No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A Sunflower Simulation Model: I. Model Development
Agronomy Journal
Abstract
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 [...]
... Furthermore, it has been shown that dHI/ dt remains stable over a range of growth conditions such as variations in sowing date, irrigation treatments and N level (Moot et al., 1996;Bindi et al., 1999;Lecoeur and Sinclair, 2001). The strength of this approach lies in its simplicity, intrinsically combining the contribution of current and stored assimilate to seed yield and so removing the need for complex predictions of seed number and size in the prediction of seed yield (Chapman et al., 1993). Use of a constant dHI/dt has proved effective and robust in a number of crop simulation models including soybean (Sinclair, 1986), maize (Muchow et al., 1990), wheat (Amir and Sinclair, 1991), sunflower (Chapman et al., 1993), and chickpea (Soltani et al., 1999). ...
... The strength of this approach lies in its simplicity, intrinsically combining the contribution of current and stored assimilate to seed yield and so removing the need for complex predictions of seed number and size in the prediction of seed yield (Chapman et al., 1993). Use of a constant dHI/dt has proved effective and robust in a number of crop simulation models including soybean (Sinclair, 1986), maize (Muchow et al., 1990), wheat (Amir and Sinclair, 1991), sunflower (Chapman et al., 1993), and chickpea (Soltani et al., 1999). ...
... 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 impact of climate change on agricultural productivity is as important to understanding prehistoric subsistence as it is to today's economic landscape. Researchers studying potential yield of modern crops use a variety of climate variables, such as temperature, precipitation, solar radiation, etc. [1][2][3][4][5]. Data for these variables are often recorded as daily measurements. ...
... Because weather information including daily maximum and minimum temperature, solar radiation, and precipitation represents key input data for agricultural crop models, weather generators originated as tools to evaluate the impacts of climate change on crop growth and yield [1][2][3][4][5]14,15]. Precipitation is usually stochastically generated first, because it is argued that it affects the statistics of many other climatic variables to be stochastically generated [16]. The traditional method for generating daily precipitation is to use a Markov chain to simulate the occurrence of wet or dry days and then to utilize a gamma distribution function to approximate the precipitation amount on a wet day [17][18][19][20]. ...
A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values.
... where Y m is the grain yield under Scenario 2, and Y a is the yield under Scenario 1. The value of A j at each phase is shown in Table 2. SWDP d is calculated as the ratio of daily water supply in the root zone to crop demand [21]: ...
... where TE crop is the transpiration efficiency coefficient for above-ground biomass and set to 0.006 g m −2 mm −1 for wheat in APSIM, and α CO2 is the transpiration efficiency adjusting coefficient for the atmospheric CO 2 concentration. It linearly increases from 1 at 350 ppm to 1.37 at 700 ppm [16,17,21]. Q p was calculated as ...
Southwestern China (SWC), one of the major rain-fed wheat production zones in China, has become vulnerable to drought in recent years under global climate change. To quantify drought severity during the wheat growing season and its impact on yield loss, we selected the Agricultural Production Systems sIMulator (APSIM) model to simulate wheat growth between 1961 and 2010 in SWC. A new drought index was developed considering different weighting factors of drought for yield loss in three growing phases. The index was shown to be reliable in assessing drought severity in the region. On average, an abnormal drought mainly occurred in mid-west Guizhou with a frequency of 10–30%. Central SWC was subjected to moderate drought with a frequency of 10–30%, whereas severe drought often occurred in Southern Sichuan and the middle of Yunnan with a frequency >50%. Temporally, drought severity fluctuated before 1990, but increased significantly afterwards. Our assessment suggested that irrigation during the period from floral initiation to flowering would help to ameliorate the effects of water stress under climatic variability in the region.
... Implementation of APSIM-sorghum into this template illustrated that it can capture genotypic differences in traits as emergent consequences of differences in the supply-demand balance among individual organs (Hammer et al., 2010). The APSIM maize module, which was originally developed from a combination of approaches used in CM-KEN , CM-SAT (Carberry et al., 1989;Carberry and Abrecht, 1991), and CERES-Maize (Jones and Kiniry, 1986), with some features of the maize model of Wilson et al. (1995), and the water and light resource use efficiency concepts as implemented in the sunflower model of Chapman et al. (1993), was redesigned into this generic template. ...
... Above-ground biomass accumulation is simulated as a function of resource capture and resource use efficiency and is either light-limited or water-limited (Chapman et al., 1993;Hammer et al., 2010). In the absence of drought stress, biomass production is the product of intercepted radiation and canopy RUE, which is set to 1.9 g MJ −1 short wave solar radiation (Lindquist et al., 2005). ...
Crop growth simulation models require robust ecophysiological functionality to support credible simulation of diverse genotype × management × environment (G × M × E) combinations. Most efforts on modeling the nitrogen (N) dynamics of crops use a minimum, critical, and maximum N concentration per unit biomass based empirically on experimental observations. Here we present a physiologically more robust approach, originally implemented in sorghum, which uses the N content per unit leaf area as a key driver of N demand. The objective was to implement the conceptual framework of the APSIM sorghum nitrogen dynamics model in APSIM maize and to validate the robustness of the model across a range of G × M × E combinations. The N modelling framework is described and its parameterisation for maize is developed based on three previously reported detailed field experiments, conducted at Gatton (27°34'S, 152°20'), Queensland, Australia, supplemented by literature data. There was considerable correspondence with parameterisation results found for sorghum, suggesting potential for generality of this framework for modelling crop N dynamics in cereals. Comprehensive model testing indicated accurate predictions at organ and crop scale across a diverse range of experiments and demonstrated that observed responses to a range of management factors were reproduced credibly. This supports the use of the model to extrapolate and predict performance and adaptation under new G × M × E combinations. Capturing this advance with reduced complexity compared to the N concentration approach provides a firm basis to progress the role of modelling in exploring the genetic underpinning of complex traits and in plant breeding and crop improvement generally.
... The evidence of deferential genotype responses to ambient temperature and other climatic parameters is limited in barley [21]; thus, the knowledge of genotype x environment or QTL× environment as well as management (G × E × M) interactions is required to obtain higher grain yields and quality [72]. The use of crop simulation modelling to predict expression of complex crop traits under diverse environments has provided plant breeders and farm managers with good opportunities to make crucial decisions such as matching the choice of genotypes to an appropriate sowing window, soil type, available soil water and other environmental conditions [73]. Therefore, integration of experimentally determined genetic responses to photoperiod, vernalization and Eps will complement plant breeder's use of genetics and molecular tools in the understanding and prediction of flowering time and the understanding of how these genes affect grain yield and quality in different climates and management. ...
... Thus, ecophysiological model could be very important for dissecting the relationship between genotype and phenotype [24]. Chapman et al. [73] also developed mechanistic model called QSUN to estimate growth, development and yield of a diverse range of genotypes of sunflower under varied environments. Their model was able to account for leaf area index (r 2 = 0.65), total biomass (r 2 = 0.96), and grain yield (r 2 = 0.93) when tested against actual phenological data. ...
Barley heading date is important in adapting barley genotypes to different environments. Important factors affecting heading date in barley are temperatures, photoperiod and sowing date. Sowing date is a management option to influence heading date. It is used to reduce climatic risks such as frosts and water stress at sensitive periods during crop development. Three major genes control heading date in barley. These genes regulate photoperiod (Ppd-H1 and Ppd-H2), vernalization (Vrn- H1, Vrn-H2 and Vrn-H3) and the duration of the vegetative phase (Eps). The Ppd-H1 locus on chromosome 2(2H) regulates flowering time under long days. Ppd-H2 on 2H regulates phenology under short day length. Vernalization is mainly controlled by three loci (VRN-H1, VRN-H2 and VRN-H3), which interact in an epistatic fashion to determine vernalization sensitivity. Appropriate physiological and simulation frameworks such as that of APSIM-Barley are required to complement breeding efforts in order to determine the location of the Eps genes and describe and quantify the effects of environment and management on gene expression and their impact on yields and quality in barley.
... In modeling studies exploring impacts of climate change on crop productivity (Lobell et al., 2015), RUE is increased with elevated C a in C 3 species but is not changed in C 4 species, in line with the known differences in response to photosynthesis. In water-limited situations, RUE is reduced in line with the relative transpiration achieved by the crop, which is determined from the balance between atmospheric demand and soil water uptake (Chapman et al., 1993). The RUE approach is a simpler way to model crop growth than the PLR models (used in the first type of canopy photosynthesis modeling), but it involves invoking a number of empiricisms to deal with responses to environmental factors and crop physiological attributes of the crop, which can reduce the predictive power of the RUE approach. ...
... When water becomes limiting, some crop models drive crop growth via transpiration, which is estimated by the balance between atmospheric demand and crop soil water uptake (Monteith and Greenwood, 1986;Monteith, 1988;Hammer et al., 2010). The switch between light-limited and water-limited crop growth depends on the estimated plant water status (Chapman et al., 1993;Hammer et al., 2010). A more mechanistic method involving the coordination of the controls of stomatal aperture, transpiration and abscisic acid (Tardieu et al., 2015) can be used to estimate transpiration, but the simpler approach for estimating transpiration in crop models has proven to be robust when applied across a diverse range of environments . ...
The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation.
... Both types include a model to simulate sunflower, CROPGRO Sunflower (Tariq et al., 2018) and OILCROP-SUN (Villalobos et al., 1996), respectively. It is relevant to point out that other sunflower simulation models have been developed but are not available in DSSAT (Kiniry et al., 1992;Chapman et al., 1993;Pereyra-Irujo and Aguirrezábal, 2007;Casadebaig et al., 2011). ...
Problem: The Decision Support System for Agrotechnology Transfer (DSSAT) contains a sunflower model based on CROPGRO. This model assumes some parameters values out of the crop species variability. Besides, this model has not been assessed for simulating grain yield and grain oil content in contrasting environments. Objective: The main goal of this study was to generate and test a revised CROPGRO-Sunflower model. In addition, we used the revised CROPGRO-Sunflower to quantify crop responses to environmental and management variables. Methods: Three sunflower models: a revised CROPGRO-Sunflower, the original CROPGRO-Sunflower and the OILCROP-SUN were calibrated and evaluated across contrasting environments. We compared the revised CROPGRO-Sunflower with the original CROPGRO-Sunflower and OILCROP-SUN in terms of ability to simulate crop development, growth, grain yield and grain oil content. Crop responses to soil depth, sowing date, and El Niño-Southern Oscillation (ENSO) effects were quantified using the revised CROPGRO-Sunflower in combination with climatic records for 37 growing seasons to simulate yield in two contrasting environments of Argentina: Balcarce and Reconquista. Results: Crop growth, grain yield and grain oil content were better simulated by the revised CROPGRO-Sunflower than by OILCROP-SUN. Simulated yield had a root mean square error (RMSE) of 48 g m-2 with revised CROPGRO-Sunflower and of 119 g m-2 with OILCROP-SUN. Moreover, RMSE for simulated grain oil concentration was 2% for revised CROPGRO-Sunflower and 11% for OILCROP-SUN. Deep soils and late sowing dates resulted in higher grain yield at Balcarce. Sowing date did not affect grain yield at Reconquista. An effect of the ENSO phases on sunflower grain yield was found. "La Niña" phase was associated with the lowest grain yields at both sites. Conclusions: Modifications made to the original CROPGRO-Sunflower improved model performance. The revised CROPGRO-Sunflower model can be utilized to simulate crop phenology, growth, grain yield and grain oil concentration over a wide range of environmental conditions. Implications: This calibrated and evaluated crop simulation model will allow to advance in the quantification of yield gaps and to study the impact of other management practices on sunflower crop production.
... Runoff is calculated using a simplified curve number procedure developed by USDA-SCS (Williams 1991;Chapman et al. 1993). Curve numbers are assigned to various soils depending on their texture and then modified to take account of ground slope, crop cover and soil water content (in the top 600mm), and rainfall intensity. ...
Reducing water evaporation from the soil surface of the cropland is a way to save irrigation water and improve water efficiency. For the first time, we calculated how much water can be saved at the level of a country by reducing soil evaporation and runoff and whether this reduction has an effect on plant production. To determine the effects of reducing soil water evaporation and runoff on crop productivity a modeling system was implemented. The model setup included 2750 combinations of “plant - production conditions - province” spanning 2000 to 2015 (44,000 simulations for each scenario). There were different combinations of reduced soil water evaporation and reduced runoff included in seven scenarios. Approximately 81.7 billion m3 of water was irrigated annually in Iranian crop production, which markedly decreased with a decrease in soil evaporation. For various species groups, the net irrigation water requirement (NIWR) and the gross irrigation water requirement were calculated. The highest NIWR was observed for oil grains (1403 m3t−1), and the lowest values were obtained for vegetables (111 m3t−1). A proper water management under irrigated systems can save an estimated 8.3 billion m3 of water per year at country level. Water savings of this magnitude is comparable with water consumption in industries and urban areas in Iran. In rainfed systems, water productivity increased under different scenarios of reduced soil water evaporation. This system can be useful in long-term planning of agriculture that is often determined by political decisions made by the nations themselves, not by world politics.
... We utilized the first-order derivative of the logistic function to describe the grain-filling rate. This method has been used to model leaf areas for a variety of different crops (Chapman et al., 1993;Setiyono et al., 2008). In addition, the model can simulate the grain-filling process with different levels of N fertilizer application. ...
Drip‐irrigation is a cultivation mode that uses water and fertilizer efficiently and is the main cultivation technique for wheat (Triticum aestivum L.) production in the Xinjiang Uyghur Autonomous Region, China. The amount of nitrogen (N) fertilizer used in drip‐irrigated wheat production is strongly connected to land productivity. However, the effect of reduced N application on the translocation of assimilates to grains and grain‐filling in wheat under drip irrigation is unknown. A 2‐yr field experiment (2018–2019) was conducted in the Xinjiang Uygur Autonomous Region with two varieties of spring wheat (Xinchun 38 [XC38] and Xinchun 49 [XC49]) and five N application levels (300 [N1], 275 [N2], 250 [N3], 225 [N4], and 0 [N0] kg hm⁻²). The results showed that N application significantly affected the dry matter accumulation, transport amount, transport rate, and the contribution rate of wheat (stem sheaths, leaves, and spikes). The maximum values of the above parameters of XC38 and XC49 were observed in response to N3 and N4, respectively. In addition, N affected the dry weight and grain‐filling characteristic parameters. Nitrogen affected the rate of grain‐filling during the three periods, with N3 and N4 being the best treatments for XC38 and XC49, respectively. The results of this study provide an understanding of the translocation of assimilates and the grain‐filling characteristics of spring wheat, and the optimum N application levels were different for XC38 and XC49. Among the N treatments in this study, the optimum nitrogen levels were 250 and 225 kg hm⁻² for XC38 and XC49, respectively.
... This framework proposes that economic yield depends upon the amount of RAD that the crop actually absorbs (ARAD) or intercepts (IRAD) during the growing season, its conversion into crop biomass, which is commonly referred to as radiation use efficiency (RUE), and its partitioning into harvestable organs (harvest index) (Kiniry et al., 1989;Monteith et al., 1977;Sinclair and Muchow, 1999). The RUE, estimated based on ARAD (ARUE) or IRAD (IRUE) depending upon the study, has been used as a parameter for estimating crop productivity using remote sensing (Garbulsky et al., 2011), simple empirical models (Monteith, 1972), and process-based simulation models (Jones and Kiniry, 1986;Sinclair, 1986;Chapman et al., 1993;Villalobos et al., 1996). Despite the intrinsic empiricism of the RUE concept, which integrates multiple processes and scales, it does provide a useful framework to evaluate hypotheses related to crop traits such as leaf area, photosynthesis, and biomass partitioning (Reynolds et al., 2000;Koester et al., 2014;Chen et al., 2019;Araus et al., 2021). ...
Ontogenic changes in soybean radiation-use efficiency (RUE) have been attributed to variation in specific leaf nitrogen (SLN) based only on data collected during seed filling. We evaluated this hypothesis using data on leaf area, absorbed radiation (ARAD), aboveground dry matter (ADM), and plant nitrogen (N) concentration collected during the entire crop season from seven field experiments conducted in a stress-free environment. Each experiment included a full N treatment that received ample N fertilizer and a zero N treatment that relied on N fixation and soil N mineralization. We estimated RUE based on changes in ADM between sampling times and associated ARAD, accounting for changes in biomass composition. The RUE and SLN exhibited different seasonal patterns: a bell-shaped pattern with a peak around the beginning of seed filling, and a convex pattern followed by an abrupt decline during late seed filling, respectively. Changes in SLN explained the decline in RUE during seed filling but failed to predict changes in RUE in earlier stages and underestimated the maximum RUE observed during pod setting. Comparison between observed and simulated RUE using a process-based crop simulation model revealed similar discrepancies. The decoupling between RUE and SLN during early crop stages suggests that leaf N is above that needed to maximize crop growth but may play a role in storing N that can be used in later reproductive stages to meet the large seed N demand associated with high-yielding crops.
... The Agricultural Production Systems Simulator (APSIM; Holzworth et al., 2014) software platform is a crop growth model that combines biophysical and management modules to simulate crop growth for a given genotype by environment by management combination, without considering pests or diseases. In this study, APSIM-Maize (Keating et al., 2003), APSIM-Mungbean (Robertson et al., 2002) and APSIM-Sunflower (Chapman et al., 1993) modules included in APSIM (version 7.10) were used to simulate salinity conditions for the two different clusters and their impacts on crop yield under historical weather conditions. Long-term simulations were performed over 55 years of historical weather data (starting from January 1, 1965, to January 1, 2020). ...
CONTEXT: Soil salinity is the main environmental limiting factor for current agricultural farming systems in the Bangladesh coastal zone. Targeting crop selection to ameliorate its impact in food production is a priority to ensure food security, diversifying crop outputs and reducing smallholders' vulnerabilities. In this context, crop growth modelling emerges as a useful tool for exploring different scenarios for crop selection.
OBJECTIVE: Studying spatial and -temporal heterogeneity in salinity and crop productivity under different climate scenarios.
METHODS: A dataset from the Polder 30 located in the Bangladesh southwest coastal zone, including different farming systems (rice-maize, rice-mungbean, rice-sunflower) comprising two years (2017–2019), was utilized, together with polder-level contiguous layers of factors (i.e., salinity, elevation, precipitation), to create clusters representing different yield-limiting conditions. Two salinity scenarios were defined and included in a long term in-silico assessment to explore dry-season field crop options (maize, sunflower, and mungbean). APSIM models for maize, sunflower and mungbean, were selected to perform all the crop simulations.
RESULTS AND CONCLUSIONS: Based on yield, sunflower was the most stable choice under salinity and maize was the best under non-saline conditions. Lastly, yield stability was accounted for different climate conditions, showing a large yield reduction for maize when combining low precipitation and high temperature, whereas mungbean and sunflower presented less sensitivity to different climates.
SIGNIFICANCE: Right crop selection for the right environment, considering both salinity restrictions and climate uncertainties, is critical for long-term yield stability. The present study provides a novel framework to better match the most suitable crop under challenging salinity restrictions and climate uncertainties affecting small- holders in the coastal zone of Bangladesh.
... The Agricultural Production Systems Simulator (APSIM; Holzworth et al., 2014) software platform is a crop growth model that combines biophysical and management modules to simulate crop growth for a given genotype by environment by management combination, without considering pests or diseases. In this study, APSIM-Maize (Keating et al., 2003), APSIM-Mungbean (Robertson et al., 2002) and APSIM-Sunflower (Chapman et al., 1993) modules included in APSIM (version 7.10) were used to simulate salinity conditions for the two different clusters and their impacts on crop yield under historical weather conditions. Long-term simulations were performed over 55 years of historical weather data (starting from January 1, 1965, to January 1, 2020). ...
... En el ámbito de la teledetección por SAR, en cultivos, resulta de gran importancia contar con un modelo realista de la vegetación. Si bien existen modelos de simulación para el cultivo de girasol, la mayoría están orientados fundamentalmente a variables de interés agronómico [8][9][10][11][12]. En este caso particular, se requiere analizar el comportamiento del girasol desde el punto de vista dieléctrico. ...
En este trabajo se presenta un modelo de crecimiento basado en la parametrización de las dimensiones de los distintos órganos de un cultivar de girasol (Helianthus annuus L.), en función de la altura del tallo, en condiciones de producción para la zona NE de La Pampa. La particularidad que presenta este modelado es que está orientado a analizar el comportamiento electromagnético del cultivo para el desarrollo de aplicaciones a partir de imágenes de radares de apertura sintética (SAR), en el marco de la puesta en órbita del primer satélite argentino SAR (SAOCOM 1A). Se realizó un exhaustivo trabajo de campo para lograr mediciones en situación de producción, y se obtuvieron las distribuciones estadísticas de las variables relevadas (tallo, pecíolos y hojas) durante todo un ciclo productivo. A partir de los datos, se encontraron discrepancias significativas con algunos de los modelos vegetales incluidos en los códigos de transporte de microondas utilizados para simular el coeficiente de backscattering de radar (sigma0). En particular, estas discrepancias son notables para las dimensiones asociadas a los pecíolos que, dada la longitud de onda de operación del SAOCOM, pueden resultar en variaciones significativas de sigma0. En este caso, se propone un ajuste de los datos mediante funciones logísticas, habiéndose obtenido correlaciones alt-mente significativas.
... The SDR is outlined as the ratio between water supply (WS) and water demand (WD) and was capped between 1.0 (no water stress) and 0.0 (no water available to the crop). More details about the SDR can be found in Chapman et al. (1993) and Chenu et al. (2013). ...
The current study evaluated the development and growth of three major rapeseed genotypes (Hyola308, Hyola401, and RGS003 as early-, mid-, and late-maturity genotypes, respectively) as well as seed yield under different irrigation regimes (full irrigation, withholding irrigation at the flowering stage, withholding irrigation at the pod initiation stage, and withholding irrigation at the seed filling period) and also the spatial yield potential. APSIM-Canola model was applied to investigate the response of rapeseed genotypes to irrigation regimes in ten locations. Simulated results indicated that yield potential for rapeseed production was higher in the west which is a temperate agro-climatic zone (2852.6 kg ha − 1) than in the southwest which is a hot agro-climatic zone (1885.1 kg ha − 1). Although Hyola401 (the mid-maturity genotype) had the maximum seed yield (2798.4 kg ha − 1), RGS003 (the late-maturity genotype) was found to be more drought-resistant due to a lower decrease in seed yield (18.1 %) under water-limited conditions compared with full irrigation conditions. The current findings suggest that the mid-maturity genotype has more yield potential in the studied locations (with different climates and soils) under full irrigation conditions due to higher seed yield, and the late-maturity genotype can be suggested as a resistant genotype for future breeding programs to introduce new-high-yielding genotypes with high drought tolerance, especially in drought-prone environments. Furthermore, withholding irrigation at seed filling onwards, which showed the lowest decrease in seed yield (13.6 %), can be recommended as a strategy for water-saving at the end of the growing season, and farmers can allocate irrigation water to other crops.
... A wide range of crops can be modelled in APSIM. Of relevance for this study are maize (Zea mays) (Carberry and Albrecht, 1991), pearl millet (Pennisetum glaucum) (van Oosterom et al., 2001), sunflower (Helianthus annuus L.) (Chapman et al., 1993), oats (Avena sativa), and the legumes soybean (Glycine max) and cowpea (Vigna unguiculata) (Robertson et al., 2002). While APSIM has been applied globally, many studies since the early 1990s have been conducted for these crops in sub-Saharan Africa (Keating and Thorburn, 2018); for a specific overview on crop modelling in southern Africa see Whitbread et al. (2010); for cowpea (Ncube et al., 2009;Sennhenn et al., 2017), soybean (Mabapa et al., 2010), pearl millet (Akponikpè et al., 2010), and maize (Rurinda et al., 2015). ...
Diversification of cropping is perceived as a strategy to simultaneously achieve high productivity and maintain environmental sustainability. In southern Africa, however, due to a lack of medium to long-term field trials, there is missing quantitative information. Utilising the capability of agro-ecosystem models to quantify the interactions of crop productivity with management and environmental variables, the APSIM model was evaluated against six and an eight-year field trial datasets comprised of different crop rotations and fertiliser rates under two contrasting agro-ecological conditions in South Africa (clay soil with a mean rainfall of 871 mm versus a sandy soil with a 570 mm rainfall). Model output was compared to observed grain yield, aboveground dry matter, soil organic carbon (SOC), and mineral nitrogen (Nmin) dynamics. APSIM was able to reproduce the observed grain yield dynamics fairly well as indicated by a Wilmott index of agreement of 0.90. Prediction accuracy, as indicated by the absolute model error across all crops, however, only reached 39%. Simulated Nmin and SOC dynamics showed similar patterns to the observations.
Subsequently, the model was applied in a ten-year simulation experiment with rotation treatments (14 rotations, respectively intercropping systems, including a maize monoculture control), fertiliser levels (zero and 70 kg N ha⁻¹), and residue management (retained and removed) for the two sites. For low input systems, such as smallholder farms, residue management and legume integration are of the utmost importance to maintain SOC and more pronounced Nmin levels, which, for the sandy soil, resulted in an average maize yield increase of up to 1000 kg ha⁻¹. Maize monoculture treatments with residues removed reduced SOC moderately by 0.04–0.08 %, while yields declined strongly (>1000 kg ha⁻¹) over the simulated period of ten years. In commercial, fertilised cropping systems, allocating land to cultivate crops other than maize reduced the simulated total yield performance. This diversification disadvantage has to be considered against the benefits of increased SOC and yields in the medium-term, i.e. a period of ten years. For the commercial systems, maize intercropped with delayed sown oats or cowpea appeared promising.
... Plusieurs exemples récents montrent que les modèles dynamiques peuvent être mobilisés utilement pour comprendre et prévoir l'IGEC (Agüera et al., 1997 ;Messina et al., 2006). Ces modèles peuvent également être utilisés pour renseigner certaines covariables environnementales utilisées dans la partition des interactions G x E. En tournesol, on dispose déjà de plusieurs modèles de simulation de la culture, certains étant spécifiques du tournesol (Chapman et al., 1993 ;Villalobos et al., 1996 ;Pereyra-Itujo et al., 2007), d'autres représentant la culture de manière générique (Cabelguenne et al., 1999 ;Todorovic et al., 2009). Cependant, le paramétrage de ces modèles n'est pas adossé à un phénotypage explicite de la variabilité génétique. ...
... Chenu, Deihimfard [28] characterized 60 sites by simulated water-stress index obtained from their climate and the typical soil of their region (Supplementary Table S1). Simulated water-stress index corresponds to the ratio of soil water supply to crop water demand, and reflects how the crops experience the stress [28,37,38]. In the mentioned study, they performed a set of simulations for a medium maturing variety, Hartog, based on 123 years of historical climate data obtained from 22 regions across the Australian wheat-belt (Supplementary Figures S2 and S3). ...
Multi-environment trial studies provide an opportunity for the detailed analysis of complex traits. However, conducting trials across a large number of regions can be costly and labor intensive. The Australian National Variety Trials (NVT) provide grain yield and protein content (GPC) data of over 200 wheat varieties in many and varied environments across the Australian wheat-belt and is representative of similar trials conducted in other countries. Through our analysis of the NVT dataset, we highlight the advantages and limitations in using these data to explore the relationship between grain yield and GPC in the low yielding environments of Australia. Eight environment types (ETs), categorized in a previous study based on the time and intensity of drought stress, were used to analyze the impact of drought on the relationship between grain yield and protein content. The study illustrates the value of comprehensive multi-environment analysis to explore the complex relationship between yield and GPC, and to identify the most appropriate environments to select for a favorable relationship. However, the NVT trial design does not follow the rigor associated with a normal genotype × environment study and this limits the accuracy of the interpretation.
... En conséquence il est préférable de proposer une valeur fixe pour ce paramètre voisine de celle atteinte à la maturité. L'approche utilisée ici pour le calcul de Ym est comparable à celle proposée par Villabos et al., ( ) et Chapman et al. (1993 Modélisation de l'indice de récolte HI L'indice de récolte est définit comme étant le rapport de la matière séche de la racine de la betterave à sucre par rapport à la matière séche totale. Sa prédiction semble poser des problèmes aux modèles de cultures dont la plupart se fondent sur une évolution de type degrés/jour pour modéliser l'évolution de ce facteur. ...
La plaine du Gharb au Maroc, souffre d'excès d'eau pendant l'hiver et du déficit hydrique en été. Cet état de fait explique la faible rentabilité des investissements hydro agricoles réalisés à ce jour par le gouvernement. L'originalité de ce travail réside dans le fait de relier l'évacuation des eaux de surface excédentaires au cours de l'hiver, et la satisfaction des besoins en eau de la culture dès le printemps où elle commence à connaître un déficit hydrique. Deux objectifs sont assignés à ce travail : (i) l'évaluation des performances hydrauliques et agronomiques du drainage de surface et (ii) l'évaluation de l'impact de l'excès d'eau sur la culture de la betterave à sucre. Il repose sur (i) une approche expérimentale et (ii) une modélisation des processus en question. Pour ce faire, des comparaisons ont été effectuées entre trois parcelles : (i) une parcelle nivelée selon une pente S0 de 0.2 % et irriguée à la raie, (ii) une parcelle non nivelée irriguée par aspersion et (iii) une parcelle nivelée selon une pente S0 de 0.2% dans la même sens que la première parcelle mais dépourvue de raies et irriguée par aspersion. L'étude expérimentale a clairement démontré la capacité d'une parcelle avec raies à évacuer efficacement les excès d'eau pendant l'hiver comparativement à une parcelle non nivelée. Sur cette dernière, les submersions locales affectent fortement la production de la betterave à sucre. Le système de raies fournit les meilleurs rendements suivi de près par le système nivelé irrigué par l'aspersion. Un modèle de ruissellement a été spécifiquement développé pour la prédiction du volume ruisselé et du débit maximal de ruissellement à l'exutoire d'une parcelle avec raies soumise à des événements pluvieux intermittents et d'intensités variables. Ce modèle utilisant entre autre pour entrée le hyetogramme de pluie d'une période de retour 1 an met en évidence le sous dimensionnement des fossés destinés à recueillir les eaux à l'aval des parcelles chez les agriculteurs. Les performances agronomiques de la raie ont été évaluées à l'aide du modèle SOFIP qui simule l'impact de l'irrigation à la raie sur la productivité de l'eau. Pour la parcelle non nivelée, le modèle PILOTE a été adapté pour simuler l'impact de la submersion sur la productivité de la betterave à sucre. Les simulations du rendement effectuées sur une série de onze années montrent clairement l'avantage du système gravitaire modernisé par rapport au système aspersif non nivelé et ce, pour différentes dates de semis. On peut pour conclure affirmer que le développement agricole dans la plaine du Gharb doit être raisonné en tenant compte des excès d'eau hivernaux préjudiciables aux cultures.
... The TEC has found wide application in modelling to separate ET into its components of E and T (Chapman et al., 1993;Howard et al., 1995). TEC differed significantly amongst growth periods but not between soils (Table II). ...
Much has been reported on the agronomic aspects of canola ( Brassica napus L.), but there is a lack of information on the crop's transpiration ( T ). The objective of this study was to evaluate the influence of soils and growth periods on the T , transpiration efficiency (TE) and transpiration efficiency coefficient (TEC) of canola under irrigation. Twenty‐four lysimeters (2.5 m ² ) were used for the study. The experimental treatments comprised two soil forms and four growth phases during the reproductive stage, all replicated three times. Irrigation was applied weekly through a surface drip system and a water table was maintained at 1200 mm from the surface through subirrigation. Soil water content was monitored using a neutron probe. The contribution of an accessible water table was up to 60% of the total T requirement of the crop during the investigation period. TE, calculated as above‐ground biomass per unit transpiration, ranged between 2.81 and 3.32 g m ⁻² mm ⁻¹ . TEC, obtained by normalizing TE to the vapour pressure deficit, resulted in values between 3.82 and 4.99 g kPa mm ⁻¹ . Both TE and TEC were significantly different between growth periods and not amongst soils. © 2020 John Wiley & Sons, Ltd.
... El IAF fue ajustado a dos funciones sigmoidales, una que representa una fase de crecimiento y la otra que representa una fase de pérdida de área foliar. En ambas funciones se utilizó el tiempo térmico (TT) como variable independiente (Chapman et al., 1993;Hammer y Muchow, 1994) ...
Available information to simulate durum wheat productivity is very scarce. For this purpose, genetic coefficients, whose values are not significantly affected by their interaction with the environment, are needed. These coefficients are involved in the development, interception of radiation, solar energy conversion to dry matter and assimilate partitioning to grain. This work attempted to determine whether there are significant differences in the coefficients between the Llareta-INIA and Corcolén-INIA varieties. The determinations of genetic coefficients (phyllochron, rate of leaf appearance, thermal time, extinction coefficient and radiation use efficiency) were made with data from field trials in alluvial soils located in Antumapu, Santiago of Chile, with a randomised complete block design, without biotic and abiotic stresses, whose results were studied by regression and analysis of variance. Both varieties had a similar phenological development because they had similar thermal requirements. This same situation happened with the phyllochron (112.18ºC day leaf-1 for Corcolén-INIA and 107.33ºC day leaf-1 for Llareta-INIA) and in the leaf area index behavior. However, Corcolén-INIA had a lower canopy radiation extinction coefficient (k) (0.438) than Llareta-INIA (0.511). The radiation use efficiency (RUE) measured throughout the crop cycle was similar in both varieties (2.96g MJ-1 Corcolén-INIA and 2.86g MJ-1 in Llareta- INIA). In spite of it, in analyzing the RUE separately for the vegetative and reproductive stages, both of them showed significant differences. Corcolén-INIA had a dry matter partitioning to grain similar to that of Llareta-INIA (0.049% C-1 day-1 and 0.486% C-1 day–1,respectively). The differences observed between the two varieties produced no effect on final
yield.
... 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). ...
... 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). ...
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.
... The root depth model considers only the depth of roots without considering root distribution in the soil. In this model, the elongation of root tips are modeled with a growth rate (Borg and Grimes 1986;Chapman et al. 1993) or a downward penetration rate which is influenced by soil conditions such as water content, nutrient content, soil temperature, among other variables (Groot 1987;O'Leary et al. 1985;Stapper 1984). ...
Roots are the only organ system to uptake water and nutrients from the soil. The root system is crucial for plants to survive and adapt to environmental stresses. Therefore, the root system architecture (RSA) is an important breeding target for developing climate-resilient rice. Since the rice genome has been completely sequenced, many genes for root development have been cloned and characterized. In addition, with the advances in technologies related to omics analysis, such as high-throughput sequencing, transcriptome analysis of roots has also progressed. In contrast, high-throughput root phenotyping has not been established not only in rice but also in whole plants because roots are hidden underground. This deficiency represents a bottleneck for utilizing an integrated multi-omics approach for molecular breeding of RSA. We first summarized previous transcriptome analyses for root development under various abiotic stresses such as drought, salinity, and heat, and assessed the current status of root phenotyping technology and modeling in rice. This knowledge allowed us to contemplate the possibility of applying an integrated multi-omics dataset from RSA to molecular breeding of climate-resilient rice.
... Identifying the parameters at which the response of fatty acid composition changes with temperature would make it easier to incorporate these effects into the models for growth, developmentand yield of sunflower (Texier, 1992;Chapman et al., 1993;Steer et al., 1993;Villalobos et al., 1996), since these take different genetic parameters to consider the differences among cultivars in characteristics such as grain number, grain filling rate, duration of cycle, etc. This should also help to identify whether there are hybrids better adapted to obtain a given oil quality in a given zone. ...
Sunflower is an important oil seed crop mainly grown for its oil content. Both oil
content fatty acid composition are influenced by environmental factors, especially
temperature has a profound effect on oleic and linoleic accumulation in sunflower seeds.
With this background, an experiment was conducted to study the genetic variability and
the effect of seasons on oil content and fatty acid composition in 33 sunflower
accessions. The genotypes showed significant variability for physiological,
morphological, yield and yield attributing characters and also for oil content and fatty
acid composition due to the effect of seasons, temperature and genetic makeup of
genotypes. A strong positive correlation was observed for several parameters including
oil and fatty acid content between the seasons. The increase in 1.3°C temperature in rabi
influenced the fatty acid composition by altering the ratio of oleic to linoleic. The most
affected parameters due to higher temperature are plant height especially among hybrids,
seed yield, total dry matter and linoleic content. In our study 13 genotypes were
considered as high oleic, 5 were low oleic and remaining were mid oleic. Similarly 4
genotypes accumulated higher oil content. Among all the tested genotypes CMS-103A,
ID-3/147/3-163 and RHA-341 showed higher accumulation of oleic acid with more seed
yield. The genotypes with higher seed yield with good fatty acid which showed lesser
variation between the seasons can be utilized further in crop improvement programme.
... Sunflower has been proved to respond well in minimum tillage system (dibbling method). It is considered as moderately salt and drought tolerant crop, and also a short durated crop (Chapman et al., 1993). So, after harvesting of T. Aman rice, sunflower seeds can be sown in dibbling method in the month of January thus residual moisture can be exploited. ...
A research work was conducted with three sunflower genotypes to evaluate their performance in saline and non-saline soil after harvesting of T. Aman rice. The experiment was laid out in Randomized Complete Block Design (RCBD) with four replications. Three genotypes significantly influenced almost all the growth and yield parameters in both non-saline and saline field. Genotype Hysun-33 showed maximum germination percentage in non-saline soil but minimum in saline soil. Whereas, KUSL- 1 performed the best in saline soil but worst in non-saline condition. Hysun-33 produced maximum leaf at flowering in both conditions but minimum leaf by BARI Sunflower-2 in saline soil and by KU-SL-1 in non-saline soil. In both non-saline and saline soils, plant height at flowering, head diameter, total seed head-1 and filled seed head-1 were maximum for the genotype Hysun-33 and that of minimum for the genotype BARI Sunflower-2. Genotype KU-SL-1 showed maximum value for 1000- seed weight followed by Hysun-33 in both saline and non-saline soils. In case of seed yield head-1, Hysun-33 performed best in saline soils but worst in non-saline soil. In non-saline soil, KU-SL-1 produced maximum seed yield head-1. Biomass at harvest, head diameter and number of filled seed head-1 was well correlated with number of seed head and seed yield head-1. Thus genotype Hysun-33 may be considered as best for saline and KU-SL-1 for non-saline soil. Bangladesh Agron. J. 2018, 21(1): 1-7
... Palumbo et al. (2014) reported 20.9 to 26.4 Mg ha -1 for a Mediterranean environment. This is because the EPIC model simulated LAI using a temperature-based method in which temperature was the most limiting factor for leaf expansion (Amir and Sinclair, 1991;Chapman et al., 1993). However, the carbon-based methods used to estimate LAI indicate that plant leaf expansion depends on the amount of dry matter available for leaf growth for that day (Soltani and Sinclair, 2012). ...
Energy sorghum is one of the most attractive alternatives for producing energy in many regions of the world because of the high biomass productivity obtained in a short period. However, it faces many challenges, particularly where water resources are limited. Crop simulation models are suitable decision support tools for the assessment of crop water use and biomass production under different spatial and climatic conditions. Calibration of simulation models to local conditions is a necessary procedure to improve model reliability. The objective of this study was to calibrate and evaluate the Environmental Policy Integrated Climate (EPIC) model for the production of energy sorghum under different irrigation levels. The model was then used to simulate crop biomass productivity and crop water use to identify appropriate irrigation strategies. This study was conducted at the Texas A&M AgriLife Research Center in Weslaco, Texas. Simulations were performed to determine the total dry biomass, crop water use, the relationship between crop productivity and crop evapotranspiration (ETc), and water use efficiency (WUE). Simulated ETc agreed well with estimates from a weather station, except for a few simulation events. The statistical parameters derived from measured versus simulated dry biomass in the calibrated model, which indicated that the model performed well, were R² = 0.99 and PBIAS = -5.35%. The calibrated model showed great potential for simulating the total dry biomass. At full irrigation, the difference between measured and simulated total dry biomass was 4.3% in 2013 and 3.0% in 2015. This study showed that energy sorghum requires approximately 600 mm of water to obtain 23 Mg ha⁻¹ of total dry biomass. It also demonstrated that the EPIC model could be used for assessment of crop water use and total biomass under limited irrigation levels, especially in semi-arid regions. © 2018 American Society of Agricultural and Biological Engineers.
... Under water-limited conditions, the two factors influencing biomass production are the amount of water the crop can capture and transpire, and the conversion rate of water to biomass, also known as water use efficiency (WUE) or transpiration efficiency (TE) (Passioura 1977, Blum 2009). This framework was developed and used in sunflower as a means to predict crop growth in water-limited situations (Chapman et al. 1993). Water productivity, the mass of biomass produced per unit of water transpired, has been proposed as a selection criterion to increase yield and gain yield stability under limited water conditions and genetic variation has been reported in some species (e.g. ...
... Crop models currently used for simulating sunflower yield in response to various environments are either: (i) generic (a single mode for multiple species): STICS (Brisson et al., 2003), CropSyst (Stöckle et al., 2003;Todorovic et al., 2009;Moriondo et al., 2011), EPIC/EPIC-Phase (Kiniry et al., 1992;Cabelguenne et al., 1999), AquaCrop (Raes et al., 2009;Todorovic et al., 2009), AqYield (Constantin et al., 2015), WOFOST (Todorovic et al., 2009) or (ii) specific to sunflower crop: Oilcrop-Sun (Villalobos et al., 1996), QSUN (APSIMsunflower) (Chapman et al., 1993;Zeng et al., 2016), SUNFLO (Casadebaig et al., 2011). ...
Climate change is characterized by higher temperatures, elevated atmospheric CO2 concentrations, extreme climatic hazards, and less water available for agriculture. Sunflower, a spring-sown crop often cultivated in southern and eastern regions of Europe, could be more vulnerable to the direct effect of heat stress at anthesis and drought during its growing cycle, both factors resulting in severe yield loss, oil content decrease, and fatty acid alterations. Adaptations through breeding (earliness, stress tolerance), crop management (planting dates), and shifting of growing areas could be developed, assessed and combined to partly cope with these negative impacts. New cultivation opportunities could be expected in northern parts of Europe where sunflower is not grown presently and where it could usefully contribute to diversify cereal-based cropping systems. In addition, sunflower crop could participate to the mitigation solution as a low greenhouse gas emitter compared to cereals and oilseed rape. Sunflower crop models should be revised to account for these emerging environmental factors in order to reduce the uncertainties in yield and oil predictions. The future of sunflower in Europe is probably related to its potential adaptation to climate change but also to its competitiveness and attractiveness for food and energy.
... This genotype had been characterized with a moderate root growth at the early stages, above-average root growth at reproductive stages (after 50 days of growth), moderate shoot biomass production and the highest HI making it to achieve the top grain yield slot. Therefore, better shoot biomass production and greater HI seem to be equally important for better drought tolerance and it is apparently achievable through a prolific and deep root system in chickpea and few other crops (Chapman et al., 1993;Blum, 2009;Chloupek et al., 2010;Kashiwagi et al., 2015). But this prolificacy need to be comprehended as a relative term and applicable only among the legumes because cereals are known to be equipped with 5 to 10 times more root length for uptake of about the same quantity of soil water (Hamblin and Tennant, 1987;Gregory and Eastham, 1996;Sandana and Pinochet, 2015). ...
Chickpea, the second most important legume crop, suffers major yield losses by terminal drought stress (DS). Stronger root system is known to enhance drought yields but this understanding remains controversial. To understand precisely the root traits contribution towards yield, 12 chickpea genotypes with well-known drought response were field evaluated under drought and optimal irrigation. Root traits, such as root length density (RLD), total root dry weight (RDW), deep root dry weight (deep RDW) and root:shoot ratio (RSR), were measured periodically by soil coring up to 1.2m soil depth across drought treatments. Large variations were observed for RLD, RDW, deep RDW and RSR in both the drought treatments. DS increased RLD below 30cm soil depth, deep RDW, RSR but decreased the root diameter. DS increased the genetic variation in RDW more at the penultimate soil depths. Genetic variation under drought was the widest for RLD ∼50 DAS, for deep RDW ∼50–75 DAS and for RSR at 35 DAS. Genotypes ICC 4958, ICC 8261, Annigeri, ICC 14799, ICC 283 and ICC 867 at vegetative stage and genotypes ICC 14778, ICCV 10, ICC 3325, ICC 14799 and ICC 1882 at the reproductive phase produced greater RLD. Path- and correlation coefficients revealed strong positive contributions of RLD after 45 DAS, deep RDW at vicinity of maturity and RSR at early podfill stages to yield under drought. Breeding for the best combination of profuse RLD at surface soil depths, and RDW at deeper soil layers, was proposed to be the best selection strategy, for an efficient water use and an enhanced terminal drought tolerance in chickpea.
... These models are mathematical representations of crops and soils which take into account dynamically and on a daily basis the effects of weather and crop management on seed yield. The QSUN model was developed in the early nineties [13]. It takes into account sowing date, irrigation and variety. ...
In France, there is a need for improved sunflower crop management, in order to meet the greater requirement for oil by increasing both seed yields and the area of this crop. The objective of this article is to review the main characteristics of sunflower crop management in France and in other countries, in order to emphasize the need for improvement, and to evaluate if the recent advances in crop modelling could help to find solutions. In France, a better adaptation of crop management to water availability is needed, as well as a more efficient control of diseases without applying more fungicides. The results of these objectives would also trigger major improvements in other countries, but there is also a need to control insects and to adapt crop management to the goals of oil quality. The main sunflower crop models are reviewed in this article, with an emphasis on the most recent ones. Their ability to contribute to improving sunflower crop management, although they do not take into account diseases and insects, is discussed. Confidence in the decisions based on simulations, and the way to evaluate it, is also examined.
... 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). ...
Crop models are simplified mathematical representations of the interacting biological and environmental components of the dynamic soil–plant–environment system. Sorghum crop modeling has evolved in parallel with crop modeling capability in general, since its origins in the 1960s and 1970s. Here we briefly review the trajectory in sorghum crop modeling leading to the development of advanced models. We then (i) overview the structure and function of the sorghum model in the Agricultural Production System sIMulator (APSIM) to exemplify advanced modeling concepts that suit both agronomic and breeding applications, (ii) review an example of use of sorghum modeling in supporting agronomic management decisions, (iii) review an example of the use of sorghum modeling in plant breeding, and (iv) consider implications for future roles of sorghum crop modeling. Modeling and simulation provide an avenue to explore consequences of crop management decision options in situations confronted with risks associated with seasonal climate uncertainties. Here we consider the possibility of manipulating planting configuration and density in sorghum as a means to manipulate the productivity–risk trade-off. A simulation analysis of decision options is presented and avenues for its use with decision-makers discussed. Modeling and simulation also provide opportunities to improve breeding efficiency by either dissecting complex traits to more amenable targets for genetics and breeding, or by trait evaluation via phenotypic prediction in target production regions to help prioritize effort and assess breeding strategies. Here we consider studies on the stay-green trait in sorghum, which confers yield advantage in water-limited situations, to exemplify both aspects. The possible future roles of sorghum modeling in agronomy and breeding are discussed as are opportunities related to their synergistic interaction. The potential to add significant value to the revolution in plant breeding associated with genomic technologies is identified as the new modeling frontier.
... Mathematical models with a more empirical approach have been developed to predict sunflower development, yield, and yield components (Chapman et al., 1993;Steer et al., 1993;Villalobos et al., 1996;Yeatts, 2004). These crop simulation models are useful tools for evaluating different agronomic management strategies (Villalobos et al., 1996). ...
Grain growth and oil biosynthesis are complex processes that involve various enzymes placed in different sub-cellular compartments of the grain. In order to understand the mechanisms controlling grain weight and composition, we need mathematical models capable of simulating the dynamic behavior of the main components of the grain during the grain filling stage. In this paper, we present a non-structured mechanistic kinetic model developed for sunflower grains. The model was first calibrated for sunflower hybrid ACA855. The calibrated model was able to predict the theoretical amount of carbohydrate equivalents allocated to the grain, grain growth and the dynamics of the oil and non-oil fraction, while considering maintenance requirements and leaf senescence. Incorporating into the model the serial-parallel nature of fatty acid biosynthesis permitted a good representation of the kinetics of palmitic, stearic, oleic, and linoleic acids production. A sensitivity analysis showed that the relative influence of input parameters changed along grain development. Grain growth was mostly affected by the specific growth parameter (μ′) while fatty acid composition strongly depended on their own maximum specific rate parameters. The model was successfully applied to two additional hybrids (MG2 and DK3820). The proposed model can be the first building block toward the development of a more sophisticated model, capable of predicting the effects of environmental conditions on grain weight and composition, in a comprehensive and quantitative way.
... However, soil salinity may involve significantly different levels and combinations that vary between regions. APSIM has a specific submodule for sunflower simulation (APSIM-Sunflower) (Chapman et al., 1993), but it has not yet been intensively evaluated outside of Australia. Moreover, to our knowledge, no study has evaluated crop growth models for sunflowers in saline soils. ...
Saline soils are a major challenge in the cropping region of Inner Mongolia, China. Crop modelling might be a useful tool for identifying suitable management practices for sunflower, which is the main crop in this area. To evaluate the applicability of the sunflower model within the Agricultural Production System siMulator (APSIM) for saline soils, the model was calibrated using one year of field trial data and tested with another year of data collected at the Yichang Experimental Station in the Hetao Irrigation District, Inner Mongolia, China. The treatments employed in the trials included different salinity levels and nitrogen application rates. The crop characteristics assessed included the seed yield (SY), dry matter (DM), and leaf area index (LAI) at various growth stages of the sunflowers. To represent salinity effects in the model, i.e., reduced water uptake by the plants, we tested three options by modifying the crop lower limit (CLL), the water-extraction coefficient (KL), and the pattern of root exploration in the soil profile (XF). We optimized each parameter individually against measured SY, DM, and LAI values using a multi-objective calibration in the PEST program with data collected from a field trial that was conducted in 2012. The results indicated that by modifying CLL, KL, and XF in the SoilWater module of APSIM, sunflower SY and DM could be simulated for saline soils, and modifying KL afforded the most accurate SY simulation (R2 =0.99, RMSE=253.51kgha-1, RRMSE=7.11%). Additionally, the scale factors used to modify CLL, KL, and XF (noted as SCLL, SKL, and SXF, respectively) according to the referenced CLL, KL, and XF values could be estimated based on the 0-20-cm soil salinity level before sowing (S). Specifically, SCLL increased linearly with S (SCLLS model, R2 =0.80), and SKL and SXF decreased linearly with the natural logarithm of S (SKLS and SXFS models, R2 =0.74 and 0.67, respectively). When using measured data from 2014 for the evaluation, the SKLS and SXFS models achieved high accuracy for the SY simulations (R2 =0.71 and 0.71; RMSE=568.7 and 845.5kgha-1, RRMSE=17.03% and 25.32%, respectively) and acceptable accuracy for the DM simulations (R2 =0.41 and 0.45; RMSE=2543.2 and 3114.5kgha-1, RRMSE=20.99% and 25.70%, respectively), although the SCLLS model slightly underestimated SY and DM. Further research is required to elucidate the mechanism linking CLL, KL, XF, crop physiological parameters (e.g., radiation use efficiency), and soil salinity by testing the approaches at additional sites; such efforts would improve the accuracy of sunflower growth modelling in salinized areas.
... While the importance of phenological development has been stressed (Ghersa and Holt 1995), there have been limited discussions as to how this information can be used in modifying weed management programs. Crop scientists have repeatedly demonstrated that phenological development is critical to our understanding of crop growth and yield potential and that phenological development can be predicted (Chapman et al. 1993;Grant 1989;Miller et al. 1993). A similar understanding must be achieved by weed scientists in order to develop predictive models. ...
Implementation of an integrated weed management system requires prediction of the effect of weed competition on crop yield. Predicting outcomes of weed competition is complicated by genetic and environmental variation across years, locations, and management. Mechanistic models have the potential to account for this variability. Weed phenological development is an essential component of such models. Growth cabinet: studies were conducted to characterize common ragweed's phenological response to temperature, photoperiod, and irradiance. Ragweed development occurred over a temperature range of 8.0 to 31.7 C, and this response to temperature was best characterized using a nonlinear funct ion. A maximum leaf appearance rate of 1.02 leaves d(-1) occurred at 31.7 C. Ragweed has a short juvenile phase, during which it was not sensitive to photoperiod. Following this juvenile phase, sensitivity to photoperiod was constant and continued until pistillate flowers were observed. Photoperiods of 14 h or less were optimal and resulted in maximum rates of development. Irradiance level affected ragweed phenological development only when combined with the additional stress of low temperatures. Data generated in this study can be used for the development of mechanistic weed competition models.
... QSUN was developed for simulating yield, growth and oil content of sunflower in dry conditions of Australia (Chapman et al., 1993). OC is simulated in a linear pattern starting from flowering and ending 25 days after flowering with a maximal OC set at 45%. ...
Sunflower appears as a potentially highly competitive crop, thanks to the diversification of its market and the richness of its oil. However, seed oil concentration (OC) - a commercial criterion for crushing industry - is subjected to genotypic and environmental effects that make it sometimes hardly predictable. It is assumed that more understanding of oil physiology combined with the use of crop models should permit to improve prediction and management of grain quality for various end-users. Main effects of temperature, water, nitrogen, plant density and fungal diseases were reviewed in this paper. Current generic and specific crop models which simulate oil concentration were found to be empirical and to lack of proper evaluation processes. Recently two modeling approaches integrating ecophysiological knowledge were developed by Andrianasolo (2014, Statistical and dynamic modelling of sunflower (Helianthus annuus L.) grain composition as a function of agronomic and environmental factors, Ph.D. Thesis, INP Toulouse): (i) a statistical approach relating OC to a range of explanatory variables (potential OC, temperature, water and nitrogen stress indices, intercepted radiation, plant density) which resulted in prediction quality from 1.9 to 2.5 oil points depending on the nature of the models; (ii) a dynamic approach, based on "source-sink" relationships involving leaves, stems, receptacles (as sources) and hulls, proteins and oil (as sinks) and using priority rules for carbon and nitrogen allocation. The latter model reproduced dynamic patterns of all source and sink components faithfully, but tended to overestimate OC. A better description of photosynthesis and nitrogen uptake, as well as genotypic parameters is expected to improve its performance.
... Daily drainage is simulated as the product of excess of water (q v > q DUL ) and a drainage factor that depends on the soil texture. Surface runoff is estimated using a simplified CN procedure (Williams, 1991) that takes into account actual soil water content in the top layer (Ritchie, 1998) and surface residue (Chapman et al., 1993). Drainage factor, CN, and soil albedo values were assigned to each soil based on the particle size distribution ( Table 2). ...
Sowing plant available water (PAW s ) can impact wheat ( Triticum aestivum L.) stand establishment, early crop development, and yield. Consequently, PAW s is an essential input in crop simulation models and its estimation can improve agronomic decisions. Our objective was to identify effective methods to predict PAW s in continuous winter wheat by exploring empirical and mechanistic models based on the preceding 4‐mo summer fallow. The mechanistic soil water balance models dual crop coefficient (dual K c ) and simple simulation model (SSM) were calibrated, validated, and tested using soil moisture datasets collected from 2009 to 2013 in Oklahoma totaling 29 site‐years. Additionally, PAW s was predicted using empirical nonlinear models based on cumulative fallow precipitation and the soil's plant available water capacity (PAWC). Both the dual K c and SSM models resulted in normalized root mean squared error (RMSE n ) below 12% (20 mm) for the calibration and validation datasets. Modeled PAW s for the prediction dataset was within ±30% of field observations in 67% of the site‐years for both dual K c and SSM models, with RMSE n of 27 and 32%. An inverse‐exponential and a logarithmic model of PAW s using cumulative fallow precipitation and PAWC both resulted in RMSE n = 23 and 29% in the calibration and validation datasets. The dual K c model was slightly superior to empirical models based on nonlinear regression analysis, and was superior to the SSM model. Initializing the dual K c at the start of the preceding fallow or using empirical relationships allow for acceptable predictions of PAW s , eliminating the need for subjective PAW s values.
Core Ideas Wheat is among the most important crops cultivated in the southern Great Plains.
Plant‐available water at sowing is a critical input in dynamic crop simulation models.
Mechanistic water balance models or empirical models can predict wheat PAW at sowing.
Models here shown decrease the need for subjective initial PAW values in crop models.
With the rapid global trend towards mechanized, continuous and dense cropping
systems that provide agricultural efficiency to meet consumer demand, soil
compaction has become a recognized problem. Soil compaction under modern
machines has had immense impact on productive land‘s physical, chemical and
biological properties, including soil-water storage capacity, fertiliser use efficiency,
and plant root architecture. As a result, farms are experiencing substantially reduced
crop yields and economic returns. The percentage of soil compaction increases with
increased soil clay fraction. Numerous investigations have been conducted to
evaluate the technical, economic and soil-crop efficiency of compaction mitigation
strategies, but deep tillage has not received sufficient consideration, particularly in
relation to high clay content soils.
This study was conducted to technically and economically evaluate a range of deep
ripping systems, and study the effect of tillage on soil and crop grown on cohesive
soils. A series of field experiments were conducted to parametrise a soil tillage force
prediction model, previously developed by Godwin and O‘Dogherty (2007) and the
Agricultural Productions Systems sIMulator (APSIM) developed by the Agricultural
Production Systems Research Unit in Australia (Holzworth et al., 2014; Keating et
al., 2003). The behaviour of soil physical properties, power requirements of ripping
operations and cost, and agronomic and economic performance of sorghum and
wheat were assessed at the University of Southern Queensland‘s research ground in
Toowoomba, Queensland (Australia) over two consecutive seasons (2015-16 and
2016-17). The work was conducted by replicating the soil conditions commonly
found in non-controlled or ‗random‘ traffic farming systems, referred to as RTF.
Sorghum was also grown at a commercial farm located in Evanslea near
Toowoomba, under controlled traffic (CTF) conditions (a farm system based on a
permanent lanes for machinery traffic) during the 2018 summer crop season.
The soil types at the two sites are Red Ferrosol (69.1% clay, 10.0% silt, and 20.9%
sand) and Black Vertosol (64.8% clay, 23.4% silt, and 11.8% sand). Three levels of
deep ripping depth, namely, Deep Ripping 1 (D1= 0-0.3 m), Deep Ripping 2 (D2= 0-
0.6 m), and Control (C= no ripping) were applied using a Barrow single tine ripper at
the Ag plot site - USQ, and a Tilco eight-tine ripper was used at the Evanslea site.
The tillage operations were performed at 2.7 km/h. A predetermined optimum N
fertiliser rate was applied after sorghum and wheat sowing at the Ag plot site. The
field experiments were conducted according to the randomized complete block
design (RCBD). The Statistical Package for Social Scientists (SPSS) software was
utilized to analyse the significance of the differences between the variables at the
probability level of 5% as the least significant difference (LSD).
The statistical analysis results showed that the D2 treatment significantly reduced
soil bulk density and soil strength by up to 5% and 24% for Red Ferrosol soil, and by
up to 6% and 40% for Black Vertosol soil respectively, and increased water content
compared with the D1 and C treatments. Overall results showed that D2 was superior in ameliorating the properties of both soils. In both soils, energy requirement results showed that tillage draft force and tractor power requirements were dependent on tillage depth, but for both tillage treatments, energy consumption was slightly lower for the CTF system (Evanslea site) than the RTF system at Ag plot site.
Crop performance results showed that at the Ag plot site, the grain and biomass
yields were highest by up to 19% for sorghum and by up to 30% for wheat when the
D2 treatment was applied, compared to the D1 and C treated crop yield components. Also, the grain and biomass yields were highest for fertilised soil by up to 10% for sorghum and by up to 16% and 25% for wheat respectively, in comparison with the non-fertilised treatments soils yield. Fertilising of D2 treated soil produced the highest significant yield of sorghum grain (5360 kg/ha), biomass (13269 kg/ha),
wheat grain (2419 kg/ha), and biomass (5960 kg/ha) compared to the yield of the
other treatment interactions. However, at Evanslea site, the D1 treatment showed
significantly higher yield and yield components for sorghum compared with C
practice (by up to 17% higher yield), and no differences were observed for treatment
D2. Economically, the D1 treatment required the lowest total operational cost at both
sites, which was estimated at AUD125/ha and AUD25.8/ha at the Ag plot and
Evanslea sites, respectively. These results compare to AUD139.3/ha (Ag plot) and
AUD30.8/ha (Evanslea) for the D2 ripping system. With regard to economic returns,
at the Ag plot site, D2 yielded the highest sorghum gross benefit (AUD1422/ha) and
net benefit (AUD1122/ha), wheat gross benefit (AUD590/ha) and net benefit (AUD482.3/ha), 2017 season gross benefit (AUD 2011.7/ha) and 2017 season net
benefit (AUD 1604.7/ha), compared to D1 and C soil benefits. The economic
fertiliser application at this site achieved the highest gross benefit for sorghum
(AUD1384.2/ha), wheat (AUD555.6/ha), and 2017 season (AUD1939.8/ha)
respectively, in comparison with the non-fertilised soils‘ total return. Also, fertilised
D2 treated soil resulted in the highest sorghum gross benefit (AUD1512.9/ha) and
net benefit (AUD1170.3/ha), wheat gross benefit (AUD633.7/ha) and net benefit
(AUD492.4/ha), 2017 season gross benefit (AUD2146.6/ha), and net benefit
(AUD1662.7/ha) compared to other interactions‘ benefits. At the Evanslea site, D1
significantly increased sorghum gross benefit and net benefit by up to 17%
(AUD2277.9/ha) and by up to 20% (AUD1825.5/ha), respectively compared to C
benefits, and no differences were observed with treatment D2.
The average of APSIM derived results for the long-term (1980-2017) at the Ag plot
site showed that the D2 treatment reported consistently higher grain sorghum (4192
kg/ha), biomass (11454 kg/ha), wheat grain (3783 kg/ha), and biomass (10623
kg/ha), compared to the D1 and C treatments‘ yields under the same long-term
conditions. However, at the Evanslea site, for long-term (1980-2018), APSIM
simulation showed that D1 treatment increased the yield of sorghum grain and
biomass significantly by up to 10% (5823 kg/ha) and 11% (12171 kg/ha),
respectively compared to C treatment‘s production, but these increases were found
not significant with the D2 yields‘ components. APSIM model simulation of field
experiment conditions during 2017 season at the Ag plot site showed that the D2
treatment also had the highest significant yield of sorghum grain (5284 kg/ha),
biomass (12488 kg/ha), wheat grain (2341 kg/ha) and biomass (6081 kg/ha)
compared to the C and D1 crop yields. Similarly, APSIM model simulation of field
experiment circumstances during the 2018 season at the Evanslea site showed that
the D1 treatment produced the highest yield of sorghum grain (7129 kg/ha), biomass
(13364 kg/ha) yields, compared to the C and D1 crop yields.
Overall, both the long and short-term model outputs were in good agreement with
experimental data, suggesting beneficial effects of deep tillage in improving cereal
crops‘ productivity in this region. Moreover, in comparison with the study findings,
the model prediction error rate was ±7, which indicates that the developed model
approach is valid and calibrated during this study.
Results derived from the G&O soil tillage mechanics model under the Ag plot and
Evanslea soil conditions showed that the required tractive force increases with the
increasing operation working depth. Furthermore, the D1 was superior, requiring the
lowest draft force at Ag plot (7.48 kN) and Evanslea (19.65 kN) soils, compared to
the D2 required forces which were 43.28 kN and 41.41kN at both sites, respectively.
In general, the model values were in line with the experiments' draft forces and when compared with the study readings, the model prediction error rate was ±8, which indicates that it is also valid and calibrated during this study.
Finally, the study provides conclusions and recommendations that contribute to crop production improvement in the face of recurrent and increasing challenges, as well as emphasizing the necessity of correct management and cultivation of economically important crops after the application of deep ripping to produce accurate results that serve decision-making in the agricultural sector.
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.
High spatiotemporal climate variability in the northeast Iran has led to increased production risk for rainfed crops. Hence, to explore potential opportunities for reducing the probable risk, it is necessary to characterize the main rainfed growing regions based on drought patterns. The CSM-CERES-Wheat and -Barley models were applied to simulate yield and to achieve input for estimating water supply demand ratio (WSDR) in 10 reference weather stations (RWSs) for the period 1980–2009. Simulated WSDR was averaged for every 100°Cd to and from flowering for each location (each RWS and each season). The environmental classes and their optimum number for each RWSs and the region were derived by k-means clustering. The soil diversity combined with spatiotemporal variations in rainfall caused rainfed wheat and barley to be exposed to drought stresses, which were very different across locations and among seasons. Nevertheless, four main environmental classes could represent the variability at local and regional scales. The first environmental class corresponded to environments with low drought stress with a frequency of 1.10 and 12.01% for wheat and barley, respectively. For wheat, environmental classes 2, 3, and 4 with a frequency of 22.62, 51.46, and 24.82% begin from 1100, 900, and 700°Cd (degree days) before flowering, respectively, and lasted until maturity. For barley, these three environmental classes with the frequency of 26.50, 32.86, and 28.62%, respectively, corresponded to sever drought stress levels with different degrees that begin from 500°Cd before flowering and was maximized at about 300°Cd after flowering which coincided with the grain filling. The occurrence frequency of environmental classes differed temporally and spatially. Over time from 1980 to 2009, environmental class 4 increased and environmental class 1 decreased for wheat and barley, respectively. In most locations, wheat and barley yield tended to decline from environmental classes 1 to 4. Such characterization of production environment can be used to help breeders focus on genes and traits adapted to target production environments.
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.
Sunflower (Helianthus annuus L.) is an annual oilseed crop primarily grown for its edible oil and fruits in temperate and subtropical climates worldwide. Its oil is somewhat superior to other vegetable oils due to the greater proportion of the unsaturated fatty acids and the content of bioactive compounds (e.g. tocopherols and phytosterols). Here, we update the knowledge about sunflower crop physiology in the context of climate change with focus on (i) the main factors affecting growth and phenology, (ii) the capture and efficiency use of radiation, water and nutrients, iii) how grain number (GN) and grain weight are defined to conform the crop yield and iv) how grain and oil quality are elaborated. All these traits are analysed considering the genetic potential of the crop and how crop management practices can modulate them (by affecting environmental factors).
In order to evaluate the effect of different waterlogging duration at full flowering stage on plant height and leaf age of sesame, two plot experiments were conducted as a split factorial design at Xinxiang, China, during 2013 and 2014 sesame growth season. Two sesame cultivars with different tolerance to waterlogging, Zhongzhi 13 (tolerant) and Zhengzhi 13 (susceptible), were assigned as main plots and five waterlogging duration of 0 h, 24 h, 36 h, 48 h and 60 h at full flowering stage (represented by CK, W24h, W36h, W48h and W60h, respectively) were chosen as subplots. The growth curve, as well as normalization method, were used to quantitatively analyze plant height and leaf age, taking the relative days after emergence as an independent variable and the relative plant height as a dependent variable. The results showed that waterlogging increased plant height and decreased leaf age of sesame. The growth dynamics of plant height and leaf age, accorded with the "S" curve, were simulated by the Logistic equation, with the root mean squared error (RMSE) of 9.97 cm and that of 2.54 pairs. The coefficient of multiple determinations (R2) was 0.949 for plant height and 0.976 for leaf age. Waterlogging duration had a significant effect on parameters of k for plant height and of k’ for leaf age. The mean k of all waterlogging treatments increased by 9.34% for Zhongzhi 13 and 10.16% for Zhengzhi 13, respectively. In contrast, the mean k′ in all waterlogging treatments decreased by 10.69% for Zhongzhi 13 and 10.16% for Zhengzhi 13, respectively. Tmax for plant height in both Zhongzhi 13 and Zhengzhi 13 was less than 0.554. In case of leaf age, Tmax of four treatments in Zhongzhi 13 changed between 0.554 and 0.590, while Tmax of W24h and W36h in Zhengzhi 13 varied between 0.554 and 0.590, and Tmax of W48h and W60h in Zhengzhi 13 was lower than 0.554. The mean of T1, T2, T3, t2 for all waterlogging treatments of plant height in Zhongzhi 13 were higher than in Zhengzhi 13. However, the mean of V2 and V3 for all waterlogging treatments of plant height in Zhongzhi 13 were smaller than in Zhengzhi 13. The mean of T2, T3, t2 for all waterlogging treatments of leaf age in Zhongzhi 13 were higher than in Zhengzhi 13. However, the mean of T1, V2, V3, V2 and V3 for all waterlogging treatments of plant height in Zhongzhi 13 were smaller than in Zhengzhi 13. The relative growth duration and relative growth rate of plant height, as well as the relative growth rate of leaf age during rapid-increase and slow-increase stage, are the main factors leading to the difference in plant height and leaf age.
Knowledge of the effects of temperature and geographic variables on the oleic acid content of sunflower ( Helianthus annuus L.) oil allows us to predict the type of oil that will be produced in a particular area. This study was designed to establish a simple empirical model, which uses available variables of previously established effects, to estimate the final oleic acid composition of sunflower oil. Over two growing seasons, sunflower seeds were collected from Spain's main producing areas, and the oleic acid concentration of oil extracted from these samples was analytically determined. The effects of two types of variables (geographical position and temperature) on oil oleic acid content were determined according to three models based on the input variables: latitude, longitude, and altitude (Model I); mean minimum and maximum temperatures during the phenological stages of sunflower seed development and maturation (Model II); and a combination of both types of data (Model III). Through stepwise regression, it was established that best results were obtained using the temperature model (Model II) and the variables’ mean minimum development and mean minimum and maximum maturation temperatures ( r ² = 0.99, P < 0.001, n = 88). Of the three variables included in this model, the mean minimum maturation temperature provided the closest estimate of percentage oleic acid content. This regression model was statistically validated and is proposed as a method for crop managers to estimate oleic acid content based on local temperatures.
Grain-filling is a key stage allowing for the achievement of high grain yield. The ridge-furrow film mulching (RFFM) farming system has been widely adopted as a water saving and yield-improving planting pattern on the Loess Plateau of China. However, there is no convenient and effective model to understand the mechanism of maize grain-filling and effective nitrogen (N) management to obtain high grain yield under RFFM and N fertilizer. A two-year (2017–2018) field experiment was conducted on summer maize under RFFM (biodegradable film mulching (BM) and PE film mulching (PM)) and nitrogen fertilizer (0 (N0), 90 (N1), 180 (N2) and 270 (N3) kg N ha⁻¹). The results showed that the 100-kernel dry weight was successfully simulated by the proposed model. After optimizing parameters, the model not only achieved minimum input requirements, but also successfully regulated N application. Moreover, characteristic parameters of grain-filling were significantly affected by the interaction of RFFM and N. N was beneficial for the improvement of the final 100-kernel dry weight (a) and grain-filling rate (Vmax, V¯) and was more improved under BM. The N1 with PM treatment reached the maximum grain-filling rate (Vmax) earlier. The maximum grain-filling rate (Vmax) and average rate of grain-filling (V¯) of PM was more sensitive to high N than BM. Parameters of the gradual-growing period were significantly affected by RFFM or N, but the interaction between RFFM and N had extremely significant effects on parameters of the fast-growing and slow-growing periods. For BM, N could improve the rate of three periods of grain-filling, and the N2 treatment was the best in the fast-growing and slow-growing periods. For PM, N could improve the rate of the gradual-growing period. According to the optimized model, the optimal nitrogen application rate of BM and PM was 205 kg ha⁻¹ and 196 kg ha⁻¹, respectively. Based on this optimized model, we could better understand the grain-filling process and achieve the appropriate nitrogen application rate.
Here we modelled the influence of phenology of barley crops under diverse environmental and management conditions. Such trait manipulation can assist breeders in genotype selection and growers in better managing barley crops to achieve their yield potential. We first developed two near isogenic lines (NILs) of barley (Eps-317-1-E, and Eps-317-1-L). NILs were developed from a cross between TX9425, a Chinese landrace, and Franklin, an Australian malting barley. Field experiments were then conducted in Tasmania, Australia, using three sowing dates per year during 2015, 2016 and 2017 to parameterise and test the barley module of the APSIM model (APSIM-Barley). We then conducted a genotype by environment by management (GxExM) analysis using ten sites across the Australian wheat-belt, with a range of sowing dates, fertiliser rates and planting densities. The early genotype (Eps-317-1-E) performed better in environments prone to terminal drought and heat stress effects. This was due to earlier flowering and a propensity for greater transpiration-use efficiency from growth stage (GS) 50 to 87. The late NIL (Eps-317-1-L) generally produced higher yield in long-season environments with high rainfall and cool terminal temperatures. Performance of all genotypes was generally better for May sowings (being mid-autumn in the southern hemisphere), wherein yields of the two NILs were highest. Overall, our study showed that Eps-317-1-E was more adapted to regions prone to drought and heat stress, while Eps-317-1-L was more suited to regions with longer growing seasons. This study exemplifies how models can be used in concert with breeding experiments and thus provides farmers and breeders with opportunities to examine how new genotypes will perform in new environments under multiple management conditions.
The experiment was conducted in the research fields in which winter wheat (Triticum aestivum L.)-vetch (Vicia sativa L.) rotation since 2001 year with conventional and reduced (conservational) tillage systems. Vetch was sown in autumn 2013, and sunflower (Hel?anthus annuus L.) was in spring of 2014. The soil tillage systems were mouldboard plough + disc harrow (PTI), and alternatively to this, chisel + disc harrow (ÇTI) and rototiller (RTI). At the end of the study, although, RTI and PTI were provided similar plant emergence rate RTI had the highest in 2014 and 2015 years when compared to two other systems. RTI and PTI were also provided similar head diameter and 1000-seed weight when ÇTI had the lowest. ÇTI produced lower biomass compared with PTI and RTI. 7 dominant weed species were found in RTI and ÇTI while PTI had lower number as 4. In results, RTI was improved the plant properties as much as PT, and produced also more seed yield. As a result of this study, it was determined that the RTI was appropriate system for spring second production of sunflower which was sown following winter legumes because of improving plant properties and yield under this conditions and it was found as alternative to conventional tillage for this region. © 2017 Namik Kemal University - Agricultural Faculty. All rights reserved.
Movement of soil water during transpiration is a complicated process in comparison to evaporation because the root system of plants extracts water (and solute) from the soil using the soil root layer. Evaporation is the typical movement of water to the soil surface (or close to it) from which water is evaporating. The properties of different plants’ root systems and their changes during ontogenesis is described, as well as the influence of different environmental factors on root growth and properties. Richards’ equation describing soil water movement with root extraction is presented. The method of root extraction rate of water estimation from soil water content (SWC) field measurements is presented. This is the proposed method of water uptake evaluation by roots, based on the results of field measurements. This method is used to model water movement and extraction by roots in soil with a plant canopy. The mesoscopic approach to water uptake by an evaluation of roots is described.
Most recent mechanistic crop models are based on source-sink relationships. To build a conceptual framework for achene quality traits in sunflower (Helianthus annuus L.), source-sink relationships were investigated in contrasting environments. Two experiments were performed under field conditions in 2011 and 2012. Oil, protein, hull (sink), leaves, receptacles and stems (source) were measured weekly under contrasting nitrogen (N−: no fertilization; N+: 150 kg N per ha), plant density (3 and 4.5 plants per m²) and genotype (cv. Kerbel, cv. LG5451 HO and cv. Olledy) treatments. A bi-linear model was fitted to source and sink component dry weight (DW) dynamics per m². Nitrogen and plant density influenced rates and duration of all source and sink dynamics, while genotype had a significant influence on timing parameters. More significant factor interactions were found in 2011 than in 2012, probably because of other factors that occurred or interacted during grain filling (water stress and/or thermal stress). We constructed a robust source-sink framework for oil and protein accumulation in relation to remobilization processes. The observed “source” chronology was receptacles, stems and green leaves, and we confirmed the “sink” chronology of receptacles, hulls, protein and oil. Chronologies were influenced mostly by genotype. Such a framework is highly useful for dynamic modeling of sunflower. We also discuss the relevance and adaptability of the bi-linear model.
Nitrogen (N) availability is the primary nutrient limitation for both total food supply and protein content in food. Nitrogen fertilizers are expensive inputs in cereal cropping systems and loss of N increases cropping costs, reduces crop yields, contributes to soil acidification, and causes off-site pollution of groundwater and waterways. The effects of N and N management on crops have been investigated extensively for shoot characteristics but not for root systems. Although shoots and roots grow and function as discrete organs for the capture of specific resources (CO2, light, H2O, and nutrients), the two systems are coupled, and their functions form an integrated system: the plant. This chapter reviews the current knowledge regarding the effects of N and selected N management strategies on the root systems of cereals and the current approaches used to simulate these effects using mathematical models. It is concluded that due to the complexity associated with the fate of the applied N fertilizer, simulation models are fundamental in developing scenarios that predict potential consequences of N management practices. Lack of experimental root data is identified as the main factor limiting the possibility to upgrade current models to be more precise and to include responses that are currently not considered.
Please view the pdf by using the Full Text (PDF) link under 'View' to the left. Copyright © 2013. ASA, CSSA, SSSA, 5585 Guilford Rd., Madison, WI 53711-5801, USA.
ResearchGate has not been able to resolve any references for this publication.