No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Assessing climatic risk to sorghum production in water-limited subtropical environments I. Development and testing of a simulation model
Abstract
Sorghum (Sorghum bicolor (L.) Moench.) is one of the major summer crops grown in the subtropics. The high rainfall variability and limited planting opportunities in these regions make crop production risky. A robust crop simulation model can assist farmer decision-making via simulation analyses to quantify production risks. Accordingly, we developed a simple, yet mechanistic crop simulation model for sorghum for use in assessing climatic risk to production in water-limited environments. The model simulates grain yield, biomass accumulation, crop leaf area, phenology and soil water balance. The model uses a daily time-step and readily available weather and soil information and assumes no nutrient limitation. The model was tested on numerous data (n=38) from experiments spanning a broad range of environments in the semi-arid tropics and subtropics. Potential limitations in the model were identified and examined in a novel testing procedure by using combinations of predicted and observed data in various modules of the model. The model performed satisfactorily, accounting for 94% and 64% of the variation in total biomass and grain yield, respectively. The difference in outcome for biomass and yield was caused by limitations in predicting harvest index. The concepts involved, and the limitations encountered, developing a crop model to be simple but consistent with the biophysical rigour required for application to such a diverse range of environments, are discussed.
... Cereal crop phenology is described by a number of well-defined development stages, which include (1) germination, (2) emergence, (3) panicle initiation (PI), (4) full flag leaf appearance, (5) anthesis, (6) start grain filling and (7) physiological maturity. Most phases between these stages have their own thermal time target (Muchow and Carberry 1990), with thermal time calculated from daily maximum and minimum temperature via a broken linear function that defines the response to temperature in terms of a base (T b ), optimum (T opt ) and maximum (T m ) temperature (Hammer and Muchow 1994). The T b represents the temperature below which the rate of development is zero, T opt the temperature at which the rate of development is maximum, and T m the temperature above which the rate of development is zero again. ...
... The duration of the phases between the stages of flag leaf, anthesis, and start and end of grain filling are all considered to have thermal time targets (Muchow and Carberry 1990;Hammer and Muchow 1994;Ravi Kumar et al. 2009;Hammer et al. 2010). Time from flag leaf appearance to anthesis is generally quite conserved across genotypes (Ravi Kumar et al. 2009). ...
... Grain fill generally starts around 4 days after flowering and ends at physiological maturity (black layer). Post-anthesis cardinal temperatures for rate of development differ substantially from those before anthesis, and have been identified as 5.7 C and 23.5 C for T b and T opt , respectively (Hammer and Muchow 1994). There is no evidence of genotypic differences for these cardinal temperatures (Ravi Kumar et al. 2009; Tirfessa Woldetensaye 2019). ...
Crop growth is a dynamic process whereby the sorghum plant germinates, emerges and begins to capture solar radiation and, via photosynthesis, accumulate biomass. Interacting with its surrounding environment, the sorghum plant adapts to the various biotic and abiotic challenges on its journey towards flowering and, ultimately, seed production. We will explain the physiology of growth and yield in sorghum using a framework based on crop growth and development. The process of evolution has enabled plants to utilise a variety of timing mechanisms that regulate development, improving the chance that germination and reproduction are aligned with favourable periods of growth. Crop development is predominantly affected by photoperiod and temperature. In contrast, crop growth, which represents the biomass produced, is predominantly affected by incoming radiation. Grain yield can be defined as the product of resource capture (light, water and nitrogen), resource use efficiency and partitioning of that resource into grain. Since water limitation is the key constraint to sorghum yield globally, crop growth will be considered in the context of water-limiting and non-limiting scenarios. In the absence of water limitation, the sorghum crop is largely limited by radiation, and in this scenario, biomass accumulation is the product of intercepted radiation and its conversion efficiency, the radiation use efficiency (RUE, biomass produced per unit of radiation intercepted). When water is a limitation, biomass accumulation under drought stress becomes a function of the total amount of water used by a crop (transpiration, T) and the transpiration efficiency (TE, biomass produced per unit of water transpired). For the first time in history, we now have the tools to measure physiological traits, such as dynamic biomass growth or canopy radiation use efficiency at a high-throughput scale that can match the genomic data. These new tools will allow us to phenotype thousands of lines that breeders have previously genotyped in multi-location field trials, a pre-requisite for the unravelling of the molecular basis of complex traits via association mapping approaches. This is particularly pertinent in sorghum due to its importance as a cereal for food, feed and fuel, especially in dry-land cropping systems.
... Its roots reside in the innovative activities of the research groups in Texas and Kansas in the 1970s (Arkin et al., 1976;Vanderlip and Arkin, 1977) that generated the SORGF and SORKAM models (Rosenthal et al., 1989). It was later that simpler mechanistic sorghum models emerged (Hammer and Muchow, 1991;Hammer and Muchow, 1994;Sinclair et al., 1997) in Australia and the United States, and this coincided with other attempts to modify the Ceres maize model for sorghum in Australia (Birch et al., 1990) and elsewhere (Ritchie and Alagarswamy, 1989;White et al., 2015). The novel modeling approach based on concepts of (light and water) resource capture was also developed for sorghum at ICRISAT in India at about this time (Monteith et al., 1989). ...
... Arbitration rules and organ level responses are invoked when resource capture cannot satisfy demand. The APSIM-sorghum model retains some features and concepts of earlier models (Sinclair, 1986;Rosenthal et al., 1989;Birch et al., 1990;Sinclair and Amir, 1992;Chapman et al., 1993;Hammer and Muchow, 1994), but has been adapted and redesigned to generate a more explanatory approach to the modeling of the underlying physiology . APSIM-sorghum operates via the dynamic interaction of crop development, crop growth, and crop nitrogen with soil and weather attributes (Fig. 1). ...
... Phenology is simulated through a number of development stages by using a thermal time approach Hammer and Muchow, 1994), with the temperature response characterized by a base (T b ), optimum (T opt ), and maximum (T m ) temperature. Hammer et al. (1993) and Carberry et al. (1993) reported values of T b , T opt , and T m for sorghum of 11, 32, and 42°C, respectively. ...
... However, larger differences in T base have been observed for crops like rice (Oryza sativa L.; Dingkuhn & Miezan, 1995) and soybean [Glycine max (L.) Merr.; Roberts et al., 1996]. Moreover, post-anthesis cardinal temperatures for rate of development of sorghum differ substantially from those before anthesis and have been identified as 5.7 and 23.5 • C for T base and T opt , respectively (Hammer & Muchow, 1994). Hence, it is likely that a larger range in the response of pre-anthesis development to temperature is present in sorghum germplasm. ...
... The date of full flag leaf expansion of each plant was estimated as the date that the ligule of the flag leaf became Crop Science visible above the ligule of the previous leaf. Anthesis was recorded when 50% of the anthers of a main shoot were visible, and physiological maturity was recorded when seeds at the base of the main shoot panicle showed a black layer (Hammer & Muchow, 1994). No data on phenological stages were recorded for the sixth sowing at Melkassa in 2013, leaving a total of 23 experiments (location × year × sowing date combinations) for analyses. ...
... A similar approach was used to determine cardinal temperatures for the development rate during the period from anthesis to physiological maturity, employing a previously defined thermal time model that used default coefficients of 5.7 and 23.5 • C for T base and T opt , respectively, as starting values (Hammer & Muchow, 1994). In this case, the model had no T max . ...
Sorghum [Sorghum bicolor (L.) Moench] is an important dryland crop in the semiarid tropics, and temperature and photoperiod are the main environmental factors affecting its phenology and thus adaptation. The objectives of this study were to quantify the response of development rate to temperature and photoperiod for 19 diverse Ethiopian sorghum genotypes, and to determine if differences in these responses could be linked to racial grouping or agroecological adaptation. The genotypes, representing four major sorghum races and adaptation to four agroecological zones, were sown on 12 dates at two locations in Ethiopia with contrasting altitude. This created a range in photoperiod and temperatures relevant to Ethiopian conditions. Days from emergence to flag leaf appearance, anthesis, and maturity were recorded. A predictive phenology modeling framework was used to fit the effects of photoperiod and temperature on the rate of development for both the pre‐ and post‐anthesis periods. Results indicated that the pre‐anthesis development rate was independent of photoperiod for the range tested. This result differed from West African germplasm and likely reflects differences in agroecological adaptation and racial background. Significant genotypic differences were observed for the base temperature (0–9.8 °C) and for the optimum rate of development (0.011–0.022 d–1, with low value indicating late anthesis), with differences related to agroecology and racial type. Post‐anthesis differences in the temperature response were minor. The observed differences in pre‐anthesis base temperature can positively affect sorghum breeding programs globally, especially in temperate regions where suitability for early spring plantings is often restricted by low temperatures.
... Hammer and Broad (2003) in sorghum found that dHI/dt was reduced considerably under cool conditions. Previously, Hammer and Muchow (1994) had shown that the lower precision in predicting grain yield in their sorghum model was associated with limitations in predicting HI. ...
... Owing to the lack of the data to quantify this effect, it is modeled by stopping the linear increase in HI if FTSW is below 0.1 and HI exceeds 0.2 as outlined by Chapman et al. (1993), Hammer et al. (1995) and Hammer and Muchow (1994). The minimum value for HI of 0.2 represents the maximum remobilization of stored assimilates. ...
The linearity of harvest index (HI) increase has been used as a simple means to analyze and predict crop yield in experimental and simulation studies. It has been shown that this approach may introduce significant error in grain yield predictions when applied to diverse environments. This error has been ascribed to variability in the rate of linear increase in HI with time (dHI/dt). Data from two field experiments indicated in chickpea (Cicer arietinum L.) that dHI/dt varied among sowing dates. This variation was related to the length of pre-seed growth phase and vegetative growth (dry matter production) during this phase and could be described by the mean daily temperature from sowing to beginning seed growth. dHI/dt increased linearly with increase in the temperature up to 17 8C when it reached to its maximum value and remained constant. Simulation of these field experiments using a chickpea crop model including a constant dHI/dt resulted in yield over-prediction for some sowing dates. However, a modified HI-based approach greatly improved model predictions. In this approach, potential seed growth rate (SGR) is calculated using the linear HI concept, but actual SGR is limited to current biomass production and the remobilisation of dry matter accumulated in vegetative organs before the seed growth period. This modification well accounted for temperature and drought effects on HI and resulted in better yield predictions under conditions of major chickpea producing areas of Iran. Therefore, we recommend that the modification to be applied in the other HI-based models. #
... The duration of most phases between these seven development stages is determined by a thermal time target, whereas photoperiod effects predominantly operate during the juvenile phase prior to PI, when increased photoperiod can increase the number of initiated leaves, which in turn can delay the timing of full flag leaf appearance (Muchow and Carberry, 1990). Thermal time is calculated from daily maximum and minimum temperatures through a broken linear function that defines the response to temperature in terms of three cardinal temperatures: a base temperature (T b ) and maximum temperature (T m ) below and above which the rate of development is zero and an optimum temperature (T opt ) at which the rate of development is maximum (Hammer and Muchow, 1994). Cardinal temperatures before anthesis of 11°C, 30°C, and 42°C for T b , T opt , and T m , respectively, have been reported (Alagarswamy et al., 1986;Hammer et al., 1993;Ravi Kumar et al., 2009), although significant genotypic differences in T b for this period have been observed (Craufurd et al., 1999;Tirfessa et al., 2020). ...
... Similarly, a black layer in the placental part of the kernel marks physiological maturity in maize (Chapter 1: Maize, Section 2.1). Postanthesis cardinal temperatures for rate of development have been identified as 5.7°C and 23.5°C for T b and T opt , respectively (Hammer and Muchow, 1994) and thus differ substantially from those before anthesis. There is little evidence of genotypic differences in these cardinal temperatures (Ravi Kumar et al., 2009;Tirfessa et al., 2020). ...
... The duration of most phases between these seven development stages is determined by a thermal time target, whereas photoperiod effects predominantly operate during the juvenile phase prior to PI, when increased photoperiod can increase the number of initiated leaves, which in turn can delay the timing of full flag leaf appearance (Muchow and Carberry, 1990). Thermal time is calculated from daily maximum and minimum temperatures via a broken linear function that defines the response to temperature in terms of three cardinal temperatures: a base temperature (Tb) and maximum temperature (Tm) below and above which the rate of development is zero, and an optimum temperature (Topt) at which the rate of development is maximum (Hammer and Muchow, 1994). Cardinal temperatures before anthesis of 11, 30, and 42 o C for T b , T opt , and Tm, respectively, have been reported (Alagarswamy et al., 1986;Hammer et al., 1993;Ravi Kumar et al., 2009), although significant genotypic differences in Tb for this period have been observed (Craufurd et al., 1999;Tirfessa et al., 2020). ...
... Similarly, a black layer in the placental part of the kernel marks physiological maturity in maize (Chapter Maize, section 2.1). Post-anthesis cardinal temperatures for rate of development have been identified as 5.7 °C and 23.5°C for Tb and Topt, respectively (Hammer and Muchow, 1994) and thus differ substantially from those before anthesis. There is little evidence of genotypic differences in these cardinal temperatures (Ravi Kumar et al., 2009;Tirfessa et al., 2020). ...
Sorghum is primarily grown in hot and dry regions worldwide in large-scale commercial operations and in smallholder farming settings. In these regions, sorghum shows comparative advantages over other summer cereals, including its capacity to fill grain during end-of-season drought. It is also broadly adapted to temperate, subtropical and tropical drylands, and irrigated environments. Worldwide, farmers face the challenge of growing sorghum in highly variable environments. Matching hybrid, agronomy, and environment enables farmers to design more profitable and less risky sorghum production systems. At the crop level, the growth of major organs is predicted based on their potential size, and then the capacity of the crop to capture resources (radiation, water, and nitrogen) to meet the demand is assessed. The extent of resource capture and biomass accumulation and the efficiency with which carbon and nitrogen are partitioned into grain will be discussed, with a focus on productivity under water scarcity. One of the challenges for future sorghum crop improvement will be the capacity to connect across scales from molecular to farm, utilising molecular understanding to develop combinations of plant-level traits that increase productivity at the field level. This will require cross-disciplinary integration to harness ‘big’ data from genotypic and phenotypic studies. Future research in sorghum will need to focus on adaptation mechanisms that manipulate water supply and demand scenarios such as canopy size and root architecture, respectively, and heat adaptation mechanisms. Considerable biological integration will be required to scale-up from the causal polymorphisms at genome level to the phenotype of interest in the field. A new crop modelling frontier is unfolding in plant breeding with the potential to add significant value to the revolution in plant breeding associated with genomic technologies.
... Important role of photosynthesis during light phase of a day in plant growth is beyond doubt. Response of light and dark phase gas exchange [day-time photosynthesis (CO 2 assimilation) and night-time respiration (CO 2 release), respectively] revealed that diurnal temperature amplitude induced night respiration during dark phase of a day has important role in carbon tradeoff (photosynthesis verses night respiration) which is one of the important factor determines the magnitude of growth along with other leaf traits and light interception (Hammer and Muchow, 1994;Ravi Kumar et al., 2009;Sunoj et al., 2016;Impa et al, 2018). Our study on C 4 cereal crop maize [hybrid maize (DKC 47-27RIB, DEKALB, USA); Sunoj et al., 2016] systematically demonstrated the impact of different diurnal temperature amplitudes (2, 8, and 10°C) with optimum (30°C) and high (35°C) mean temperatures on vegetative growth. ...
... At the same time, dark phase in plants are equally important as light phase not only due to contribution of night respiration but also because of its effect on synchronization of circadian clock to manage the redox state of photosystems, response of hormones and induction or control of different mechanisms which are collectively responsible for sustainable growth under optimum and tolerance under stress conditions (McClung, 2006;Sunoj et al., 2016;Srivastava et al., 2019). Studies in sorghum have also shown a strong influence of temperature on leaf initiation, appearance, and expansion rates, all of which are important for development of plant leaf area and hence, light interception (LI) and biomass accumulation (Hammer and Muchow, 1994;Ravi Kumar et al., 2012). In the current experiment, overall, there was positive correlation of total leaf area with total biomass and carbohydrates (Figure 7). ...
Effect of diurnal temperature amplitude on carbon tradeoff (photosynthesis vs. respiration) and growth are not well documented in C4 crops, especially under changing temperatures of light (daytime) and dark (nighttime) phases in 24 h of a day. Fluctuations in daytime and nighttime temperatures due to climate change narrows diurnal temperature amplitude which can alter circadian rhythms in plant, thus influence the ability of plants to cope with temperature changes and cause contradictory responses in carbon tradeoff, particularly in night respiration during dark phase, and growth. Sorghum [Sorghum bicolor (L.) Moench] is a key C4 cereal crop grown in high temperature challenging agro-climatic regions. Hence, it is important to understand its response to diurnal temperature amplitude. This is the first systematic investigation using controlled environmental facility to monitor the response of sorghum to different diurnal temperature amplitudes with same mean temperature. Two sorghum hybrids (DK 53 and DK 28E) were grown under optimum (27°C) and high (35°C) mean temperatures with three different diurnal temperature amplitudes (2, 10, and 18°C) accomplished by modulating daytime and nighttime temperatures [optimum daytime and nighttime temperatures (ODNT): 28/26, 32/22, and 36/18°C and high daytime and nighttime temperatures (HDNT): 36/34, 40/30, and 44/26°C]. After exposure to different temperature conditions, total soluble sugars, starch, total leaf area and biomass were reduced, while night respiration and specific leaf area were increased with narrowing of diurnal temperature amplitude (18 to 2°C) of HDNT followed by ODNT. However, there was no influence on photosynthesis across different ODNT and HDNT. Contradiction in response of foliar gas exchange and growth suggests higher contribution of night respiration for maintenance rather than growth with narrowing of diurnal temperature amplitude of ODNT and HDNT. Results imply that diurnal temperature amplitude has immense impact on the carbon tradeoff and growth, regardless of hybrid variation. Hence, diurnal temperature amplitude and night respiration should be considered while quantifying response and screening for high temperature tolerance in sorghum genotypes and comprehensive understanding of dark phase mechanisms which are coupled with stress response can further strengthen screening procedures.
... The APSIM cropping systems model was developed to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practices in the face of climatic risk. APSIM's sorghum module is based on the fusion of earlier models and concepts (Rosenthal et al., 1989;Sinclair and Amir, 1992;Chapman et al., 1993;Hammer and Muchow, 1994). It simulates complex adaptive traits and genotype-to-phenotype prediction (Hammer, 2010). ...
... It simulates complex adaptive traits and genotype-to-phenotype prediction (Hammer, 2010). Crop development follows a thermal time approach (Muchow and Carberry, 1990;Hammer and Muchow, 1994), with reported base (T b ), optimal (T opt ) and maximum (T m ) temperatures of 11, 32, and 42 0 C, respectively (Carberry et al., 1993a, b). The thermal time target for the phase between emergence and panicle initiation is also a function of day length (Hammer et al., 1989;Kumar Ravi et al., 2009) and its duration, when divided by the plastochron ( 0 C degrees per leaf), determines total leaf number. ...
Climate variability and change will have far reaching consequences for smallholder farmers in sub-Saharan Africa, the majority of whom depend on agriculture for their livelihoods. Crop modelling can help inform the improvement of agricultural productivity under future climate. This study applies the Agricultural Production Systems sIMulator (APSIM) to assessing the impacts of projected climate change on two (early and medium maturing) sorghum varieties under different management practices. Results show high model accuracy with excellent agreement between simulated and observed values for crop phenology and leaf number per plant. The prediction of grain yield and total biomass of an early maturing variety was fair RMSEn (22.9 and 23.1%), while that of the medium maturing was highly accurate RMSEn (14.9 and 11.9%). Sensitivity analysis performed by changing the calibrated variables of key plant traits in the model, showed higher significant yield change by + or - 10 % changed in radiation use efficiency, (RUE), coefficient extinction (Coeff_ext) and Phyllocron (Phyllo) for early maturing variety while + or - 10 % changed in phyllochron and RUE showed a significant yield change for the medium maturing variety. Under climate change scenerios using RCP 8.5, the simulated yield for the early–maturing variety revealed high inter-annual variability and potential yield loss of 3.3% at Bamako and 1% at Kano in the near-future (2010–2039) compared to baseline (1980–2009). The mid-century (2040–2069) projected yield decline by 4.8% at Bamako and 6.2% at Kano compared to baseline (1980–2009). On the contrary, the medium maturing variety indicated significantly yield gain with high yielding potential in both climate regimes compared to the baseline period (1980–2009). The simulated grain yield increased by 7.2% at Bamako and 4.6% at Kano, in the near-future (2010–2039) while in the mid-century (2040–2069) projected yield increase of 12.3% and 2% at Bamako and Kano compared to baseline (1980–2009). Adaptation strategies under climate change for varying sowing dates in the near-future (2010–2039, indicated that June sowing had a higher positive yield gained over July and August sowing for early maturing variety; July sowing simulated positive gained by 5 -11% over June and August sowing for medium maturing variety in both locations. Similarly, under the mid-century (2040–2069), among the sowing dates and in both locations, June sowing indicates lowest negative yield change over July and August sowing for early maturing variety. However, for medium maturing variety, July sowing had the highest yield gain of 16% over June and August sowing at Bamako and June highest positive yield gained of 11.4% over July and August at Kano. Our study has, therefore, demonstrated the capacity of APSIM model as a tool for testing management, plant traits practices and adoption of improved variety for enhancing the adaptive capacity of smallholder farmers under climate change in the Sudanian zone of West Africa. This approach offers a promising option to design more resilient and productive farming systems for West Africa using the diverse sorghum germplasm available in the region.
... where WEATHER% CO 2 is the actual atmospheric CO 2 concentration in ppm. Hammer and Muchow [49] used a transpiration efficiency coefficient of 0.009 kPa for their sorghum simulation. As Hammer and Muchow [49] based their calculations only on aboveground biomass, their transpiration efficiency coefficient was compatible with the DSSAT-NWheat approach for modeling transpiration [20]. ...
... Hammer and Muchow [49] used a transpiration efficiency coefficient of 0.009 kPa for their sorghum simulation. As Hammer and Muchow [49] based their calculations only on aboveground biomass, their transpiration efficiency coefficient was compatible with the DSSAT-NWheat approach for modeling transpiration [20]. Pembleton et al. [50] used the APSIM-Wheat model as a basis for estimating the functions for modifying transpiration efficiency for various forage crops. ...
Tef is an Ethiopian staple grain that provides both food security and income for smallholders. As tef is nutritious and gluten free, it is also gaining popularity as a health food. A tef model was calibrated based on the Decision Support System for Agrotechnology Transfer’s (DSSAT) NWheat model and included parameter changes in phenology, photoperiod response, radiation use efficiency, and transpiration efficiency for both standard and elevated atmospheric CO2, based on published literature for tef and other C4 species. The new DSSAT-Tef model was compared with tef field experiments. DSSAT-Tef accurately simulated phenology and responded to changes in N supply and irrigation, but overestimated growth and occasionally yields. Simulation-observation comparisons resulted in an RMSE of 2.5 days for anthesis, 4.4 days for maturity, 2624 kg/ha (49.6%) for biomass, and 475 kg/ha (41.0%) for grain yield. Less data were available for N uptake, and the model simulated crop N uptake with an RMSE of 45 kg N/ha (46.2%) and 15 kg N/ha (37.3%) for grain N. While more data from contrasting environments are needed for further model testing, DSSAT-Tef can be used to assess the performance of crop management strategies, the suitability of tef for cultivation across growing environments, and food security.
... Interestingly, the exposure to cooler temperature during vegetative growth was associated with an underprediction of yield for rice and sorghum; however, these temperatures would not be considered extreme. Both the rice (Li et al., 2017) and sorghum (Hammer & Muchow, 1994) models have a different model structure and approaches compared to the maize (Carberry et al., 1989), soybean, and mungbean (Robertson et al., 2002) models. Recent efforts by the APSIM initiative (www.APSIM.info) to update the APSIM framework and standardize the crop model development process called APSIM next generation (D. ...
Extreme weather (high rainfall and temperatures) and challenging soils are sources of uncertainties in the use of current crop models that have been developed for more favorable environments. This may limit their applicability to guide and support decision making for the development of new agricultural regions in tropical environments. We evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) framework in representing yield and development of a range of crops across multiple locations in the Northern Territory of Australia, a tropical region with large potential for agricultural development. Observations of yield, biomass, and phenology for a range of crops from 28 experiments undertaken at three locations were compiled and used to develop simulations undertaken using APSIM version 7.10. Model performance varied with coefficients of determination and concordance correlation coefficients ranging from 0.36 to 0.98 and 0.37 to 0.93, respectively. Instances where model performance was less than ideal were associated with conditions presenting a limited number of observed values. Deviations by the model from yield observations were larger for situations with high‐yielding crops and low daily maximum temperatures during vegetative growth stages. Deviations in phenology were larger for conditions associated with water and N stress. APSIM was capable of representing the yield, biomass, and development of cereal and pulse crops and can be used with confidence to assist the expansion of agriculture in tropical environments such as the Northern Territory of Australia.
... Clifford et al. (2000) tested the effects of elevated CO2, drought and temperature on the water relations and gas exchange of groundnut. Hammer and Muchow (1994) used the modelling approach to quantify climatic risk to sorghum in Australia's semi-arid tropics and subtropics. The EPIC, ALMANAC, CROPSYST, WOFOST, ADEL and CERES-Maize models are being successfully used to simulate maize crop growth and yield. ...
... Clifford et al. (2000) tested the effects of elevated CO2, drought and temperature on the water relations and gas exchange of groundnut. Hammer and Muchow (1994) used the modelling approach to quantify climatic risk to sorghum in Australia's semi-arid tropics and subtropics. The EPIC, ALMANAC, CROPSYST, WOFOST, ADEL and CERES-Maize models are being successfully used to simulate maize crop growth and yield. ...
... Drawing upon existing research results, this article classifies sweet sorghum into three growth stages and determines the climate suitability index threshold values for each stage [66,67] (Table 4). ...
In order to effectively address the issue of severe soil salinization in the coastal area of the Yellow River Delta, which has led to a significant number of medium and low-yield fields in this region, and to satisfy the rising demand for feed grain in China in recent years, a highly effective solution is to replace conventional crops by cultivating a specialized type of forage grass that can withstand high salinity levels and is well adapted to the local climate. This study proposed a spatial layout scheme for planting salt-tolerant forages, with the aim of providing a foundation for enhancing saline-alkali land and increasing resource utilization efficiency. The results showed that the climate conditions in the Yellow River Delta were suitable for planting sweet sorghum. With respect to soil salt content, the suitable planting regions for sweet sorghum can be classified into four categories: Suitable, moderately suitable, less suitable, and unsuitable, with soil salt concentrations of 2.62–5.25‰, 5.25–7.88‰, respectively. Concerning economic benefits, sweet sorghum’s input-output ratio (74.4%) surpasses that of cotton in high saline-alkali zones, providing a significant advantage in comparison with traditional crops. In non-saline-alkali and light saline-alkali areas, the traditional winter wheat-summer maize planting system offers higher economic benefits and nitrogen use efficiency, so it is recommended to maintain this system as the dominant agricultural model. In moderately and severe saline-alkali zones, although one-season maize exhibits greater nitrogen efficiency, its economic benefits are lower than those of sweet sorghum. Hence, it is advisable to promote one-season maize in suitable regions and introduce salt-tolerant forage, such as sweet sorghum in other areas. This approach offers novel ideas and methods for crop spatial layout planning and addresses potential feed grain shortages in the region.
... Un enfoque diferente al uso de relaciones que estiman directamente el valor del índice de cosecha final (Sadras and Connor, 1991;Kemanian et al., 2007) es un procedimiento basado en la construcción del HI, que va progresivamente aumentado con el tiempo (Moot et al., 1996;Bindi et al., 1999) desde un periodo que abarca desde la etapa fenológica de floración hasta madurez fisiológica, momento en el que HI alcanza el valor objetivo establecido. Sin embargo, es difícil establecer un ritmo de incremento de HI (Hammer and Muchow, 1994), así como determinar cuál es el valor de HI objetivo. ...
La presente Tesis Doctoral se centra en el desarrollo y evaluación de una metodología operativa que permita la modelización del rendimiento final en los cultivos de grano desarrollados en parcelas comerciales bajo una amplia variedad de condiciones ambientales, y de manejo de agua y nutrientes. Dado que unos de los principales factores que limitan las producciones de los cultivos a nivel mundial es la escasez de agua, existe un especial interés en evaluar dicha metodología en cultivos que se desarrollan en condiciones de déficit hídrico.
Para ello, se propone el desarrollo y evaluación del modelo MYRS (Mapping Yield Remote Sensing-based) basado en la integración de los datos meteorológicos y las medidas de reflectividad de la cubierta vegetal derivadas de las imágenes de satélite en las bases de los modelos de crecimiento de cultivo (CGMs) para la estimación de cada una de las componentes, biomasa seca total acumulada (B) e índice de cosecha (HI), que conforman el rendimiento final (Y) de los cultivos, y además, incluyendo el impacto del estrés hídrico en cada una de estas componentes.
Un aspecto innovador del modelo propuesto es la inclusión de una metodología operativa basada en medidas de teledetección para la modelización del índice de cosecha (HI) en cultivos de trigo desarrollados en parcelas comerciales gestionadas en una amplia variedad de manejos de agua y nutrientes.
Los resultados obtenidos revelan el desempeño del modelo MYRS para modelizar con precisión el rendimiento final y sus componentes (biomasa total acumulada e índice de cosecha) en cultivos desarrollados en distintos ambientes y sometidos a distintos grados de estrés hídrico.
Aunque las series temporales de imágenes multiespectrales de satélite son capaces de recoger parcialmente el efecto del estrés hídrico sobre la cubierta vegetal, como la reducción en el ritmo de crecimiento de la cubierta y/o aceleración de la senescencia, el impacto del estrés hídrico que reduce el ritmo de transpiración requiere de la estimación del coeficiente de estrés hídrico (Ksw),que es modelado a través del balance diario de agua en el suelo explorado por las raíces siguiendo la metodología descrita en FAO-56 (Allen et al., 1998) y asistido por teledetección. Los resultados muestran que la inclusión de Ksw en la estimación de las variables acumuladas (APAR, T, Kt·Kst) es indispensable para conseguir modelar con precisión la acumulación de biomasa y el índice de cosecha en aquellos cultivos sometidos a condiciones de déficit hídrico.
Los resultados conseguidos en la modelización de la distribución espacial del rendimiento final de los cultivos y sus componentes, indican la capacidad del modelo MYRS para reproducir con precisión la variabilidad intraparcelaria tanto en cultivos sometidos a estrés hídrico como manejados en condiciones óptimas de agua. Además, se abre la posibilidad de elaborar mapas de distribución espacial del rendimiento final de manera operativa, que sirven para la caracterización del potencial productivo de la parcela, y que constituyen una herramienta indispensable para la implementación de una agricultura de precisión que permita un manejo diferencial.
Por otro lado, la aplicación del modelo MYRS en extensas áreas, permite comparar la evolución de los cultivos en diferentes campañas a escala comarcal y cuantificar los posibles daños en la producción final producidos por el efecto de una sequía prolongada.
A partir de los resultados y análisis realizados se concluye que el modelo propuesto MYRS (Mapping Yield Remote Sensing-based) es una herramienta operativa valiosa y validada que permite la modelización del rendimiento final en cultivos de grano desarrollados en parcelas comerciales bajo un amplio rango de condiciones ambientales, y para diferentes manejos de agua y grados de estrés hídrico. Además, el modelo propuesto presenta una aplicabilidad que abarca desde la escala intraparcelaria, lo que posibilita el estudio de la distribución espacial del potencial productivo de la parcela, así como a escala de parcela y para extensas áreas, lo que permite la monitorización de la evolución del cultivo a escala de comarca y la posibilidad de cuantificar los daños producidos en el rendimiento final debido al impacto de la sequía.
... Phenology parameterization was conducted using the observed number of expanded leaves throughout the crop growth life cycle, thermal time to anthesis, and thermal time to physiological maturity. Leaf area measurements were used to fit sigmoid curves for total leaf area per plant as a function of thermal time from emergence (Hammer et al., 1993;Hammer and Muchow, 1994) for each genotype. Dry mass accumulation, grain number, grain size, and grain yield data from the field studies were used to derive estimates for each genotype of the coefficient, relating grain number to biomass (Rosenthal et al., 1989;Heiniger et al., 1997). ...
Environmental characterization for defining the target population of environments (TPE) is critical to improve the efficiency of breeding programs in crops, such as sorghum (Sorghum bicolor L.). The aim of this study was to characterize the spatial and temporal variation for a TPE for sorghum within the United States. APSIM-sorghum, included in the Agricultural Production Systems sIMulator software platform, was used to quantify water-deficit and heat patterns for 15 sites in the sorghum belt. Historical weather data (∼35 years) was used to identify water (WSP) and heat (HSP) stress patterns to develop water–heat clusters. Four WSPs were identified with large differences in the timing of onset, intensity, and duration of the stress. In the western region of Kansas, Oklahoma, and Texas, the most frequent WSP (∼35%) was stress during grain filling with late recovery. For northeast Kansas, WSP frequencies were more evenly distributed, suggesting large temporal variation. Three HSPs were defined, with the low HSP being most frequent (∼68%). Field data from Kansas State University sorghum hybrid yield performance trials (2006–2013 period, 6 hybrids, 10 sites, 46 site × year combinations) were classified into the previously defined WSP and HSP clusters. As the intensity of the environmental stress increased, there was a clear reduction on grain yield. Both simulated and observed yield data showed similar yield trends when the level of heat or water stressed increased. Field yield data clearly separated contrasting clusters for both water and heat patterns (with vs. without stress). Thus, the patterns were regrouped into four categories, which account for the observed genotype by environment interaction (GxE) and can be applied in a breeding program. A better definition of TPE to improve predictability of GxE could accelerate genetic gains and help bridge the gap between breeders, agronomists, and farmers.
... SSM has been used to examine yield potential and production risks for a range of crop species including spring wheat (Triticum aestivum L.) (Amir & Sinclair, 1991), maize (Zea mays L.) (Muchow & Sinclair, 1991), sorghum (Sorghum bicolor L. moench) (Hammer & Muchow, 1994), and grain legumes such as cowpea (Vigna unguiculata (L.) Walp.) and black-gram (Vigna mungo (L.) Hepper) (Sinclair et al., 1987), peanut (Arachis hypogaea) (Hammer et al., 1995), chickpea (Cicer arietinum L.) (Soltani et al., 1999), and lentil (Lens culinaris, L.) (Ghanem et al., 2015). SSM has now also been parameterized for faba bean and shown to be robust in simulating crop development, growth, and yield (Marrou et al., 2021). ...
Faba bean (Vicia faba L.) is a useful grain legume for production in Mediterranean climates due to its consumption as food for humans and feed for animals, and its ability to symbiotically fix atmospheric nitrogen. Currently, in Morocco a substantial fraction of faba bean is sown under a rainfed management scheme in which the crop is sown after about a 15-day delay following the first rains after the dry season. The 15-day delay allows weed seeds to germinate and be killed during land tillage prior to sowing of faba bean. However, the 15-day delay shortens the growing season and may negatively impact seed yield. Two alternate sowing date criteria were simulated for faba bean sowing date in Morocco as approaches to increase production. In addition to the 15-day delay management by farmers, sowing was simulated to occur immediately following accumulation of 10 mm or 25 mm of water in the soil. A geospatial analysis was undertaken using the SSM-faba bean model to simulate production on a 1° × 1° grid across Morocco. Eighty three locations were each simulated for 30 growing seasons of weather input. The simulation results for the 25-mm sowing date criteria resulted in decreased geographical area in which faba bean could be grown while the 10-mm sowing date criteria resulted in an expanded geographical area for faba bean production. The average yield based only on seasons in which sowing was achieved, was fairly stable among the sowing-date criteria. The probability of yield increase of the 10-mm sowing date criterion as compared to the 15-day delay sowing was greater than 50% in much of the area found suitable for faba bean production. Assuming an acceptable method for weed control for the 10-mm sowing date criterion, this alternate management could expand faba bean production in Morocco as compared to the current practice of a 15-delay in sowing date.
... En ce qui concerne la conceptualisation du modèle, deux grands types d'approches sont possibles : une approche « ascendante » où l'on part du problème à traiter pour construire un modèle, et une approche « descendante » où l'on part du modèle le plus complet possible et où l'on évalue s'il s'applique au problème à traiter (Hammer and Muchow, 1994;Legay, 1996). Située typiquement dans le cadre de l'approche ascendante, la méthode itérative de diagnostic et de modélisation décrite ci-dessus constitue mon apport à un ensemble de méthodes d'analyses multi-variées du système à modéliser pour établir la hiérarchie des variables et processus à prendre en compte. ...
... Dans la même quête d'un équilibre entre les "erreurs de structure" et les "erreurs de paramètres", Monteith (1996) prêche pour que les efforts des modélisateurs portent sur la suppression de composants lorsqu'ils entraînent davantage de bruit que de précision dans la variable simulée. Sinclair et Seligman (1996) Dans l'idée de garantir la pertinence d'un modèle pour l'application qu'on souhaite en faire tout en réduisant les tâches de modélisation au minimum, enfin, Hammer et al (1989), Shorter et al (1991), et Hammer et Muchow (1994) ont proposé une méthode de construction de modèle "pilotée par les besoins" ou "top-down" tenant compte de la hiérarchie entre les variables nécessaires pour prédire les variables désirées en sortie du modèle. Cependant, ces auteurs n'indiquent pas comment est obtenue cette hiérarchie entre variables impliquées dans le déterminisme des variables à simuler. ...
... The duration of growth stages such as flag leaf to anthesis, anthesis to start of grain filling and start to end of grain filling are also simulated in the model by accumulation of thermal time to reach genotype-specific target values (Muchow and Carberry 1990;Hammer and Muchow 1994;Ravi Kumar et al. 2009 Canopy development is simulated based on the relationship between total plant leaf area (TPLA) and thermal time. TPLA accounts for the number of fully expanded leaves, size of each leaf, and tiller number (Hammer et al. , 2010. ...
Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programs to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (1) augment field phenotyping data and derive hard-to-measure phenotypes and (2) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote sensing and ground reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote sensing measurements combined with manual phenotyping in West Lafayette, Indiana in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, Texas between 2001 and 2018. These trials demonstrated that (1) maximum plant height, final dry biomass, and radiation use efficiency (RUE) of photoperiod sensitive and insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (2) photoperiod sensitive sorghum hybrids exhibited greater biomass production in longer growing environments, and (3) the parameterized and validated models perform well in above ground biomass simulations across years and locations. Crop growth models that integrate remote sensing data offer an efficient approach to parameterise larger plant breeding populations.
... Following the data used in this calibration HI 0 = 0.50. This value is comparable to those obtained by other studies on wheat, such as Toumi et al. [66] in Morocco (HI 0 = 0.46), Moriondo et al. [88] in South America (HI 0 = 0.48), Jin et al. [89] in the northern plain of China (HI 0 = 0.46) and Hammer and Muchow [90] in northern Europe (HI 0 = 0.55). ...
In this study, a simple model, based on a light-use-efficiency model, was developed in order to estimate growth and yield of the irrigated winter wheat under semi-arid conditions. The originality of the proposed method consists in (1) the modifying of the expression of the conversion coefficient (εconv) by integrating an appropriate stress threshold (ksconv) for triggering irrigation, (2) the substitution of the product of the two maximum coefficients of interception (εimax) and conversion (εconv_max) by a single parameter εmax, (3) the modeling of εmax as a function of the Cumulative Growing Degree Days (CGDD) since sowing date, and (4) the dynamic expression of the harvest index (HI) as a function of the CGDD and the final harvest index (HI0) depending on the maximum value of the Normalized Difference Vegetation Index (NDVI). The calibration and validation of the proposed model were performed based on the observations of wheat dry matter (DM) and grain yield (GY) which were collected on the R3 irrigated district of the Haouz plain (center of Morocco), during three agricultural seasons. Further, the outputs of the simple model were also evaluated against the AquaCrop model estimates. The model calibration allowed the parameterization of εmax in four periods according to the wheat phenological stages. By contrast, a linear evolution was sufficient to represent the relationship between HI and CGDD. For the model validation, the obtained results showed a good agreement between the estimated and observed values with a Root Mean Square Error (RMSE) of about 1.07 and 0.57 t/ha for DM and GY, respectively. These correspond to a relative RMSE of about 19% for DM and 20% for GY. Likewise, although of its simplicity, the accuracy of the proposed model seems to be comparable to that of the AquaCrop model. For GY, R2, and RMSE values were respectively 0.71 and 0.44 t/ha for the developed approach and 0.88 and 0.37 t/ha for AquaCrop. Thus, the proposed simple light-use-efficiency model can be considered as a useful tool to correctly reproduce DM and GY values.
... Unfair agreements made by leaders are likely to be of limited duration and may jeopardize agro-industrial investments. Likewise, farmers should consider that adhering to contractual agreements will likely benefit them in the long run (Hammer & Muchow, 1994;Heald, 1988). Indeed, in developing countries, studies have revealed several factors that determine the adoption of agricultural contracts. ...
The purpose of this article is to analyze the economic impact of participation of parboiled rice stakeholders in contract farming. Data were collected from a random sample of 200 farming households including 150 participants and 50 non-participants in the department of Collines in Benin. The results of econometric estimates show that the participation in contract is mainly determined by membership in a group of rice farmers, technical extension assistance and access to markets, good quality agricultural products, gender and formal education. Findings also reveal that contractualization has a positive effect on the income of the main primary players, in particular producers and processors of parboiled rice. It is therefore urgent that the agricultural contract remains a very important lever in rural areas in Benin. The contracts are likely to improve the economic efficiency of the adopters. Appropriate flexible contract measures are necessary and essential to boost growth in developing countries.
... The assumption of a direct link between the evolution of LAI and crop development has been proposed by several authors (Nelder, 1961, Dale et al., 1980, Dwyer and Stewart, 1986, Teittinen et al., 1994Hammer et al., 1994) and in the model of Jamieson et al. (1995) four stages of evolution can be found for LAI. ...
... The response of simulated tef and wheat yields to elevated atmospheric CO 2 levels agreed with the existing general knowledge for C3 and C4 plants. While there are no published field studies on the effects of elevated CO 2 on tef, it is likely similar to other C4 crops (Hammer and Muchow, 1994;Pembleton et al., 2016). Unlike C3 crops, like wheat, which increase both in radiation use efficiency and transpiration efficiency, C4 crops only increase in transpiration efficiency (Urban et al., 2017). ...
Tef and wheat are staple grains in Ethiopia and are an important part of Ethiopian food security. The DSSAT NWheat and DSSAT Tef models were used to examine the effects of nitrogen fertilizer, planting date, and atmospheric CO2 on tef and wheat grain yields across four locations in Ethiopia and a 30-year time period.
Observed wheat yields were consistently higher than observed tef yields, but the models showed that tef could outproduce wheat in some low yielding scenarios. Wheat yields were more responsive to N fertilizer than tef, due to a higher harvest index causing more of the additional biomass to be allocated to grain yields. Frequently, high rainfall increased N leaching, exacerbated N stress, and reduced yields for both crops. Early planting was often detrimental to yields, except for regions and years with terminal drought and heat stress. With continuously increasing atmospheric CO2 concentrations, wheat, as a C3 crop, will further outperform tef, a C4 crop, in the future, as long as N is not limiting. Breeding for lodging resistance and a higher harvest index could significantly improve future tef yields, while higher N applications and the use of split fertilizer applications to avoid leaching would improve both tef and wheat yields. As wheat has a higher N response than tef, is more responsive to future elevated atmospheric CO2 levels, and is generally higher yielding, wheat could add more to food security in Ethiopia. However, under low input, low yielding conditions, growing tef will likely remain the preferred cereal in Ethiopia due to its higher cultural, nutritional and economic value.
... However, it is generally considered that seasonal TE is primarily determined later in the season when leaf area index is higher and the bulk of dry matter accumulation occurs (Tanner and Sinclair, 1983). The effects of VPD on TE are often accounted for using the following equation (Hammer and Muchow, 1994) TE (g m −2 mm −1 ) = kc/VPD (kPa) where kc is a crop specific constant, and VPD is mean daylight VPD. ...
Australian agriculture is dominated by rainfed cropping in environments where evaporative demand greatly exceeds annual rainfall. In this paper we review field measurements of crop transpiration and bare soil evaporation under rainfed grain crops, and crop transpiration efficiencies. Crop transpiration is typically calculated from the difference between evapotranspiration and bare soil evaporation, however, while the former is readily measured, the latter is difficult to obtain. For wheat we found only 19 studies which measured the critical water balance parameters of bare soil evaporation and crop transpiration in Australia, and very many fewer for other crops. From the studies reported for wheat, on average 38% of evapotranspiration was lost to direct soil evaporation. Data for other crops are insufficient to ascertain whether they are similar or different to wheat in terms of the relative contributions of Es and T to the water balance. Although it may have occurred in practice, we can find no field measurements of the crop water balance to demonstrate an increase in crop transpiration at the expense of bare soil evaporation as a function of improvements in agronomic practices in recent decades. Although it is thought that crop transpiration efficiencies are primarily a function of vapour pressure deficit, transpiration efficiencies reported in the literature vary considerably within crops, even after accounting for vapour pressure deficit. We conclude that more reliable estimates of crop transpiration efficiency would be highly valuable for calculating seasonal transpiration of field grown crops from shoot biomass measurement, and provide an fruitful avenue for exploring water use efficiency of grain crops.
... The same result has been achieved for APSIM simulation of wheat biomass in the Netherland and Western Australia (Asseng et al., 1998;Asseng et al., 2000). Crop model like APSIM use crop-specific radiation use efficiency (RUE) to estimate daily biomass production and allocating biomass to different organs using stage dependent empirical coefficients (Hammer & Muchow, 1994;Sinclair & Muchow, 1999). The equation to calculate biomass in APSIM as follows: ...
Global crop production is affected by seasonal and climatic variations in temperature, rainfall patterns or intensity and the occurrence of abiotic and biotic stresses. Climate change can alter pest and pathogen populations as well as pathogen complexes that pose an enormous risk to crop yields and future food security. Crop simulation models have been validated as an important tool for the development of more resilient agricultural systems and improved decision making for growers. The Agricultural Production Systems Simulator (APSIM) is a software tool that enables sub-models to be incorporated for simulation of production in diverse agricultural systems. Modification of APSIM to incorporate epidemiological disease model for crop growth and yield under different disease intensities has few attempts in the UK or elsewhere. The overall aim of this project is to model disease impact on wheat for improved food security in two different agro-ecological zones. The incidence of wheat diseases between 2009 and 2014 in two different agro-ecological zones, UK and Oman were compared. Most of the fields surveyed in Oman and UK were found to have at least one disease. Leaf spot was the most prevalent foliar disease found in Omani fields while Septoria was the most common foliar disease in the UK. Fusarium followed by eyespot and ear blight represents the most common diseases of stem and ears in UK winter wheat between 2009 and 2014. However, in Omani wheat Fusarium causing stem base and loose smut of ears were the most common. Eyespot was not found in Omani winter wheat and this may relate to the high temperature during winter in Oman. This study discussed the first work on the occurrence of fungal diseases and their pathogens in Oman and the influence of agronomy factors. Large numbers of pathogenic fungi causing symptoms were found to be prevalent in wheat fields in Oman. Isolation from six symptomatic wheat varieties resulted in 36 different fungal species. Alternaria alternata was the most frequently isolated pathogen followed by Bipolaris sorokiniana, Setosphaeria rostrata, and Fusarium equiseti. Results also showed some agronomic practices influenced disease incidence. Mechanical sowing method and time of urea application were found to influence leaf spot disease. An investigation into the recovery of treatment cost for eyespot control through yield and the effect of fungicide treatment on risk showed that all fungicides apart from (epoxiconazole) Opus at 1 L ha-1 were found to be worth the costs, either under high disease pressure (inoculated sites) or naturally infected sites. For the risk averse manger fungicide treatment would be worth the cost as it would reduce the higher level of disease and consequently minimise associated yield losses. In this work, disease models were built to predict the disease development and yield loss in relation to crop phenology using results from previous literature on conditions favouring sporulation, infection and disease development and severity. Analysis of 461 data sets showed that climatic conditions and agronomic factors significantly influenced disease development either positively or negatively in all models. The application of a range of fungicides at GS31/32 reduced disease significantly at GS39 in comparison to epoxiconazole alone. Disease severity at GS39 decreased yield only slightly by 2.2% whilst only (prothioconazole) Proline 275 increased yield significantly with almost 30% yield increase. The performance of the APSIM wheat model to simulate phenology, leaf area index, biomass and grain yield of two winter wheat varieties (Okley and Cashel) was evaluated under UK conditions and the previously developed eyespot disease were linked with APSIM. Generally, APSIM poorly predicted the phenology, LAI, biomass and yield of winter wheat grown under UK conditions. The linked eyespot disease models with APSIM simulated an adequate level of disease predication at GS12/13 (9.6%), GS31/32 (1.3%) and GS39 (12%). Overall, the link between eyespot epidemiological disease models and crop growth model has successfully provided the basis for further development of the model and enhance crop growth simulation. Moreover identification of main diseases threatening wheat production in Oman can help to plan for future research, to assess the economic importance and to contrast environment models for yield loss.
... Some anecdotal evidence suggests that importance of sowing date may have been incorrectly downplayed in the study area. Influential research papers between 1990-2014 have implied it is not a determining factor for sorghum yields (Hammer and Muchow 1994;Hammer et al. 2014). Furthermore, when presented with our results, agronomists from major seed suppliers cited these same papers. ...
Sorghum production is a major cropping activity in Australia's northern grain region. The Darling Downs is one of the most important agricultural areas of this region. Between 2005 and 2015, average on-farm sorghum yields in the Eastern Darling Downs were 4.15 t ha-1 , but yields can be as high as 12 t ha-1. We conducted interviews with 12 highly-engaged sorghum farmers in the Eastern Downs (total 75 fields characterised) to measure the diversity of sorghum fields and identify drivers of higher yield, water use efficiency (WUE), N fertiliser efficiency (NUE) and profitability. We observed substantial differences in yield (3.9-7.1 t ha-1); WUE (8-15 kg mm-1 ha-1); NUE (35-78 kg grain kg N applied-1) and gross margin (397-930 AU $ ha-1). A logistic regression indicated that differences in basic agronomy (i.e. sowing date and N input) could explain the observed diversity in performance. Further analysis using crop simulation (via APSIM) showed no increase in downside risk from adopting earlier sowing associated with higher performance. Our results suggest that the importance of basic principles of agronomy remain underappreciated among farmers. The question, then, is why our extension efforts have not yet been able to instil these principles to clients. Our analysis suggests insufficient attention of research and extension has been given to the importance of sowing date, while N investment is constrained by the effects of high farm debt per hectare.
... Crop models provide a way to refine environment characterization as they can account for simultaneous impacts from various environmental factors affecting crop growth and development. Chapman et al. (2000a) used a sorghum crop model (Hammer and Muchow, 1994) to characterize different environments based on a drought stress index, which integrated climatic, soil, management, and crop development factors and also accounted for the timing and severity of the stress. This index was used to distinguish three patterns of drought stress in sorghum and showed that in severe terminal stress conditions, the performance of cultivars ranked differently than in midseason or mid-terminal stress conditions. ...
In plant breeding, one of the major challenges of genomic selection is to account for genotype-by-environment (G × E) interactions, and more specifically how varieties are adapted to various environments. Crop growth models (CGM) were developed to model the response of plants to environmental conditions. They can be used to characterize eco-physiological stresses in relation to crop growth and developmental stages, and thereby help to dissect G × E interactions. Our study aims at demonstrating how environment characterization using crop models can be integrated to improve both the understanding and the genomic predictions of G × E interactions. We evaluated the usefulness of using CGM to characterize environments by comparing basic and CGM-based stress indicators, to assess how much of the G × E interaction can be explained and whether gains in prediction accuracy can be made. We carried out a case study in wheat (Triticum aestivum) to model nitrogen stress in a CGM in 12 environments defined by year × location × nitrogen treatment. Interactions between 194 varieties of a core collection and these 12 different nitrogen conditions were examined by analyzing grain number. We showed that (i) CGM based indicators captured the G × E interactions better than basic indicators and that (ii) genomic predictions were slightly improved by modeling the genomic interaction with the crop model based characterization of nitrogen stress. A framework was proposed to integrate crop model environment characterization into genomic predictions. We describe how this characterization promises to improve the prediction accuracy of adaptation to environmental stresses.
... A simple and robust crop model for soybean (Glycine max (L.) Merr) was developed by Sinclair (1986) using a phenomenological framework. This modelling approach has been generalized for crop growth and yield simulation in spring wheat, maize, sorghum and peanut (Sinclair and Amir, 1992;Hammer and Muchow, 1994;Sinclair and Muchow, 1995). One of the distinguished differences between this approach and that of more complex models (i.e., DSSAT) is that it predicts net dry matter assimilation as a function of radiation use efficiency (RUE) and light interception (Monteith, 1977(Monteith, , 1994, thus avoiding photosynthesis, growth and maintenance respiration issues (Boote et al., 2013). ...
Irrigated processing snap bean (Phaseolus vulgaris L.) production in Wisconsin, mostly in the central sands region, ranks first in both yield and harvested hectarage in the U.S. However, little crop modelling effort has been made to simulate the nitrogen (N) effects on growth and yield of non-nodulating snap bean, which demands high N inputs and imposes risks on groundwater nitrate-nitrogen (NO3-N) leaching in sandy soils. The objective of this study was to develop a simple model for non-nodulating snap bean development, growth and yield in response to N, following a phenomenological and physiological framework and applying the 4-N-pool approach to quantify the crop N demand. The two mechanisms under N limitation were tested and incorporated into the crop modelling, 1) reducing green leaf area while maintaining specific leaf N (SLN, gm−2), and (2) diluting the SLN which further reduces radiation use efficiency (RUE, MJm−2) while maintaining green leaf area. The 2015 dataset with six N treatments, five plant densities and two sowing dates was used to develop the model, and an independent dataset from four commercial fields across the 2013 and 2014 growing seasons were used to va- lidate the model. The model was first tested with 2015 dataset by comparing predicted and measured leaf area index (LAI), yield (pod dry weight, gm−2), above ground biomass (AGB, gm−2) and cumulative crop N uptake (CNUP, gm−2), and high coefficients of determination (R2, 0.83–0.90) and low root-mean-square errors (RMSE, 7.6–8.6% of the whole range of the target crop attributes) were determined. The external validation was con- ducted with the 2013 and 2014 datasets by comparing the yield, AGB and CNUP, a good agreement was found, with standard deviation (SD) lower than 10% of the mean (range, 1.9–9.0%), except for yield in one field in 2013 (SD=19.4%). The results proved the robustness of the model to simulate snap bean growth and yield under various management strategies.
... The total number of leaves produced, multiplied by the leaf appearance rate, determines the timing of the flag leaf stage. All subsequent stages use thermal time targets, although cardinal temperatures during reproductive development differ from those during vegetative phases, with a base temperature of 5.7° C, an optimum temperature of 23.5° C, and no maximum temperature (Hammer and Muchow, 1994). Canopy development is simulated on a per plant basis as the sum of the area of all fully expanded leaves on the main shoot and each tiller. ...
High temperatures across the Australian sorghum belt can reduce sorghum yields, but genotypic differences in heat tolerance could mitigate these yield losses. The objectives of this study were to quantify occurrences of high temperatures around anthesis of sorghum, determine their yield impacts, and assess the potential for management and genetics to minimise any adverse effects. Long term weather records for six locations across the Australian sorghum belt were used to quantify the probability of high temperature occurrence. These records were then used in a simulation study with the APSIM-sorghum model. The model was adapted to capture high temperature effects on grain yield for five hypothetical genotypes that differed in temperature threshold for effects on seed set and in tolerance to temperatures above that threshold. Results showed that the most common incidence of heat stress around anthesis was the occurrence of individual days with maximum temperatures between 36–38° C. Because these temperatures were near the threshold limiting seed set in tolerant genotypes, an increased temperature threshold generally minimised adverse yield effects. However, 1–5 °C predicted temperature increases in coming decades will justify additional selection for increased tolerance above the threshold. Manipulation of sowing dates did not reduce risks of heat stress around anthesis, unless sowing was extremely late. Hence, genetic improvement provides the best prospect to mitigate heat stress effects on grain yield.
... قرار کرد ن د پراکند میزان قت گ داده ی دقت میزان و مدل های داده برآورد تغییر ضریب از ها پذیری با برابر که 97 / 4 درصد است استفاده شد خطا مربعات میانگین جذر . یعنی RMSE با برابر 77 / 5 به نسبت که بود دیگر ک صفات ه مدل با پ ی ش ب ی ن ی شده اند دقت به نسبت و داشته کمتری همان از که گونه بر تبیین ضریب م ی آ ی د ( 67 / 0 ،) ذرت ( Sinclair & Muchow, 1999;Turabi & Soltani, 2013 ) سورگوم ، ( Hammer & Muchow, 1994 ) بادام ، زمینی ( Hammer et al., 1995 ) نخود و ( Soltani et al., 1999 ...
In order to modeling of growth stages and yield of corn according to Hamedan province meteorological data (minimum and maximum temperature, radiation and rainfall) By using the sub models of phenology, production and distribution of dry matter and leaf area changes in maize studies was conducted at the Faculty of Agriculture, University of Vali-e-Asr Rafsanjan in spring 2015. Daily changes of phenology, total dry matter and leaf area was calculated using the model and the yield was predicted. One of the criteria to evaluation of a model is Comparison between coefficients of linear regression of observed and predicted yield (b=0.29+- 2.11 and a=0.93+- 0.23) and coefficients of line 1:1 (1, 0). Accuracy of the model related to coefficient of variations of predicted and observed seed yield (CV= 4.13) was very high so that in field experiments coefficient of variations limit is 20 to 25. R2 quantity of seed yield was 0.69; showing that the probability for coordination of predicted and observed data is 69 percent. The Root mean square error is the other statistics which is used to evaluation of model accuracy. The Root mean square error of seed yield was 0.36, which is evidence of accuracy of model for yield prediction. domain variation for observed and predicted data were 8.54-9.99 tones and 8.02-9.25 tons per hectare respectively and the means were 9.09 and 8.75 tones per hectare respectively.
Keywords: Modeling, Phenology, Grain yield, Corn
... Les IPFD ont ensuite été soustraites des SFD pour avoir les durées semis-initiation paniculaire (SIP) aux différentes dates de semis. Toutes les durées calculées ont été converties en temps thermique selon l'approche broken linear function ( Hammer et Muchow, 1994). Les températures de base, optimale et maximale ont été fixées respectivement à 11°C, 34°C et 46°C ( Garcia-Huidobro et al., 1982;van Oosterom et al., 2001). ...
Today crop simulation models are as a developed tools for understanding and analyzing how soil, plant
and atmosphere affecting crop growth and development. This study was conducted in order to evaluate
WOFOST model in maize (Zea mays L.) yield production in summer cropping system under sub-tropical
conditions in Jiroft region, Iran at 2012 and 2013. Model input database includes the climate data (daily
parameters of solar radiation, temperature and rainfall), plant data (time of germination, flowering and
maturity, grain yield and dry matter) and soil data (physical and chemical soil properties). Plant variables
of model include phenological stages, dry matter production, yield and biomass collected from field
which performed at different planting dates and genotypes. At the next step, model was calibrated and
evaluated with field data. The results showed RMSE, for grain yield, biomass and harvest index were
9.06, 4.24 and 10.11, respectively. The model efficiency coefficient (E) for grain yield, biomass and
harvest index was 0.99, 0.87, and 0.82, respectively. Therefore, these results presented high precision of model simulation. The results of WOFOST evaluation showed that the efficiency of model is good for maize summer cropping system under tropical climatic conditions of Jiroft region.
Keywords: planting date, maize (Zea mays L.), phenology, dry matter, modeling.
An increase in crop water stress is expected in many regions over coming decades. Therefore, there is a need for drought tolerant and high yielding wheat varieties to ensure global food security. Breeding on drought tolerance has proven to be difficult as there is no fast, automated and reproducible phenotyping method linked to yield under water stress. In this study, we present a method measuring leaf elongation rate (LER) on a high temporal resolution. 320 wheat varieties with three replicates were grown for one week in a greenhouse and were exposed to increasing water stress. LER was measured along with temperature, air humidity, light and gravimetric water content (GWC) of the substrate. Genotype specific response curves to environmental variables were used to model LER. The resulting model was able to predict LER of an unseen data set (R 2 = 0.40). A genome wide association study (GWAS) resulted in some interesting candidate genes for genotype specific drought response which might be further examined. The entire phenotyping process was cheap and could easily be adapted by breeders. It led to a rough characterization of drought tolerance within three weeks. This opens the way for selection on drought tolerance at an early breeding stage.
Sorghum is an important food and feed crop in the dry lowland areas of Ethiopia. Farmers grow both early-sown long-duration landraces and late-sown short-duration improved varieties. Because timing and intensity of drought stress can vary in space and time, an understanding of major traits (G), environments (E), management (M), and their interactions (G×E×M) is needed to optimize grain and forage yield given the limited available resources. Crop simulation modeling can provide insights into these complex G×E×M interactions and be used to identify possible avenues for adaptation to prevalent drought patterns in Ethiopia. In a previous study predictive phenology models were developed for a range of Ethiopian germplasm. In this study, the aims were to (1) further parameterize and validate the APSIM-sorghum model for crop growth and yield of Ethiopian germplasm, and (2) quantify by simulation the productivity-risk trade-offs associated with early vs late sowing strategies in the dry lowlands of Ethiopia. Field experiments involving Ethiopian germplasm with contrasting phenology and height were conducted under well-watered (Melkassa) and water-limited (Miesso) conditions and crop development, growth and yield measured. Soil characterization and weather records at the experimental sites, combined with model parameterization, enabled testing of the APSIM-sorghum model, which showed good correspondence between simulated and observed data. The simulated productivity for the Ethiopian dry lowlands environments showed trade-offs between biomass and grain yield for early and late sowing strategies. The late sowing strategy tended to produce less biomass except in poor seasons, whereas it tended to produce greater grain yield except in very good seasons. This study exemplified the systems approach to identifying traits and management options needed to quantify the production-risk trade-offs associated with crop adaptation in the Ethiopian dry lowlands and further exemplifies the general robustness of the sorghum model in APSIM for this task.
Developing and promoting neglected and underutilised crops (NUS) is essential to building resilience and strengthening food systems. However, a lack of robust, reliable, and scalable evidence impedes the mainstreaming of NUS into policies and strategies to improve food and nutrition security. Well-calibrated and validated crop models can be useful in closing the gap by generating evidence at several spatiotemporal scales needed to inform policy and practice. We, therefore, assessed progress, opportunities, and challenges for modelling NUS using a systematic review. While several models have been calibrated for a range of NUS, few models have been applied to evaluate the growth, yield, and resource use efficiencies of NUS. The low progress in modelling NUS is due, in part, to the vast diversity found within NUS that available models cannot adequately capture. A general lack of research compounds this focus on modelling NUS, which is made even more difficult by a deficiency of robust and accurate ecophysiological data needed to parameterise crop models. Furthermore, opportunities exist for advancing crop model databases and knowledge by tapping into big data and machine learning.
An approach based on a linear rate of increase in harvest index (HI) with time after anthesis has been used as a simple means to predict grain growth and yield in many crop simulation models. When applied to diverse situations, however, this approach has been found to introduce significant error in grain yield predictions. Accordingly, this study was undertaken to examine the stability of the HI approach for yield prediction in sorghum [ Sorghum bicolor (L.) Moench]. Four field experiments were conducted under nonlimiting water and N conditions. The experiments were sown at times that ensured a broad range in temperature and radiation conditions. Treatments consisted of two population densities and three genotypes varying in maturity. Frequent sequential harvests were used to monitor crop growth, yield, and the dynamics of HI. Experiments varied greatly in yield and final HI. There was also a tendency for lower HI with later maturity. Harvest index dynamics also varied among experiments and, to a lesser extent, among treatments within experiments. The variation was associated mostly with the linear rate of increase in HI and timing of cessation of that increase. The average rate of HI increase was 0.0198 d ⁻¹ , but this was reduced considerably (0.0147) in one experiment that matured in cool conditions. The variations found in HI dynamics could be largely explained by differences in assimilation during grain filling and remobilization of preanthesis assimilate. We concluded that this level of variation in HI dynamics limited the general applicability of the HI approach in yield prediction and suggested a potential alternative for testing.
Crop models are essential tools for analysing the effects of climate variability, change on crop growth and development and the potential impact of adaptation strategies. Despite their increasing usage, crop model estimations have implicit uncertainties which are difficult to classify and quantify. Failure to address these uncertainties may result in poor advice to policymakers and stakeholders for the development of adaptation strategies. Since the 1990s, the number of crop model uncertainty assessments that consider different sources of model uncertainty (model structure, model parameters and model inputs such as climate, soil, and crop management practices) has increased significantly. We present the outcomes of a systematic review focused on uncertainty assessments of crop model outputs (mainly grain yield) and crop model uncertainty decomposition. We reviewed 277 articles from 1991 to 2019 which included studies conducted in 82 countries (460 locations) across all continents. 57% of the articles have been published between 2015 and 2019. 52% of the studies focus on input uncertainty assessments with climate change projections as the most frequently considered source of input uncertainty. Only 28% and 20% of the studies, respectively, dealt with uncertainties related to model parameters and model structure. The latter was mainly quantified using multi-model ensembles. Over half the studies were carried out in European and Asian countries, 34% and 23%, respectively. Most articles estimated model uncertainty focusing on the grain yield of major cereal crops (wheat > maize > rice) using the Decision Support System for Agrotechnology Transfer (DSSAT) model. Sensitivity analysis was the most used technique to quantify the contribution of different sources of uncertainty although the range of approaches for uncertainty quantification was wide. There is a need for standard procedures to estimate crop model uncertainty and evaluate estimates. We discuss the challenges of quantifying the components of uncertainty within crop models and identify research needs to better understand sources of uncertainty and thus improve the accuracy of crop models.
Functional genomics is the systematic study of genome‐wide effects of gene expression on organism growth and development with the ultimate aim of understanding how networks of genes influence traits. Here, we use a dynamic biophysical cropping systems model (APSIM‐Sorg) to generate a state space of genotype performance based on 15 genes controlling four adaptive traits and then search this space using a quantitative genetics model of a plant breeding program (QU‐GENE) to simulate recurrent selection. Complex epistatic and gene × environment effects were generated for yield even though gene action at the trait level had been defined as simple additive effects. Given alternative breeding strategies that restricted either the cultivar maturity type or the drought environment type, the positive (+) alleles for 15 genes associated with the four adaptive traits were accumulated at different rates over cycles of selection. While early maturing genotypes were favored in the Severe‐Terminal drought environment type, late genotypes were favored in the Mild‐Terminal and Midseason drought environment types. In the Severe‐Terminal environment, there was an interaction of the stay‐green (SG) trait with other traits: Selection for + alleles of the SG genes was delayed until + alleles for genes associated with the transpiration efficiency and osmotic adjustment traits had been fixed. Given limitations in our current understanding of trait interaction and genetic control, the results are not conclusive. However, they demonstrate how the per se complexity of gene × gene × environment interactions will challenge the application of genomics and marker‐assisted selection in crop improvement for dryland adaptation.
Phenology algorithms in crop growth models have inevitable systematic errors and uncertainties. In this study, the phenology simulation algorithms in APSIM classical (APSIM 7.9) and APSIM next generation (APSIM-NG) were compared for spring barley models at high latitudes. Phenological data of twelve spring barley varieties were used for the 2014–2018 cropping seasons from northern Sweden and Finland. A factorial-based calibration approach provided within APSIM-NG was performed to calibrate both models. The models have different mechanisms to simulate days to anthesis. The calibration was performed separately for days to anthesis and physiological maturity, and evaluations for the calibrations were done with independent datasets. The calibration performance for both growth stages of APSIM-NG was better compared to APSIM 7.9. However, in the evaluation, APSIM-NG showed an inclination to overestimate days to physiological maturity. The differences between the models are possibly due to slower thermal time accumulation mechanism, with higher cardinal temperatures in APSIM-NG. For a robust phenology prediction at high latitudes with APSIM-NG, more research on the conception of thermal time computation and implementation is suggested.
Reducing sorghum yield gaps depends on the capacity to identify combinations of genetics and management that best suit region and seasonal conditions. Using simulated and empirical data, we explored how the combination of different sowing dates and genotype maturity respond to specific water stress patterns common across a temperate region (Argentina Pampas). This region was recently characterized by three water stress patterns (or environmental types, ENVT). These ENVT are: pre-flowering stress, low terminal stress, and grain-filling stress.
In the north and central regions, significant ENVT x sowing date interaction for yield (p<0.05) indicated that sowing date should be chosen depending on the prevailing seasonal ENVT. This drought scape strategy increased yields by 4068 to 5049 kg ha -1. In the southern region, early sowings had the highest yields independently of the ENVT. Genotype maturity effect was less important, although early materials increased yield by 438 to 923 kg ha -1 (5-25%) relative to the intermediate genotype, depending on the region. Under low terminal or grain-filling stress, early sowings gave the highest yields via increased accumulated biomass and/or harvest index. Under pre-flowering stress, delaying the sowing dates increased final yields via improved harvest index. Later sowings provided a conservative strategy for reducing risk in the north and central east regions, while for the central west and southern regions the sowing date should be as early as possible. We provided information to improve sorghum management decisions and guide breeding in temperate regions.
Plant processes, such as leaf expansion, stomatal conductance and transpiration, are affected by soil water, particularly in water-stressed environments. Quantifying the effects of soil water on plant processes, especially leaf expansion and transpiration, could be useful for crop modeling. In order to quantify the leaf expansion and transpiration in response to soil water deficit in three millet species, common (Panicum miliaceum L.), pearl (Pennisetum glaucum L.) and foxtail (Setaria italica L.) millets, a pot experiment was performed at the Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran. The soil water status was characterized by the fraction of transpirable soil water (FTSW). Leaf area and transpiration were measured daily. Relative leaf area expansion (RL) and relative transpiration (RT) data were plotted against FTSW. Finally the FTSW thresholds for RL and RT were calculated using linear-plateau and logistic models. The results showed that the thresholds for RL and RT were 0.68 and 0.62, respectively, based on all measured data of the three millet species using the linear-plateau model, indicating that RL and RT were constant when FTSW decreased from 1 to the threshold point. Thereafter, until FTSW = 0, RL and RT declined linearly with a slope of 1.48 and 1.43, respectively. Although millet is cultivated as a resistant crop in arid, semiarid and marginal lands, it showed an early response to soil water deficit at high FTSW thresholds. As leaf expansion and transpiration can be considered morphological and physiological variables, respectively, the results in this study indicate that millet has strong morphological flexibility when faced with soil water deficit.
Argentinean current sorghum management is similar across the entire temperate region, and no environmental characterization is assisting breeding and management decisions. Crop growth and development simulation models are a valuable tool for generating this characterization. Our study calibrated and validated APSIM-sorghum for our genetics and production environments, and used it to characterize the main water and heat stress patterns at our temperate central region. The calibration and validation provided accurate phenology, biomass, and yield estimations. Long-term weather records (44–61 years per site) and soil data were used to simulate the seasonal drought patterns at seven representative sites across the region. Clustering analysis identified three major drought environmental types (ENVT): (i) a pre-flowering drought stress, showing large occurrence frequency (39%), (ii) a low terminal drought stress, showing similar frequency (38%), and (iii) a grain-filling drought stress, showing lower frequency (23%). The most frequent ENVT at individual sites agrees with the spatial distribution of annual rainfall. However, most sites evidenced variable frequency of all ENVT. Flowering heat stress (>33 °C) showed an intermediate occurrence frequency (20–50%) only at lower latitudes, and was independent of drought ENVT. Defined ENVT helped explain observed genotype x environment (GxE) interactions for yield in an independent data set, showing they have practical implications for optimizing breeding and management strategies across the region of interest. Grouping sites of similar frequency can help to handle the spatial variability when defining these strategies, but dealing with seasonal variability will be challenging in the context of no predominant ENVT.
Sugarcane is the most important crop in tropical Australia. Limited water, increasing cost of water, and competing demands (e.g., environmental flows) all impact greatly on sugarcane profitability and expansion.
Improving crop transpiration efficiency (TE, defined as growth per unit of water used) is one strategy to help address these issues.
TE measured in a wide range of sugarcane clones was found to vary about ±20% around the mean. Crop growth simulation modelling indicated a 1% variation in TE translated to about 0.5 to 1.5% variation in final cane yield in commercial production environments, highlighting the importance of this trait.
Genetic variation in TE was due to variation in both stomatal conductance and photosynthesis capacity.
The contribution from the latter suggests improvements in TE are possible without necessarily compromising growth rates.
Increasing CO2 levels increased TE but genotypes ranked similarly for TE at different CO2 levels, indicating selection at current atmospheric CO2 levels will translate to improvements in future years when CO2 is higher.
The results suggest targeted improvement of TE in sugarcane breeding would be worthwhile. This could include screening for relative TE in early stage selection trials using thermal imaging and routine yield measurements, and a recurrent parental improvement program focused on crossing high TE parental clones.
The soil of rangelands is an important global carbon sink, in which any change makes a high impact on the CO2
emissions to the atmosphere and global warming. The capacity of this sink is controlled by complex interaction
functions among various factors, including climate, soil properties, vegetation type, and management practices.
For understanding the effect of these factors on soil carbon in long term, the soil carbon models have a vital
role. The soil carbon models must be correctly validated for a specific region and ecosystem, then they can be
used to simulate and predict changes in soil carbon. The main objective of this study was to evaluate the
performance of RothC and Century models as the most widely used models in the soil carbon studies for semiarid rangelands of Bajgah in Fars province. The R2
(determination Coefficient), r (correlation coefficient),
RMSE (root mean square error), MAE (mean absolute error), MD (mean difference) and t-student test between
simulated and measured values of soil organic C were used to evaluate the performance of RothC and Century
models. Results showed although the Century model negligibly simulated SOC lower than the RothC model,
but based on the statistical analyses, both models represented satisfactory results and their simulated values
were consistence well with the measured values. Also the results of simulations by Century and RothC models
showed that the SOC stocks will be increased during the years of 1987 to 2050 by 7.92% and 12.92%,
respectively.
Soil organic carbon (SOC) had been as the largest storehouse of terrestrial carbon and
is a key soil component and determinant soil quality and health. Soil carbon dynamics
is outcome of complex processes. Rothamsted carbon estimation models had been a
simple model that can be used to simulate SOC content changes. The main objective of
this study is evaluate the accuracy of the Roth C model at estimating soil organic
carbon stock changes in rangeland ecosystems of Ghir VA Karzin’s BandBast. For this
purpose, in each month 20 samples from surface layer (0 to 20 cm) were collected and
in the laboratory, soil physicochemical properties including soil texture, bulk density
and organic carbon content was determined. Based on the results obtained of carbon
simulation, this model has been well to estimate the organic carbon.
Key words: Simulation of organic carbon, BandBast rangelands, Roth C.
Safflower (Carthamus tinctorius) is one of the most important agronomic and medicinal crops in Iran, that it’s potential yield and limitations can be determined using a simple model and long-term meteorological data. This study was performed to yield prediction and statistical modeling of Safflower based on meteorological indicators and climatic parameters. Phenology, dry matter production and distribution and soil-water balance sub models should be studied in order to growth stages and yield prediction in agricultural crops. Parameters related to each sub model were estimated using data reported on different sowing dates during the years 2002-2015 in Isfahan region and the data reported by other researchers in other regions. Growth and yield changes were calculated by phenology, dry matter production and distribution using meteorological data (minimum and maximum temperatures, radiation and rainfall) from Isfahan region, and the safflower crop yield at the end of growing season was predicted. One of the model evaluation criteria is comparison of coefficient of linear regression between observed and predicted yield (a= 0.46 ± 0.073, b= 1.49 ± 0.18) with coefficient of line 1:1. In the field experiments the limit for Coefficient of variation (CV) is 20 to 25. Accuracy of the model was high, regarding to the coefficient of variation of predicted and observed grain yield (CV=8.89). R2 of grain yield was 0.75, which is indicating that predicted data are 70 percent likely match with observed data. Variation range for observed data was 1.2 to 4.61 tones per hectar and the mean was 2.9 tones and for the predicted data it was 1.94 to 3.62 tones per hectar and the mean was 2.78 Tones per hectar. In all cases, simulated yield compliance with observed yield. Hence, given the ability of the model to simulate the phonological stages of safflower, it can be used as a suitable tool for planning and better management of safflower fields in Isfahan
To prediction growth and yield of crops should be studied phonological sub models, production and distribution of dry matter and soil water balance. This study was conducted to predict the yield of wheat on weather conditions in Hamedan. model parameters for each sub model were estimated by using data from different sowing date in the years from 1982 to 2002 in Hamadan and data from other researchers in other parts. According to the province meteorological data (minimum temperature, maximum temperature, irradiance and rainfall) and by using the following sub models of phenology, production and distribution of dry matter was calculated changes of growth and yield. And yield of wheat crop yield was predicted at the end of the growing season. Variations of grain yield was observed between 4.08 to 8.01 ton per hectare and the average data was 6.09 tons and for prediction data Range of yield Variations was between 4.08 to 7.59 ton per hectare and that average was 5.53 ton per hectare that all of the them Simulated yield had a good correspond with the observed yield. Thus, according to the ability to appropriate the model to simulate phenological stages of wheat, Could be use it as a convenient tool for better management planning and wheat fields as well as a decision support system used in Hamedan.
Keywords: Meteorological data, wheat, Management, Grain yield, Hamedan.
In order to modeling of growth stages and yield of wheat according to Hamedan province meteorological data (minimum and maximum temperature, radiation and rainfall) By using the sub models of phenology, production and distribution of dry matter and leaf area changes in maize studies was conducted at the Faculty of Agriculture, University of Vali-e-Asr Rafsanjan in spring 2015. The parameters of sub model were estimated according to data from previous researches in Iran and other countries. Daily changes of phenology, harvest Index total dry matter and leaf area was calculated using the model and the yield at the end of season was predicted. One of the criteria to evaluation of a model is Comparison between coefficients of linear regression of observed and predicted yield (b=0.90±0.67 and a=0.73±0.10) and coefficients of line 1:1 (1, 0). Accuracy of the model related to coefficient of variations of predicted and observed seed yield (CV= 7.28) was very high so that in field experiments coefficient of variations limit is 20 to 25. R2 quantity of seed yield was 0.81; showing that the probability for coordination of predicted and observed data is 81 percent. The Root mean square error is the other statistics which is used to evaluation of model accuracy. The Root mean square error of seed yield was 0.43, which is evidence of accuracy of model for yield prediction. domain variation for observed and predicted data were 4.08-8.01 tones and 4.08-7.59 tons per hectare respectively and the means were 6.09 and 5.53 tones per hectare respectively.
A grain sorghum growth model based on complex physical, physiological and morphological principles simulated plant DM accumulation relatively accurately. Changes in row spacing, row direction, plant population, hybrids, ambient temperature, daily solar radiation or available soil moisture resulted in changes in plant DM accumulation
The CREAMS hydrology model was evaluated for two Vertisols, each with three fallow management strategies, by comparing predictions of runoff, soil moisture and drainage with 5-8 years of measured data. Model parameter values were derived by: (i) using a combination of measured site characteristics and published values, and (ii) optimizing selected parameters, particularly the runoff parameter (curve number). With parameter values from published sources, runoff was overpredicted by 1 to 39%; good estimates of total soil moisture were obtained. Using optimized curve numbers, runoff was predicted well (daily, r2 = 0.83; monthly, r2 = 0.92; annual, r2 = 0.94). Total soil moisture values were predicted well, the main source of error being from overprediction of transpiration. Errors in predicted runoff caused little of the error in predicted total soil moisture. The distribution of soil moisture in the soil was poorly predicted. Drainage predictions were similar to estimates from steady-state solute mass balance. Optimized curve numbers derived in this study provide parameter values for modelling the water balance of self-mulching Vertisols. Values of other model parameters, derived from field measurements and published sources were near optimal, and predictions were not improved by adjusting the more sensitive of these parameters. The model is considered adequate for many practical applications. Some enhancements to the model are suggested.
A model is presented for calculating the daily evaporation rate from a
crop surface. It applies to a row crop canopy situation in which the
soil water supply to the plant roots is not limited and the crop has not
come into an advanced stage of maturation or senescence. The crop
evaporation rate is calculated by adding the soil surface and plant
surface components (each of these requiring daily numbers for the leaf
area index), the potential evaporation, the rainfall, and the net
radiation above the canopy. The evaporation from the soil surface
Es is calculated in two stages: (1) the constant rate stage
in which Es is limited only by the supply of energy to the
surface and (2) the falling rate stage in which water movement to the
evaporating sites near the surface is controlled by the hydraulic
properties of the soil. The evaporation from the plant surfaces
Ep is predicted by using an empirical relation based on local
data, which shows how Ep is related to Eo through
the leaf area index. The model was used to obtain the total evaporation
rate E = Es + Ep of a developing grain sorghum
(Sorghum bicolor L.) canopy in central Texas. The results agreed well
with values for E measured directly with a weighing lysimeter.
Approaches using breeding, physiology and modelling for evaluating adaptation of plant genotypes to target environments are discussed and methods of characterizing the target environments outlined. Traditional approaches, and their limitations, to evaluation of genotypic adaptation using statistical and classificatory techniques with a phenotypic model are discussed. It is suggested that a simple biological model is the most appropriate framework in which to integrate physiology and modelling with plant breeding. Methods by which physiology and modelling may contribute to assessment of adaptive traits and to selection for adaptation in a breeding programme are considered.
The major problem facing the area is inter- and intrayear variability of rainfall. Farmers have adapted to this by building diversity and flexibility into their farming systems. Policy makers and planners have also attempted to reduce the instability of dryland agriculture and its impact on the rest of the economy. However, the measures adopted by them have often in the past been designed to protect against drought rather than to respond to both negative (drought) and positive (high rainfall) aspects of rainfall variability. More recently, advances in science and technology have provided new methods and data enabling improved understanding of the agroclimatic agrobiological and other environmental variables affecting agriculture, and these have demonstrated the extent to which it is possible to obtain higher and more stable crop production. In Section 2 a method of assessing the impact of rainfall variability on crop yields is demonstrated with respect to sorghum in a semi-arid tropical subregion in Karnataka State. The large variation of the distribution of seasonal rainfall has led farmers to devote a large proportion of their annual crop production to post-rainy cropping, so that the crops are raised on residual moisture. This practice has led to increased soil erosion as bare lands are exposed during the rainy season. Section 3 considers the variations in sorghum yields that occur under moist and dry scenarios. The study identifies the areas in which it would be worthwhile to collect surface runoff water for subsequent use for protective irrigation, to ensure crop survival during the dry season. Short-term responses take the form of adjustments of plant population, changes in levels of input such as the application of fertilizers and variations in other agronomic practices. Adjustments to severe drought situations often include curtailment of essential consumption and the disposal of productive assets. The study concludes with a plea for dovetailing the application of technology with public interventions, and with farmers' traditional responses to climatic variability. The public interventions should be more in the nature of permanent drought-relieving assets rather than short-term measures to alleviate distress. -from Editors
In dryland farming systems, opportunities to improve sunflower (Helianthus annuus L.) yields are mostly associated with management decisions made at planting. Dynamic crop simulation models can assist in making such decisions. This study reports the structure of QSUN, a simple and mechanistic crop model for sunflower, and how it accounts for the dynamic interaction of the crop with the soil and aerial environment. The model incorporates several recent approaches to simulation of crop growth in dryland conditions. QSUN estimates growth, development, and yield of a sunflower crop. Daily inputs of temperature and photoperiod drive a phenology submodel to predict stages of emergence, bud visibility, 50% anthesis, and maturity [...]
Laboratory and greenhouse experiments were performed for 9 Kansas soil types to determine percentage sand, silt and clay, moisture content percentage by weight (θ <sub>m</sub>) at -1/3 bar and -15 bar water potential, and soil factors that quantity the soil's drying rate: U, the cumulative evaporation during the energy-limiting stage; and c, the least squares slope of evaporation rate versus $\text{time}^{1/2}$ during the soil-limiting stage of soil surface evaporation. Hydrometer method for mechanical analysis was used to determine the particle-size distribution, and pressure plate method was used to determine Θ m. U and c were obtained from weight losses from soil columns in a greenhouse experiment. Results of the experiment indicated that particle size composition and percentage water-holding capacity (WHC) influenced the values of U and c obtained. U and c were relatively low for sandy soil and soil with low WHC but higher for soil with greater WHC and percentage silt. The values of U ranged from 5 to 18.7 mm; of c, from 1.68 to 3.73 mm $\text{day}^{-1/2}$ .
The objective of this analysis was to use a simple, mechanistic crop growth model to examine the effects of variation in solar radiation and temperature on potential maize (Zea mays L.) yield among locations. Crop phenology and leaf growth were calculated from daily mean temperature data obtained at the five locations studied. Daily biomass accumulation was calculated by estimating the amount of radiation intercepted and assuming maximum crop radiation use efficiency of 1.6 g MJ−¹. Grain yield accumulation was simulated using a linear increase in harvest index during grain filling. Observed and simulated grain yields were compared for several sowings at each of five localions ranging from latitude 14°S to 40°N lat. Averaged across sowings, respective observed and simulated oven-dry grain yields (g m−²) were 816 and 830 at Katherine, Australia; 953 and 908 at Gainesville, FL; 1059 and 1106 at Quincy, FL; 1091 and 1119 at Champaign, IL; and 1580 and 1626 at Grand Junction, CO. Temperature primarily affected growth duration with lower temperature increasing the length of time that the crop could intercept radiation. The solar radiation response was related to the amount of incident radiation and to the fraction of radiation intercepted by the crop. In the tropics (Katherine), high temperature decreased the duration of growth and grain yield, despite high levels of radiation. Only at locations with low temperature and consequent long growth duration. and with high radiation were maize yields simulated to be more than 1600 g m−² (300 bushels per acre at 15.5% moisture).
Florida Agric. Exp. Stn. Journal Seiies no. 9749.
Please view the pdf by using the Full Text (PDF) link under 'View' to the left. Copyright © . .
The water balance of soybean (Glycine max), cowpea (Vigna unguiculata), black gram (Vigna mungo), and pigeonpea (Cajanus cajan) grown in pots was studied during a soil drying cycle. The response of the plants was analysed for three distinct stages of dehydration. In stage I, the rate of transpiration remained constant and equal to that of well watered plants even though soil water status fell by more than 50%. Stage II began when the rate of soil water supply to the plant was less than potential transpiration and stomates closed resultingjn the maintenance of plant water balance. When soil water content was expressed as a fraction of transpirable soil water, all species showed a transition from stage I to stage II at a fraction of transpirable soil water of about 0.3 to 0.2. As the soil water declined further, all species had a similar decrease in relative transpiration rate. Consequently, the responses of the four species in stages I and II were essentially identical, except that pigeonpea extracted a slightly greater amount of soil water.
Stage III occurred once stomates had reached minimum conductance and water loss was then a function of the epidermal conductance and the environment around the leaf. Substantial differences were found among the four grain legumes in epidermal conductance. Soybean had the highest conductance, followed by black gram, cowpea and pigeonpea. Substantial variation in dehydration tolerance among the four grain legumes was also found: the ranking of dehydration tolerance based on the relative water content was pigeonpea > cowpea > mungbean > soybean. Differences among the four grain legume species in the duration of stage III which finished when plants died, were consistent with differences in epidermal conductance and in dehydration tolerance of leaves.
Differences in biomass accumulation due to variable nitrogen supply in maize and sorghum grown under irrigation in the semi-arid tropics were associated with differences in both radiation interception and the efficiency with which intercepted radiation was used to produce dry matter. Radiation-use efficiency was more responsive to N supply than was radiation interception. Radiation-use efficiency increased with higher rates of applied N; maximum radiation-use efficiency was greater in maize than in sorghum; and radiation-use efficiency declined during grain filling in maize more than in sorghum. These differences were explained in terms of specific leaf N. A linear relationship, which was similar for both species, was fitted between radiation-use efficiency and specific leaf N. It is concluded that radiation-use efficiency may not be as stable across environments as was previously thought, but rather depends on the balance of leaf growth, N uptake and allocation to leaves, and N mobilization from leaves to grain.
Two theoretical approaches to evaporation from saturated surfaces are outlined, the first being on an aerodynamic basis in which evaporation is regarded as due to turbulent transport of vapour by a process of eddy diffusion, and the second being on an energy basis in which evaporation is regarded as one of the ways of degrading incoming radiation. Neither approach is new, but a combination is suggested that eliminates the parameter measured with most difficulty-surface temperature-and provides for the first time an opportunity to make theoretical estimates of evaporation rates from standard meteorological data, estimates that can be retrospective. Experimental work to test these theories shows that the aerodynamic approach is not adequate and an empirical expression, previously obtained in America, is a better description of evaporation from open water. The energy balance is found to be quite successful. Evaporation rates from wet bare soil and from turf with an adequate supply of water are obtained as fractions of that from open water, the fraction for turf showing a seasonal change attributed to the annual cycle of length of daylight. Finally, the experimental results are applied to data published elsewhere and it is shown that a satisfactory account can be given of open water evaporation at four widely spaced sites in America and Europe, the results for bare soil receive a reasonable check in India, and application of the results for turf shows good agreement with estimates of evaporation from catchment areas in the British Isles.
Twelve sorghum [ Sorghum bicolor (L.) Moench] cultivars were grown in the field at two elevations in Arizona and in two day lengths and five temperature regimes in phytotron greenhouses in an effort to obtain a better understanding of the influence of photoperiod and temperature on time of floral initiation and leaf number in sorghum.
Maturity genotypes were known for seven of the cultivars tested. Alleles at all four maturity loci responded differently to temperature. All cultivars responded to differences in temperature differently in the two photoperiods, indicating interaction between temperature and photoperiod.
Regardless of length of photoperiod, most cultivars produced more leaves in the warmer night temperature regime of 32/29 C than in the 32/23 C regime, but leaf numbers also tended to be high in the cold 17/11 C regime. Plastochrons varied among cultivars.
Sorghum was sown at six dates in a tropical environment and grown under high water and nutrient supply. For each sowing, mean temperature during grain-filling was relatively stable, but across sowings it ranged from 24.6 to 30.2¦C. The effect of this variation on the following was examined: the linear rate of grain growth, the duration of the effective grain-filling period, the rate of increase in harvest index during grain-filling, the rate of development of black layer (as an indicator of maturity) and final grain yield. Contrary to previous findings from controlled environment experiments, the rate of grain growth increased linearly with temperature to 30¦C. However, the duration of grain-filling and the rate of development of black layer were not closely related to mean temperature. Final grain yield was not necessarily adversely affected by high temperature during grain-filling. Also, high grain yield was not necessarily associated with long duration of grain-filling. Similar to the rate of grain growth, harvest index increased linearly during grain-filling. The rate of increase in harvest index was not significantly related to temperature, with a mean value across sowings of 0.0185 day-1. An important implication of the linear increase in harvest index is that grain yield accumulation can be estimated from the crop biomass at any stage of grain growth without knowledge of grain number or the rate of grain growth.
Variation in yield of irrigated grain sorghum (Sorghum bicolor L. Moench) grown during the dry season in tropical Australia was analysed in terms of the amount of photosynthetically active radiation (PAR) intercepted, its efficiency of use in dry matter production and the proportion of dry matter partitioned to grain. Three commercial hybrids (Texas 610SR, Dekalb DK55, Pacific Monsoon) grown under favourable conditions on two soil types (Cununurra Clay and Ord Sandy Loam) yielded similarly, but there was a significant effect of sowing date. Grain yield was highest (9.5 t ha-1 at 14% moisture) in the May sowing, with the lowest yield (7.4 t ha-1) being obtained in the April sowing. Yield was intermediate from a July sowing. Differences in grain yield across the dry season were not related to the amount of PAR intercepted, nor to the efficiency of conversion of intercepted PAR into net aboveground dry matter, but rather to differences in dry matter partitioning. A stable efficiency of conversion of 2.4 g MJ-1 of intercepted PAR was recorded for the entire crop cycle for sorghum crops growing under favourable growing conditions in this environment. This conversion efficiency for a tropical C4 cereal is similar to maximum values, but higher than average conversion efficiencies over the entire crop cycle obtained for temperate C3 cereals growing in temperate regions. Temperature did not affect this conversion efficiency, but had a pronounced effect on the duration of crop development.
Under favourable growing conditions, the source-sink relationships in grain yield of sorghum were analysed in terms of capacities for net assimilation, of head storage, and of the transport system to move assimilates between source and sink. In four commercial hybrids (Dekalb E57, Pacific Goldfinger, Texas 610SR, and Texas 626) grown at three population densities (20.2, 40.4 and 80.8 plants m-1), the assimilates supply was varied by increasing or decreasing the radiation available per plant (by thinning or shading), the potential gram storage capacity was decreased by spikelet removal, and the transport system was reduced by incision of the culm, all manipulations being performed at anthesis. Decreasing the number of grains increased the size of those remaining in all cultivars at all population densities The degree of Increase was greatest for T626 and T610 and least for E57 Thinning increased the grain size In all cultivars, but only sufficiently to cause a significant increase In gram yield In T610 and T626 Reduction in the assimilate supply by shading decreased the gram size and yield in all cultivars. Reduction In the transport system had no significant effect on gram yield. These results showed that there was surplus capacity for storage and transport In all cultivars In T626, T6L0, and Goldfinger, all post-anthesis assimilate was stored as gram and grams could grow larger The yield was therefore completely source-limited In E57, however, not all port-anthesis assimilate was stored as gram, and these grams showed little capacity to grow larger, which suggests that the yield was partially limited by both source and sink.
Male sterility was induced in sorghum by exposing plants to a temperature regime of 18/13°C (day-night temperatures) during meiosis in the pollen mother cells. This normally occurs at the time the last (flag) leaf is emerging and elongating. The majority of genotypes examined were rendered completely male sterile by the low temperature regime. However, some genotypes retained a low degree of pollen fertility. The low temperatures appeared to have little, if any, effect on female fertility. The available evidence indicates that it is the night temperature, rather than the mean temperature, which is critical for the induction of pollen sterility. The potential uses of this method of inducing male sterility in plant breeding and genetics programs are briefly discussed.
The comparative productivity of maize, sorghum and pearl millet subjected to water shortage during different stages of growth was analysed in terms of differences in radiation interception, radiation-use efficiency and dry-matter partitioning to grain. Those environments in the semi-arid tropics to which these species are best suited were defined.Maize out-yielded sorghum and millet under water deficit where maize grain-yield was at least 6 t ha−1, whereas sorghum yielded more than millet and maize where maize yield ranged from 1 to 2 t ha−1. Only where maize produced no grain under water deficit did millet yield the same as sorghum. In millet, grain-yield was more stable than biomass in response to water shortage, but in maize and sorghum biomass was more stable. The decrease in biomass in response to water deficit was associated more with a reduction in radiation-use efficiency than with a decrease in radiation interception, except when the water deficit was imposed during early vegetative growth, when the opposite was the case. Mobilization of pre-anthesis assimilate to grain occurred in sorghum and millet but not in maize. Where water shortage occurred, harvest index was more conservative than biomass accumulation; harvest index was reduced only when water deficits severely decreased grain-yield.
Leaf area dynamics are controlled by genotypic and environmental influences. In this series of papers, general models of leaf area dynamics of uniculm and tillering sorghum (Sorghum bicolor (L.) Moench) at the whole plant and individual leaf levels were developed to examine and quantify both genotypic and environmental controls. Green leaf area was modelled by examining leaf area production and senescence separately. Data from field experiments involving broad ranges of hybrids and environments were collated and analysed. Crops were grown with adequate water and nutrient supply.In this paper, a general framework to model leaf area production at the whole plant level is presented. Accumulation of total plant leaf area (TPLA) (without losses due to senescence) was simulated using a logistic function of thermal time (TT) from emergence and it increased to its maximum value (TPLAmax) shortly before flowering. To calculate TT, base optimum and maximum temperatures of 11, 30 and 42° C respectively were derived by examining the effect of temperature on rate of appearance of fully expanded leaves. Hence, TT incorporated the major effect exerted on TPLA by temperature. Most remaining genotypic and environmental variation in TPLA was related to variation in TPLAmax. Values of TPLAmax were predicted from total leaf number on the main culm (TLN) and fertile tiller number per plant (FTN) by allowing for a curvilinear increase in TPLAmax with TLN and a sequential decrease in total leaf area produced by successive surviving tillers relative to that on the main culm. The potential genotypic and environmental controls on TPLA, introduced via factors affecting TLN and FTN, were considered. Frr seven hybrids grown in eight environments (locations and times of planting), this simple general model accounted for 93% of observed variation in TPLA with time, with a root mean square deviation of 664 cm2 for observed values of TPLA ranging from 161 to 11 302 cm2.
The effect of nitrogen (N) supply on the relative contributions of pre- and post-anthesis net above-ground biomass accumulation and N uptake to grain-yield and grain N concentration was examined in four contrasting environments in semi-arid tropical Australia. The four environments had different radiation and temperature regimes, and varying levels of water deficit. The grain-yield achieved under high N supply ranged from 156 to 621 g m−2 (on an oven-dry basis).In all but the lowest-yielding environment, there was substantial biomass accumulation during grain-filling and it increased with N application. Only in the lowest-yielding environment was there substantial mobilization of pre-anthesis biomass to grain. Biomass mobilization was not affected by N application. Nitrogen uptake during grain-filling was unresponsive to N application, and was small relative to total N uptake during the life-cycle. Mobilization of pre-anthesis N to the grain was much more significant. In all but the lowest-yielding environment, N mobilization increased with N application.Grain-yield under variable N supply and differing environmental conditions was not dependent on the proportions of pre- and post-anthesis growth. However, grain-yield was proportional to biomass at maturity over the entire yield range in this study and variability in biomass accounted for 95% of the variance in grain-yield. Similarly, grain N concentration was not related to the proportions of pre- and post-anthesis N uptake, but variability in total N uptake accounted for 92% of the variance in grain N accumulation. Consequently, there was no differential effect of N supply or environmental factors on yield physiology that could not be explained by their effect on biomass and N uptake.
Rainfed crop production in the subtropics is a risky enterprise due to high rainfall variability. When planting opportunities occur, farmers face risky choices because the consequences of decisions made at planting are uncertain. This paper presents a general approach to generating the information required to assistt in making planting decisions in climatically variable subtropical environments. The approach involved coupling a sorghum growth simulation model to long-term sequences of climatic data to provide probabilistic estimates of yield for the range of decision options, such as planting time and cultivar maturity, for a range of soil conditions. The likely change in the amount of stored soil water with delay in planting was also simulated to account for the decision option of waiting for a subsequent planting opportunity. The approach was applied to three locations (Emerald, Dalby and Roma) in subtropical Australia. Production risk varied with location, time of planning, soil water storage, and cultivar phenology. Yield responses to these factors were associated closely with differences in leaf area development and degree of depletion of the water resource. The probabilistic estimates of yield and change in stored soil water provided in this paper can assist decision-makers with risky choices at planting in subtropical environments. Such information can be used in decision analysis or in computerized decision support, where decision-makers, and their risk preferences, can interact directly with the information.
The effects of fallow surface management treatments on stubble (crop residue) levels and soil water storage were studied during seven fallow periods between grain sorghum crops on a grey Vertisol near Biloela in central Queensland, Australia. Treatments were disc (D), blade (B) and zero (Z) tillage, each with stubble or residue from preceding crops either retained (+) or removed (-) at the start of the fallow periods, which were of 7-8 months duration. Where stubble was retained, stubble dry matter levels on the soil surface at the start of the fallow period were mainly influenced by stubble produced by the previous crop, but also by residual stubble on the soil surface before the previous crop. The general order was D +< B +< Z+. Stubble dry matter and stubble cover on the soil surface declined during the fallow period in all stubble-retained treatments, with the greatest reductions occurring after the initial disc or blade tillage. From the start to end of the fallow, mean reductions in stubble dry matter and stubble cover were, respectively, 60 and 74% in D+, 31 and 57% in B+, and 17 and 24% in Z+. Mean stubble dry matter levels on the soil surface at the end of the fallow period in December-January were 1030, 2030 and 2910 kg ha-1 in D+, B+ and Z+, respectively; corresponding stubble cover levels were 8, 16 and 35%. Mean plant available water capacity to 1.8 m was 201 mm. Mean fallow soil water accumulation varied between fallow periods from 11 to 102 mm. The corresponding variation in mean fallow water storage efficiency (percentage of rainfall over the fallow stored in the soil) was from 3 to 37%. Fallow soil water accumulation was significantly (P<0.05) higher in Z+ (116 mm) than in Z- (86 mm), D+ (96 mm) and D- (84 mm) in one fallow period. During the fallow period, B+ and Z+ generally resulted in higher plant available water than other treatments at mean values of 50-100 mm. However, these effects were not present at higher plant available water levels (mean of 128-164 mm), as occurred at the end of six fallow periods. The main treatment effect at the end of the fallow was for significantly (P<0.05) lower plant available water in Z-.
The supply of water provided by the root system of a crop stand is defined in terms of the rate at which water is extracted by a root front moving downwards with a constant velocity, the available water per unit soil volume, and a time constant that is inversely proportional to root density. The demand for water, often identified with a potential transpiration rate, is defined in terms of a maximum crop growth rate multiplied by the conservative ratio of transpiration to dry-matter production. From experimental evidence, supported by theory, this ratio is proportional to the mean saturation vapour-pressure deficit. As hypothesized, the root front accelerates during seedling establishment to keep demand and supply in balance. Once a maximum root velocity is reached (ca. 2-4 cm d-1) transpiration is limited by water supply, except when the soil behind the root front is wetted by rain or irrigation, when it is limited by demand. Irrigation amounts and timing can both be estimated from this scheme.
(…) Development rate of all hybrids exhibited a curvilinear response to temperature in both phases. Old and new hybrids differed in their temperature responses in GS1 but were similar in GS2. New hybrids had slower rates of development at all temperatures, but the difference was greater at higher temperature (>25 o C). All hybrids had similar short-day photoperiodic response in GS1, with a critical photoperiod 13.2 h. The models were tested on a separate data set covering a similar broad range of environments and performed well
This paper reports on the development and evaluation of a grain sorghum model (CERES-Sorghum (SAT) for use in the semi-arid tropics. The model was developed from a version of CERES-Maize, previously adapted for use in this climatic zone. Functions for phenology, leaf growth, leaf senescence, assimilate accumulation and grain growth were modified using a small subset of sorghum data and validated against a much larger field-data set. When tested with cultivar De Kalb DK55 at Katherine, Northern Territory, the model successfully predicted grain-yield with a root mean square deviation of 0.972 to ha−1 over a range of sowing dates and water regimes resulting in observed yields ranging from 1.56 to 6.28 t ha−1. Deviations of predicted from observed yields were no greater than those of maize predictions by the parent model. Prediction of components of yield and biomass were also satisfactory. Calibration required 28 changes to the CERES-Maize (SAT) model, of which 15 were changes to coefficients in equations rather than substantial changes to the model. Because of the ease of conversion and the time-use efficiency found in these analyses, the techniques used in this paper could have application where locally calibrated models are required.
Wheat cropping in the northern sector of the Australian wheat belt has been expanding into a region with a more marginal moisture regime and a more variable climate than in the established cropping regions. To provide a sound basis for land use assessment, the likely reliability of cropping in this region was examined. Reliability included the likelihood of planting a wheat crop in any year and the likely yield of planted crops. Simulation studies, using an appropriate model of the cropping system and long-term rainfall records (92-year period), were used to derive yield probability distributions for sites throughout the region. The main features of the cropping system model developed are outlined. The yield probability distributions and associated economic analyses indicated that expansion of wheat cropping in this region was likely. Trends in simulated yield sequences were compared with analyses of factors associated with recent climatic change. Similarities of patterns suggested an association of rainfall and yield trends with climate forcing factors. Implications of this association are discussed. A better understanding of the action of the climate forcing factors is required before possible climatic change can be included in determining reliability of cropping.
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.
A study is described in which cultivars of 44 species of field crops were sown at monthly intervals at three site in eastern Australia, and observations made on the duration of the developmental phases. The aim of the work was to devise models and estimate parameters for predicting phasic development in whole crop simulations. In this paper we describe the effect of temperature on the time from sowing to emergence. A number of different models was investigated including linear and non-linear equations using daily mean temperature. We also compared the use of soil temperature with screen temperature and the use of daily mean temperatures with temperatures measured at 2-h intervals. Of all the systems used the modified linear day-degree system with daily steps of mean screen temperature was found to be the most satisfactory for our purpose and not significantly inferior to any other model tested.Parameters of equations for predicting emergence for 30 of the 44 species are presented. The other 14 are temperate species which apparently suffered delayed emergence in our experimental conditions.The results are discussed in terms of the ecological and physiological significance which may be placed on the base temperature used in the day-degree system.
A simple, phenomenological model was developed to describe the carbon, nitrogen, and water budgets of a soybean (Glycine max (L.) Merr) crop from emergence to maturity. Daily meteorological data input to the model were minimum temperature, maximum temperature, solar radiation, and precipitation. Leaves were grown to give a linear function of mean daily temperature. From the calculated leaf area index the daily intercepted solar radiation by the crop was determined. Daily carbon accumulation was calculated as a linear function of intercepted radiation. The daily nitrogen accumulation rate was calculated as a linear function of vegetative biomass. Leaf growth, carbon accumulation and symbiotic-nitrogen fixation rates were all made sensitive to the amount of soil water. Experimental data were collected to develop the empirical relationships between these three physiological processes and the amount of transpirable soil water. These data showed nitrogen fixation rates to be more sensitive to soil dehydration than either leaf growth or leaf gas exchange. Preliminary tests showed that model predictions compared favorably with field observations. An analysis of the model's behavior showed that crop yield was most sensitive to those variables influencing the interception of solar radiation and its conversion to biomass.
Crop simulation models are proposed as tools for agricultural risk analysis in order to explore potential cropping locations and appropriate farming systems in the semi-arid tropics. This study takes the initial step of independently validating the STANDARD CERES-Maize simulation model in the semi-arid tropics, and reports some modifications made to improve its performance. The CERES-Maize model did not accurately predict grain-yield of cultivar Dekalb XL82 which was grown over a range of sowing dates and water regimes at Katherine, N.T. Experimental yields (at 15.5% moisture) ranged from 0 to 9840 kg ha−1. Calibration of CERES-Maize reduced the root mean square deviation (rmsd) for observed grain-yields from 3480 to 2015 kg ha−1. Functions describing phenology, leaf growth and senescence, assimilate production and grain growth were revised and validated against field data. The revisions to CERES-Maize not only provide a model more applicable to the semi-arid tropics but also identify the parameters that may require calibration for other maize genotypes and locations in this climatic zone. Further validations of the functions describing nitrogen cycling and rainfall infiltration and runoff are required to increase the model's applicability to risk-analysis studies.
A sorghum simulation model, SORGF, was revised for use in the semi-arid tropics. As a result of the revisions in the model, the correlation coefficient between observed and simulated grain yield of sorghum (n=59) increased from 0.52 to 0.86. Comparison between simulated and observed grain yields showed that the SORGF model can be used to estimate sorghum yields with reasonable accuracy before harvest. Responses of sorghum to drought-stress and to changes in plant density were simulated. The correlation coefficient between observed and simulated sorghum grain yield data pooled over five levels of plant density and two cultivars was 0.91. The correlation coefficient between observed and simulated sorghum grain yield data pooled over two water treatments, two cultivars, and two seasons was 0.92. The model was used to compute the probabilities of simulated sorghum grain yield and the requirements of N-fertilizers based on 30 years of climatic data for four locations in India.A simulation model for pearl millet was developed following an approach similar to that of SORGF. The pearl millet model was tested against independent data; further testing of the pearl millet model is required before its application.
Under water-limiting conditions, water extraction by a dryland crop is limited by the depth of the root system, and by the rate and degree of water extraction. The water extraction pattern of 6 crops of grain sorghum under continous soil drying in a sub-humid sub-tropical environment was analysed in terms of two components: the rate of descent of the extraction front down the soil profile (extraction front velocity), and the time required to extract 90% of the extractable water from each depth after the extraction front arrived (1/kl90). Extractable water content (θa), at each depth, was defined as the difference between the stable water content (θ) at the start of extraction and the lower asymptote of the exponential decay curve of θ versus time (lower limit). The crops varied in genotype, level of evaporative demand, degree of tillering, and plant population density, and were grown on the same soil type over two seasons. The aim of the study was to test the stability of the extraction front velocity, θa and of 1/kl90 under different agronomic and environmental conditions, to assess their usefulness for modelling water extraction of sorghum.
This is the third paper in a series that examines the genotypic and environmental controls of leaf area dynamics in grain sorghum. In this paper, genotypic variation in leaf area senscence was firstly quantified both at the whole plant level and at the level of individual leaves. Green leaf area was then simulated at both levels, and predictions were compared with independent data on crop leaf area index.At the whole plant level, senesced plant leaf area (SPLA) was modelled as a logistic function of thermal time after emergence, where the asymptote of the relationship is determined by maximum leaf area produced per plant. At the level of individual leaves, where leaf size was predicted, SPLA was simulated by a constant rate of senescence of individual leaves. The relationship at the two levels of simulation were both affected by different leaf numbers per plant. While there were genotypic differences in the senescence of sorghum leaf area, the genotypic control of sorghum leaf area dynamics was generally less influential than the effect of the environment.Relationships to predict SPLA were added to those to predict total plant leaf area (TPLA), previously developed in this series, to enable prediction of green leaf area development of sorghum crops. There was little difference in the accuracy with which the models at the whole plant and individual leaf levels simulated independent data. The model at the whole plant level was simpler and required less initial information. The model at the level of individual leaves was more complex and needed greater information, but provided a more detailed treatment of leaf area dynamics. The choice between the two approaches will depend to a large degree on the demands of the study for which a sorghum leaf area model is to be applied.
Crop adaptation of grain sorghum in semi-arid environments — a modelling approach
- Hammer
Modelling adaptation and risk of production of grain sorghum in Australia
- Hammer
Water extraction by dryland grain sorghum
- Robertson
Water extraction by field-grown grain sorghum
- Robertson
The effect of planting date and environment on the phenology and modeling of grain sorghum, Sorghum bicolor (L.) Moench
- Schaffer
Evaluation of the CREAMS model. III. Simulation of the hdyrology of vertisols
- Silborn