the mean temperature increase were greatly ameliorated by variance decreased by one-half. Changes for precipitation are more complex, since change in variability naturally attends change in mean, and constraining the stochastic generator to mean change only is highly artificial. The crop model is sensitive to precipitation variance increases with increased mean and variance decreases with decreased mean. With increased mean precipitation and a further increase in variability Topeka (where wheat cropping is not very moisture limited) experiences decrease in yield after an initial increase from the 'mean change only case. At Goodland Kansas, a moisture-limited site where summer fallowing is practiced, yields are decreased with decreased precipitation, but are further decreased when variability is further reduced. The range of mean and variability changes to which the crop model is sensitive are within the range of changes found in regional climate modeling (RegCM) experiments for a CO2 doubling (compared to a control run experiment). We then formed two types of climate change scenarios based on the changes in climate found in the control and doubled CO2 experiments over the conterminous U. S. of RegCM: (1) one using only mean monthly changes in temperature, precipitation, and solar radiation; and (2) another that included these mean changes plus changes in daily (and interannual) variability. The scenarios were then applied to the CERES-Wheat model at four locations (Goodland, Topeka, Des Moines, Spokane) in the United States. Contrasting model responses to the two scenarios were found at three of the four sites. At Goodland, and Des Moines mean climate change increased mean yields and decreased yield variability, but the mean plus variance climate change reduced yields to levels closer to their base (unchanged) condition. At Spokane mean climate change increased yields, which were somewhat further increased with climate variability change. Three key aspects that contribute to crop response are identified: the marginality of the current climate for crop growth, the relative size of the mean and variance changes, and timing of these changes. Indices for quantifying uncertainty in the impact assessment were developed based on the nature of the climate scenario formed, and the magnitude of difference between model and observed values of relevant climate variables.
Crop simulation models are used widely to predict crop growth and development in studies of the impact of climatic change. An important problem is the uncertainty inherent in the construction of the future weather scenarios used as inputs to models. In seeking to couple meteorological information to crop-climate models it must be remembered that many interactions between crops and weather are non-linear. Non-linearity of response means it is necessary to preserve the variability of weather sequences to estimate the effect of climate on agricultural production and to assess agricultural risk. To date, only changes in average weather parameters derived from general circulation models (GCMs) and then applied to historical data have been used to construct climatic change scenarios and in only a few studies were changes in climatic variability incorporated. Accordingly, a computer system, AFRCWHEAT 3S, was designed to couple the simulation crop model for wheat, AFRCWHEAT2, with a stochastic weather generator based on the series approach. AFRCWHEAT 3S provides flexible construction of climatic scenarios and allows changes not only in mean values but also in the variance or type of distribution for a wide variety of weather parameters. Analyses of sensitivity to changes in the variability of temperature and precipitation, as compared with changes in their mean values, were made for locations in the UK and France for winter wheat. Results indicated that changes in climatic variability can have a more profound effect on yield and its associated risk than changes in mean climate.
APSIM-wheat is a crop system simulation model, consisting of modules that incorporate aspects of soil water, nitrogen (N), residues, and crop development. The model was used to simulate above- and belowground growth, grain yield, water and N uptake, and soil water and soil N in wheat crops in Western Australia. Model outputs were compared with detailed field experiments from four rainfall zones, three soil types, and five wheat genotypes. The field experiments covered 10 seasons, with variations in sowing date, plant density, N fertiliser, deep ripping and irrigation. The overall APSIM model predictions of shoot growth, root depth, water and N uptake, soil water, soil N, drainage and nitrate leaching were found to be acceptable. Grain yields were well predicted with a coefficient of determination r2(1:1)=0.77, despite some underestimation during severe terminal droughts. Yields tended to be underestimated during terminal droughts due to insufficient pre-anthesis stored carbohydrates being remobilised to the grain. Simulation of grain protein, and depth to the perched water table showed limited accuracy when compared with field measurements. In particular, grain protein tended to be overpredicted at high protein levels and underpredicted at low levels. However, specific simulation studies to predict biomass, yield, drainage and nitrate leaching are now possible for wheat crops on the tested soil types and rainfall zones in Western Australia.
Climate Change 2001: The Scientific Basis is the most comprehensive and up-to-date scientific assessment of past, present and future climate change. The report: • Analyses an enormous body of observations of all parts of the climate system. • Catalogues increasing concentrations of atmospheric greenhouse gases. • Assesses our understanding of the processes and feedbacks which govern the climate system. • Projects scenarios of future climate change using a wide range of models of future emissions of greenhouse gases and aerosols. • Makes a detailed study of whether a human influence on climate can be identified. • Suggests gaps in information and understanding that remain in our knowledge of climate change and how these might be addressed. Simply put, this latest assessment of the IPCC will again form the standard scientific reference for all those concerned with climate change and its consequences, including students and researchers in environmental science, meteorology, climatology, biology, ecology and atmospheric chemistry, and policymakers in governments and industry worldwide.
Clear, plastic-coated, temperature gradient tunnels (TGTs), 8 × 1.25 × 1-25 m were designed and built to examine how temperature and CO2 affect the yield of wheat in the field. Each of the three modules of each TGT was maintained at a different temperature above the ambient temperature using solar heating during the day and electric heating at night. The maximum day-time increment above ambient for the warmest module was 5ºC and full-season averages were close to 2ºC. TGTs were paired, with air in one being enriched to 700 μL L-1 CO2, and in the other being maintained at ambient CO2. Crops were planted in the TGTs at two sites in either summer (December) or winter (April and July) and they remained there until maturity. CO2 enrichment increased the yield in summer plantings by up to 36%. In winter plantings, with mean temperatures between sowing and anthesis of around lVC, the responses to CO2 were small averaging only 7% (range 1-12%). Though yield declined with increasing temperature in the TGTs in summer, there was a clear trend for an increasing response to CO2 at these higher temperatures, i.e. yield declined less. In summer, there was no convincing evidence for a different relative response to CO2 in two isolines which differed in maturity date, though the later line yielded more under the highest temperature regime (mean of 22-24ºC between sowing and anthesis). In winter there was a strong trend for the isoline requiring less vernalisation to respond more to CO2. It is suggested that early progress towards flowering might predispose wheat to a greater CO2 response. Overall, the data indicated that the positive response to CO2 in grain yield is likely to increase at approximately 1.8% per 1°C in wheat crops that are not limited by water. Extrapolation indicated that the temperature at which there was no response to CO2 was 5ºC. All yield responses reflected biomass responses as harvest index was unchanged by CO2
Wheat was grown at a density of 120 plants m-2 in deep pots of soil in two artificially illuminated growth cabinets. One cabinet was left at ambient CO2 levels and the other enriched by 250 volumes per million (vpm). Four levels of growth-restricting water supply were imposed. Responses by the two cultivars used (Gabo and WW15) did not differ appreciably in terms of the mature crop dry-weight parameters examined.
Comparison of the crop responses to water supply indicated sufficient correspondence between generalized field behaviour and cabinet behaviour to justify tentative interpretation of the results in terms of possible response of water-limited field wheat crop yields to the globally rising level of atmospheric CO2.
The less water made available to the crop the less was the absolute response of grain yield to CO2 enrichment, but the greater was the response relative to the control yield. Under extreme aridity (about 100-120 mm crop transpiration overall), the data implied infinite relative enhancement of yield due to CO2 enrichment, because it allowed some grain growth where none occurred without extra CO2. The absolute yield enhancement was equivalent to 5-13 kg ha-1 per 1.2 vpm increment of atmospheric CO2 concentration. The level of CO2 in the global atmosphere is currently rising by about 1.2 vpm year-1. The higher temperature at which the crops were grown (19°C), relative to average field conditions in many wheat areas, may influence this interpretation.
Photosynthesis is the incorporation of carbon, nitrogen, sulphur and other substances into plant tissue using light energy from the sun. Most of this energy is used for the reduction of carbon dioxide and, consequently, there is a large body of biochemical and biophysical information about photo synthetic carbon assimilation. In an ecophysiological context, we believe that most of today’s biochemical knowledge can be summarized in a few simple equations. These equations represent the rate of ribulose bisphosphate (RuP2)-saturated carboxylation, the ratio of photorespiration to carboxylation, and the rates of electron transport/photophosphorylation and of “dark” respiration in the light. There are many other processes that could potentially limit CO2 assimilation, but probably do so rarely in practice. Fundamentally this may be due to the expense, in terms of invested nitrogen, of the carboxylase and of thylakoid functioning. To reach our final simple equations we must first discuss the biochemical and biophysical structures — as they are understood at present — that finally reduce the vast number of potentially rate-limiting processes to the four or five listed above. A diagrammatic representation of these processes is given in Fig. 16.1.
'Haying-off' was studied by comparing wheat responses to applied nitrogen (N) at 3 sites in southern New South Wales, which differed in the amount and timing of rainfall during crop growth. At a site where the crops encountered little water deficit, dry grain yield increased from 607 g/m(2) for a low-N control crop to 798 g/m(2) for a high-N crop. At a site with severe terminal drought, dry grain yield decreased 24% from 374 g/m(2) for the control, to 284 g/m(2) for the highest N crop. At the third site, yields increased with small applications of N, whereas greater applications resulted in a negative yield response. At the 2 latter sites, the crops that showed decreased yield with applied N had clearly hayed-off. At all sites, irrespective of water status, N application resulted in increases in biomass at anthesis, spike density, kernels per spike, and kernel number. Kernel weight decreased in response to additional N at all sites, but most markedly at the haying-off sites where it decreased by up to 38%. Harvest index increased in response to N at the high-rainfall site, but decreased in crops that hayed-off. Grain protein increased in response to N at all sites, with a range from 9% to 18% at the haying-off sites. The apparent retranslocation of assimilates to grain contributed 37-39% of grain yield (depending on N supply) at the high-rainfall site, compared with 75-100% at the haying-off sites. In contrast, when apparent retranslocation was expressed in relation to biomass at anthesis, it remained relatively constant, amounting to 23-26% at the high-rainfall site and 24-28% when crops hayed-off. By anthesis, high-N crops extracted more soil water than the low-N crops. By maturity the most severely hayed-off crop had extracted 10 mm less soil plater than a low-N crop, but at the high rainfall site the high-N crops extracted 20 mm more soil water than the control crops. The weather conditions between anthesis and physiological maturity were relatively mild, with no daily maximum temperatures above 30 degrees C and no sudden increases in evaporative demand. Thus, there appeared to be 3 processes leading to haying-off. Firstly the results confirm previous studies showing that haying-off was associated with reduced post-anthesis assimilation in response to a lack of soil water. The water deficit was due to vigorous vegetative growth stimulated by a high level of soil N and was not associated with heat shocks or sudden increases in evaporation. Secondly, the most severely hayed-off crop failed to extract soil water fully, leading to a further reduction in post-anthesis assimilation. Thirdly, there was inadequate apparent retranslocation of pre-anthesis reserves to compensate for the lack of post-anthesis assimilation.
Two models that differ markedly in how they represent the crop-soil system have been used to simulate soil processes and crop production in the long-term experiment at Hermitage Research Station, Warwick, Queensland. The experiment was designed to examine the effects of tillage, stubble management, and nitrogen (N) fertiliser on the productivity of a winter cereal-summer fallow cropping system. It commenced in 1968 and the treatments have been maintained until the present. Both models reproduced the observations well enough to indicate their suitability for providing useful insights into the behaviour of cropping systems where the focus is on depletion of soil fertility. -from Authors
A free‐air CO 2 enrichment (FACE) experiment was conducted at Maricopa, Arizona, on wheat from December 1992 through May 1993. The FACE apparatus maintained the CO 2 concentration, [CO 2 ], at 550 μmol mol ⁻¹ across four replicate 25‐m‐diameter circular plots under natural conditions in an open field. Four matching Control plots at ambient [CO 2 ] (about 370 μmol mol ⁻¹ ) were also installed in the field. In addition to the two levels of [CO 2 ], there were ample (Wet) and limiting (Dry) levels of water supplied through a subsurface drip irrigation system in a strip, split‐plot design.
Measurements were made of net radiation, R n ; soil heat flux, G o ; soil temperature; foliage or surface temperature; air dry and wet bulb temperatures; and wind speed. Sensible heat flux, H , was calculated from the wind and temperature measurements. Latent heat flux, λ ET , and evapotranspiration, ET , were determined as the residual in the energy balance. The FACE treatment reduced daily total R n by an average 4%. Daily FACE sensible heat flux, H , was higher in the FACE plots. Daily latent heat flux, λ ET , and evapotranspiration, ET , were consistently lower in the FACE plots than in the Control plots for most of the growing season, about 8% on the average.
Net canopy photosynthesis was stimulated by an average 19 and 44% in the Wet and Dry plots, respectively, by elevated [CO 2 ] for most of the growing season. No significant acclimation or down regulation was observed. There was little above‐ground growth response to elevated [CO 2 ] early in the season when temperatures were cool. Then, as temperatures warmed into spring, the FACE plants grew about 20% more than the Control plants at ambient [CO 2 ], as shown by above‐ground biomass accumulation. Root biomass accumulation was also stimulated about 20%. In May the FACE plants matured and senesced about a week earlier than the Controls in the Wet plots. The FACE plants averaged 0.6 °C warmer than the Controls from February through April in the well‐watered plots, and we speculate that this temperature rise contributed to the earlier maturity. Because of the acceleration of senescence, there was a shortening of the duration of grain filling, and consequently, there was a narrowing of the final biomass and yield differences. The 20% mid‐season growth advantage of FACE shrunk to about an 8% yield advantage in the Wet plots, while the yield differences between FACE and Control remained at about 20% in the Dry plots.
Increases in crop growth under elevated atmospheric CO2 concentration (CA) have frequently been observed to be greater under water-limited versus non-limited conditions. Crop simulation models used in climate change studies should be capable of reproducing such changes in growth response to CA with changes in environmental conditions. We propose that changes with soil water status in crop growth response to CA can be simulated if stomatal resistance is considered to vary directly with air-leaf CA gradient, inversely with leaf carboxylation rate, and exponentially with leaf turgor. Resistance simulated in this way increases with CA relatively less, and CO2 fixation increases with CA relatively more, under water-limited versus non-limited conditions. As part of the ecosystem model ecosys, this simulation technique caused changes in leaf conductance and CO2 fixation, and in canopy water potential, temperature and energy balance in a modelling experiment that were consistent with changes measured under 355 versus 550 μmol mol−1CA and low versus high irrigation in a free air CO2 enrichment (FACE) experiment on wheat. Changes with CA in simulated crop water relations allowed the model to reproduce under 550 μmol mol−1CA and low versus high irrigation a measured increase of 20 versus 10% in seasonal wheat biomass, and a measured decrease of 2 versus 5% in seasonal evapotranspiration. The basic nature of the processes simulated in this model is intended to enable its use under a wide range of soil, management and climate conditions.
We investigated the effect of two different spatial scales of climate change scenarios on crop yields simulated by the EPIC crop model for corn, soybean, and wheat, in the central Great Plains of the United States. The effect of climate change alone was investigated in Part I. In Part II (Easterling et al., 2001) we considered the effects ofCO2 fertilization effects and adaptation in addition to climate change. The scenarios were formed from five years of control and 2CO2 runs of a high resolution regional climate model (RegCM) and the same from an Australian coarse resolution general circulation model (GCM), which provided the initial and lateral boundary conditions for the regional model runs. We also investigated the effect of two different spatial resolutions of soil input parameters to the crop models. We found that for corn and soybean in the eastern part of the study area, significantly different mean yield changes were calculated depending on the scenario used. Changes in simulated dryland wheat yields in the western areas were very similar, regardless of the scale of the scenario. The spatial scale of soils had a strong effect on the spatial variance and pattern of yields across the study area, but less effect on the mean aggregated yields. We investigated what aspects of the differences in the scenarios were most important for explaining the different simulated yield responses. For instance, precipitation changes in June were most important for corn and soybean in the eastern CSIRO grid boxes. We establish the spatial scale of climate changescenarios as an important uncertainty for climate change impacts analysis.
The possible consequences of climate change on carbon and nitrogen budgets of winter wheat were examined by means of model predictions. Biomass, nitrogen, water and heat dynamics were simulated for long-term climatic conditions in central and southern Sweden for a clay soil and a sandy soil. The effects of elevated atmospheric CO2 and changed climate as predicted for 2050 were simulated daily with two linked process orientated models for soil and plant (SOIL/SOILN). The models had previously been calibrated against several variables at the sites under present conditions, and the long-term predictions at present climate were shown to correspond reasonably well with measured soil C and N trends in long-term experiments. The climate and CO2 conditions for the year 2050 were represented by climatic scenarios from a global climate model, and the elevated atmospheric CO2 concentration was assumed to change plant parameter values in accordance with literature data.For the year 2050, winter wheat production was predicted to increase by 10–20% (depending on soil type) compared with the present value. Plant N concentration decreased although N mineralisation increased by 18%. Drainage was predicted to increase which resulted in increased N leaching by 17%, and the decrease in soil C became larger. The predictions were found to be most sensitive to assumptions concerning changes of radiation use efficiency, stomatal conductance, air temperature and precipitation. Hence, the load of N and C to surrounding ecosystems and the atmosphere, on a ground surface basis, was predicted to increase under climate change. However, because the harvest will increase, these negative effects of climate change on a yield basis will be almost zero, except that N leaching from the sandy soil will still increase.
This paper examines whether high temperature modifies the effects of photoperiod on reproductive development in six Triple Dirk isolines. Isolines chosen for study were genetically diverse in their responses to vernalisation and photoperiod. The information is important when attempting to predict the performance of temperate wheats in high temperature locations such as the tropics. Averaged across isolines, days to ear emergence was reduced by 12 days for every hour that photoperiod lengthened between 9 and 13 h in both 15 and 25°C mean temperature regimes, but responses amongst isolines differed. In day-degree terms, heading was delayed by high temperature in all isolines under all photoperiods. In four of the lines, the delay to heading due to high temperature increased as photoperiod shortened. This interaction resulted in similar calendar days to heading at 15 and 25°C under a 9-h photoperiod.Heading date gave no insight into the timing of earlier phenological events. In general, high temperature delayed the appearance of double ridges under short photoperiods in calendar day terms, but accelerated later development up to ear emergence. Conversely, high temperature shortened the time to double ridges under long photoperiods. At moderate temperature (15°C), delays in the time of appearance of double ridges, associated both with shortening of photoperiod and with genotype, led to the initiation of more spikelet primordia. By contrast, at high temperature (25°C), extended delays to double-ridge appearance resulted in little or no increase in spikelet primordia, as primordium production on the apex slowed and eventually ceased in some isolines. A retardation in primordium initiation was not reflected in leaf emergency rates. Consequently, the plastochron interval, expressed in day-degree terms, was affected far more by high temperature than the phyllochron interval.
The wheat module, I_WHEAT, from the APSIM cropping system model was used to investigate the impacts of changes in atmospheric CO2 concentrations on wheat crops by modifying radiation use efficiency, transpiration efficiency, specific leaf area and critical nitrogen concentrations. The effects of several combinations of atmospheric CO2, climate change and crop adaptation strategies on wheat production in the Burnett region were studied. Mean wheat yields were increased under doubled CO2, with the response relative to ambient CO2 greatest in dry years. Higher temperatures under the climate change scenarios moderated the yield gains achieved with increasing CO2 and in some instances reversed them under the reduced rainfall scenario. The status of the region as a producer of prime hard wheat may be at risk due to reduced grain protein levels under doubled CO2 and the increased likelihood of “heat shock” in the climate scenarios used.
For hydrological or agronomic purposes, the potential rate of transpiration from vegetation is often calculated as a function of climatological variables, sometimes with the inclusion of a canopy resistance to water vapour diffusion. If, within leaves, the intercellular concentration of CO2 is conservative, the canopy resistance must depend on the photosynthesis rate implying that potential transpiration depends on potential growth. The relevant form of the Penman-Monteith equation is developed to link water use efficiency with the conversion coefficient for solar radiation.When water is limiting, the maximum rate at which transpiration can occur depends mainly on the rate of extension of the root system and on the water “available” per unit soil volume. It follows that both potential and subpotential rates of transpiration are consequences of the assimilation of carbon by vegetation and its subsequent redistribution to form shoots and roots.
The cropping systems simulation model APSIM-Nwheat was tested against detailed field measurements representing possible growing conditions under future climate change scenarios. Increasing average temperatures by 1.7 °C observed over several seasons at Obregon, Mexico reduced the time to flowering by 11 days and resulted in a decline of total biomass and grain yield. These effects were reproduced by the model, except when the observed total biomass inexplicably rose again in the fourth and fifth year, despite higher temperature and a much shorter growing time. In a water stress experiment, the effects of different timing and duration of water deficit on crop growth and yield were reproduced with the model for a rain-shelter experiment at Lincoln, New Zealand where observed grain yields were reduced from 10 to 4 t ha−1 due to increased water deficit. In experiments from Western Australia, reduced growth and yields due to extreme terminal water deficit were also reproduced with the model where measured yields fall below 0.5 t ha−1. In the Maricopa Free Air Carbon-Dioxide Enrichment (FACE) experiment in Arizona, USA, the largest yield increase occurred with elevated CO2 in the dry and high N treatments, whereas little or no response was observed in the wet and low N supply treatments, as simulated with the model. Combining elevated CO2 with increased temperature in a sensitivity analysis, two levels of water supply and a range of N applications indicated a positive effect of elevated CO2 on yield as long as N was not limiting growth. Increased temperature and reduced water supply reduced yields and the yield response to N supply under ambient and elevated CO2. Grain protein concentrations were reduced under elevated CO2, but the difference was minor with ample N fertiliser. Evapotranspiration was reduced under elevated CO2. Higher temperatures increased evapotranspiration with low N input, but reduced it with ample N fertiliser, resulting in a reduction and an increase, respectively, in drainage below the root zone. In the Mediterranean environment of Western Australia the impact of elevated CO2 and increased temperature on grain yield was in average positive, but varied with seasonal rainfall distribution. Based on the range of model testing experiments and the sensitivity analysis, APSIM-Nwheat was found suitable for studies on directional impacts of future climate change on wheat production. Due to some large discrepancies between simulated and observed data, field experiments representing only a limited range of possible climate change scenarios and the large possible range of factorial interactions not tested, simulated quantitative effects with the model should be interpreted cautiously.
Climate models indicate that increasing atmospheric concentrations of carbon dioxide and other greenhouse gases could alter climate globally. The EPIC (Erosion/Productivity Impact Calculator) model was used to examine the sensitivity of soil erosion (wind, water) and soil organic carbon (SOC) (15 cm and 1 m depth) across the US corn belt to changes in temperature (+ 2°C), precipitation (±10%, ±20%), wind speed (±10%, ±20%), and atmospheric CO2 concentration (350, 625 ppmv). One-hundred-year simulations were run for each of 100 sites under 36 climate/CO2 regimes. The 100-year regionally aggregated mean water erosion rates increased linearly with precipitation, whereas the wind erosion rates decreased and total erosion rates increased non-linearly. Increasing temperature by 2°C (with CO2 and mean wind speed held constant) decreased water erosion by 3–5%, whereas wind erosion increased by 15–18%. Total erosion increased with increased temperature. Increasing CO2 from 350 to 625 ppmv (with temperature increased by 2°C and mean wind speed held constant) had no effect on water erosion, despite increases in annual total and peak runoff; this was attributed to increased vegetation cover. Wind erosion decreased by 4–11% under increased CO2. Wind erosion was very sensitive to mean wind speed, increasing four-fold and decreasing 10-fold for a 20% increase or decrease in mean wind speed, respectively. This was attributed to a threshold effect. SOC to 1 m decreased 4·8 Mg-C ha−1 from an initial value of 18·1 Mg-C ha−1 during the 100-year baseline simulation. About 50% of this loss (2·3 Mg-C ha−1) was due to transport off-site by soil erosion. SOC in the top 15 cm decreased 0·8 Mg-C ha−1 from an initial value of 4·9 Mg-C ha−1. Increased temperature and precipitation accelerated these losses of SOC, whereas increased CO2 slowed the losses.
Crop simulation models are an essential tool for testing whether predicted global atmospheric changes are likely to have impact on food production. Any confidence in model predictions must be based on their ability successfully to predict performance in experiments. Accordingly, the predictions of three daily time step wheat simulation models (AFRCWHEAT2, FASSET and Sirius) were tested against data from wheat (Triticum aestivum L.) experiments in AZ in which the amount of applied N and the atmospheric CO2 concentration were both varied. Although there were differences between predicted and observed yields, all the three models predicted yield trends with treatments very similar to those observed. They all predicted, both in absolute terms and in the magnitude of responses, very similar effects of the variations on green area index (GAI), shoot and grain biomass accumulation, and shoot and grain biomass yield to observations and to each other. Comparison of simulated and observed results showed that CO2 effects were expressed through effects on light use efficiency (LUE), whereas N effects were expressed by causing variations in GAI. The exercise showed that the models used have potential for assessing climate change impacts on wheat production.
The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.
APSIM (Agricultural Production Systems Simulator) is a software system which provides a flexible structure for the simulation of climatic and soil management effects on growth of crops in farming systems and changes in the soil resource. The focus of this paper is the predictive performance of APSIM for simulation of soil water and nitrate nitrogen in contrasting soils (vertisols and alfisols) and environments. The three APSIM modules that determine the dynamics of water, carbon, and nitrogen in the soil system (viz. SOILWAT, SOILN and RESIDUE v.1) are described in terms of the processes represented, with particular emphasis on aspects of their coding that differ from their precursors in CERES and PERFECT. The most fundamental change is in SOILN, which now provides a formal balance of both carbon and nitrogen in the soil and includes a labile soil organic matter pool that decomposes more rapidly than the bulk of the soil organic matter. Model performance, in terms of prediction of soil water and nitrate, is evaluated during fallows, thereby avoiding complications arising from water use and nitrogen uptake by a crop. One data set is from a long-term experiment on a vertisol in southeast Queensland which studied two tillage treatments (conventional and zero tillage) in combination with fertiliser nitrogen inputs for the growth of wheat; soil water and nitrate were measured twice each year (pre-planting and post-harvest). The second comes from experiments at Katherine, Northern Territory, where legume leys growing on alfisols were chemically killed and ensuing changes in soil water and nitrate were measured during a single season. For both datasets, the predictive ability of the model was satisfactory for water and nitrate, in terms of both the total amounts in the whole profile and their distribution with depth. Since neither of these datasets included measurements of the runoff component of the water balance, this aspect of model performance was evaluated, and shown to be generally good, using data from a third source where runoff had been measured from contour bay catchments.
If, as many climate change analysts speculate, industrial and other emissions of CO2 can be offset by substitution of biofuels, large areas of land, including agricultural land, may be converted to the production of biomass feedstocks. This paper explores the feasibility for the Missouri–Iowa–Nebraska–Kansas (MINK) region of the US of converting some agricultural land to the production of switchgrass (Panicum virgatum L.), a perennial warm season grass, as a biomass energy crop. The erosion productivity impact calculator (EPIC) crop growth model simulated production of corn (Zea mays L.), sorghum (Sorghum bicolor (L.) Moench), soybean (Glycine max L.), winter wheat (Triticum aestivum L.) and switchgrass at 302 sites within the MINK region. The analysis is done for both current climatic conditions and a regional climate model-based scenario of possible climate change. Daily climate records from 1983 to 1993 served as baseline and the NCAR-RegCM2 model (RegCM hereafter) nested within the CSIRO general circulation model (GCM) provided the climate change scenario. Crop production was simulated at two atmospheric CO2 concentrations ([CO2]) at 365 and 560 ppm to consider the CO2-fertilization effect. Simulated yields of the perennial switchgrass increased at all sites with a mean yield increase of 5.0 Mg ha−1 under the RegCM climate change scenario. Switchgrass yields benefited from temperature increases of 3.0–8.0°C, which extended the growing season and reduced the incidence of cold stress. Conversely, the higher temperatures under the RegCM scenario decreased yields of corn, soybean, sorghum and winter wheat due to increased heat stress and a speeding of crop maturity. With no CO2-fertilization effect, EPIC simulated maximum decreases from baseline of 1.5 Mg ha−1 for corn, 1.0 Mg ha−1 for sorghum, 0.8 Mg ha−1 for soybean and 0.5 Mg ha−1 for winter wheat. Simulated yields increased for all crops under the RegCM scenario with CO2 set to 560 ppm. Yields increased above baseline for 34% of the soybean and 37% of the winter wheat farms under RegCM/[CO2] = 560 ppm scenario. Water use increased for all crops under the higher temperatures of the CSIRO scenario. Precipitation increases resulted in greater runoff from the traditional crops but not from switchgrass due to the crop’s increased growth and longer growing season. Simulated soil erosion rates under switchgrass and wheat cultivation were less severe than under corn management. However, simulated erosion under switchgrass was considerable in eastern Iowa during the period of crop establishment because of strong winds at that time.
Agricultural production can be increased through better plant characteristics obtained either through breeding or through better growing conditions, both in the soil and above ground. In the chain of events necessary for plant growth, photosynthesis stands at the beginning as the primary conversion of light energy to chemical energy stored in organic substances. This paper deals with the influence of photosynthetic performance on the eventual dry matter production of plants. The approach used is mechanistic and quantitative, and, because the number of interacting factors is large, a simulation method is used. Our simulation model is essentially BACROS (de Wit et al., 1978), modified to the present (1983) version in a number of ways indicated below. We consider characteristics of C3 plants only.
APSIM Nwheat is a crop system simulation model, consisting of modules that incorporate aspects of soil water, nitrogen (N), crop residues, and crop growth and development. The model was applied to simulate above- and below-ground growth, grain yield, water and N uptake, and soil water and soil N of wheat crops in the Netherlands. Model outputs were compared with detailed measurements of field experiments from three locations with two different soil types. The experiments covered two seasons and a range of N-fertiliser applications. The overall APSIM Nwheat model simulations of soil mineral N, N uptake, shoot growth, phenology, kernels m−2, specific grain weight and grain N were acceptable. Grain yields (dry weight) and grain protein concentrations were well simulated with a root mean square deviation (RMSD) of 0.8 t ha−1 and 1.6 protein%, respectively. Additionally, the model simulations were compared with grain yields from a long-term winter wheat experiment with different N applications, two additional N experiments and regional grain yield records. The model reproduced the general effects of N treatments on yields. Simulations showed a good consistency with the higher yields of the long-term experiment, but overpredicted the lower yields. Simulations and earlier regional yields differed, but they showed uniformity for the last decade.
Costs and Benefits of CO2 Increase and Climate Change on the Australian Wheat Industry. Report to the Australian Greenhouse Office
- S M Howden
- R N Jones
Howden, S.M., Jones, R.N., 2001. Costs and Benefits of CO 2
Increase and Climate Change on the Australian Wheat Industry.
Report to the Australian Greenhouse Office, CSIRO Sustainable
Ecosystems, Canberra, Australia.
Climate Impact Group Design of simulation experiments: an example for assessing potential impact of climate change on wheat yields and deep drainage in Western Australia
- Van Ittersum
- M K Howden
- S M Asseng
CSIRO, 2001. Climate Change Projections for Australia. Climate Impact Group, CSIRO Atmospheric Research, Melbourne, 8 pp., http://www.dar.csiro.au/publications/projections.pdf. De Vries, S.C., Van Ittersum, M.K., Howden, S.M., Asseng, S., 2002. Design of simulation experiments: an example for assessing potential impact of climate change on wheat yields and deep drainage in Western Australia. In: Book of Proceedings VII Congress of the European Society for Agronomy. Córdoba, Spain, 15–18 July 2002, pp. 773–774.
Computer simulation of the effects of cropping rotations and fallow management on solute movement
- J E Turpin
- N I Huth
- B A Keating
- J P Thompson
Turpin, J.E., Huth, N.I., Keating, B.A., Thompson, J.P., 1996.
Computer simulation of the effects of cropping rotations and
fallow management on solute movement. In: Asghar, M. (Ed.),
Proceedings of the 8th Australian Agronomy Conference.
Agronomy: Science with Its Sleeves Rolled Up. Australian
Society of Agronomy Inc., Toowoomba, Qld, pp. 558-561.
- M K Van Ittersum
M.K. van Ittersum et al. / Agriculture, Ecosystems and Environment 97 (2003) 255–273