Cropping System Model Research Articles

A comprehensive R interface for the DSSAT Cropping Systems Model

The Decision Support System for Agrotechnology Transfer Cropping Systems Model (DSSAT-CSM) is a widely used modeling system. The DSSAT R package was developed to provide tools that would facilitate preparing required model inputs, executing simulations, and processing and analyzing outputs for DSSAT-CSM. This application note demonstrates the use of this new package for building reproducible crop modeling workflows using the DSSAT-CSM system. Example workflows are provided for modifying values in input data files (soil, weather, and experimental details), running simulations, reading simulated output, and creating publication-quality visualizations of observed and simulated data. The DSSAT R package provides basic tools that when combined with other R packages will facilitate developing robust, reproducible, scientific modeling workflows.

Adaptation strategies for maize production under climate change for semi-arid environments

Abstract Maize is the third most cultivated food crop in the world. Therefore, the impact of climate change and the development of adaptation strategies for maize are crucial to agricultural production and food security. The current study was undertaken to evaluate the impact of climate change and the development of adaptations strategies for maize in semi-arid environments using the Cropping System Model (CSM)-CERES-Maize of the Decision Support System for Agrotechnology Transfer (DSSAT). The model was calibrated and evaluated with an experimental data set and compared to on-farm data. The sensitivity of the model was evaluated against Carbon, Temperature, Water and Nitrogen (CTWN) analysis for the same environments. Survey data for maize were collected from 64 farms in the Faisalabad district of Pakistan using a stratified random sampling technique. Initial crop conditions and management practices were used as input data for CSM-CERES-Maize. Current climate data from 1980 to 2010 were obtained from the nearest weather station and future climate projections for 2040–2069 were obtained from Global Climate Models (GCMs) under Representative Concentration Pathway (RCP) 8.5. Representative Agricultural Pathways (RAPs) were designed to represent the future autonomous production system. The GCM results showed an increase of 3.4 °C in maximum and 3.8 °C in minimum temperature for hot/dry conditions. The projected increase in temperatures for the hot/dry GCM would result in a 28 % reduction for the current production system and a 29 % reduction for the future maize production system by the middle of the century. The impact of climate adaption options on current production systems was evaluated and the results showed that yield increased by 21 %. Results of climate adaptation for the future production system indicated that yield would increase by 12–17 % for all GCMs. Both the current and future production systems were negatively affected by climate change. However, improved management as adaptation strategies can offset the potential decrease in yield.

Combining Simple Phenotyping and Photothermal Algorithm for the Prediction of Soybean Phenology: Application to a Range of Common Cultivars Grown in Europe.

Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios.

Assessing the Potential Impact of Climate Change on Rice Yield in the Artibonite Valley of Haiti Using the CSM-CERES-Rice Model

HighlightsRice yield decreased in the spring-summer and summer-autumn seasons and increased in the winter-spring season.The average annual rice yield in the Artibonite Valley is expected to decrease.MarkSim climate data linked with DSSAT provide a means to simulate climate change impacts on crop yield.Abstract. Rice (Oryza sativa) is one of the major crops in the world and one of the most consumed agricultural products in Haiti, with the main production area in the Artibonite Valley. Crop management, poor soil conditions, and weather uncertainty affect rice production in this region. The objective of this study was to determine the potential impact of climate change on rice yield in the Artibonite Valley of Haiti for future periods (near-term: 2010-2039 and mid-century: 2040-2069) under two Representative Concentration Pathways (RCPs 4.5 and 8.5) defined by the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). The Crop Estimation Resource and Environment Synthesis (CERES)-Rice model of the Decision Support for Agrotechnology Transfer (DSSAT) cropping system model was used to perform the simulations using local soil characteristics, meteorological data, and crop management following model calibration with local experimental data. Temperature (maximum and minimum) was predicted to increase during all three rice-growing seasons (spring-summer, summer-autumn, and winter-spring). Under both RCPs (4.5 and 8.5), the simulation results indicated that the ensemble-mean rice yield decreased during the spring-summer and summer-autumn seasons (by 5.1% to 6.6% and by 5.4% to 8.3%, respectively) and increased during the winter-spring season (by 2.3% to 3.6%). Although yield increased during the winter-spring season, the average annual yield was predicted to decrease by 3.6% to 7.1% and by 4.2% to 9.6% for the near-term and mid-century climate periods, respectively. These findings could assist with the implementation of adaptation strategies to mitigate the projected negative impact of climate change on rice production in Haiti. Keywords: Cropping system model, DSSAT, Food security, Global climate model, Rice production, Systems analysis.

Analyzing the Performance and Application of CERES-Wheat and APSIM in the Guanzhong Plain, China

HighlightsAPSIM and CERES-Wheat were calibrated and evaluated for winter wheat in the Guanzhong Plain, China.Both models performed well as the result of the calibration based on comprehensive field data.CERES-Wheat showed high sensitivity to field capacity.APSIM showed high sensitivity to nitrate at sowing.A crop model ensemble is better than a single model application, particularly for grain yield.Abstract. Cropping system models are useful tools to estimate the impact of climate and environment on agricultural production and to improve the management of agricultural systems. Numerous crop models are used worldwide to simulate wheat crop growth and predict yield. In this study, two process-based crop models (APSIM and CERES-Wheat) were assessed for simulating wheat crop growth in the Guanzhong Plain, China. The main objectives were to compare the performance of both models and perform sensitivity analysis. The possibility of reducing the model output uncertainty by estimating ensemble means was also explored. Crop models were calibrated and evaluated using local experimental data from three growing seasons (2009 to 2012), which encompassed 27 crop managements based on different levels of irrigation and nitrogen application. APSIM and CERES-Wheat performed well after calibration. The nRMSE for aboveground biomass, grain yield, LAI, canopy nitrogen, cumulative evapotranspiration, water use efficiency, and nitrogen fertilizer productivity was less than 21%. Sensitivity analysis of both models showed that grain yield in CERES-Wheat was more sensitive to field capacity. In APSIM, grain yield was sensitive to nitrogen application rate. Mean ensemble model results showed reduced uncertainty for simulated crop parameters, especially for differences between observed and simulated grain yield, which were reduced by up to 4.1%. Overall, the results suggest that APSIM and CERES-Wheat are both suitable for simulating wheat crop growth, development, and yield in the Guanzhong Plain. Furthermore, by using the ensemble modeling approach, decision-makers can be more confident in their planning, as the information will be based on multiple models’ results. Keywords: Crop model ensemble, Model calibration, Multiple crop models, Sensitivity analysis, Winter wheat.

Using a Crop Modeling Framework for Precision Cost-Benefit Analysis of Variable Seeding and Nitrogen Application Rates

A key goal of precision agriculture is to achieve the maximum crop yield while minimizing inputs and loses from cropping systems. The challenge of precision agriculture is these factors interact with one another on a subfield scale. The seeding density and the nitrogen (N) fertilizer application rate are two of the most important inputs influencing agronomic, economic and environmental outcomes including yield, return on investment (ROI) and nitrate (NO3-) leaching. A cropping system simulation framework is used to predict site-specific subfield optimum seeding density and (N) fertilizer application rates for the economic optimum (maximum ROI) versus agronomic optimum (maximum yield). The framework couples the process-based APSIM cropping system model with the SSURGO soils database, Daymet weather data service, land grant university estimates of crop production costs and commodity price estimates, and the R statistics software. The framework performance was evaluated using multiple years of precision yield monitor data obtained from a continuous maize (Zea mays L.) cropping system field experiment with varying N-fertilizer rates. Subfield model estimates of crop yield were sensitive to initial conditions related to historical management of the field and had an r2 = 0.65 and a root mean square error of 2356.0 kg ha-1. A site-specific application of the framework comparing economic optimum seeding density and N-fertilizer rates with agronomic optimum values estimated the average ROI benefit to be up to 12.1% with a NO3- leaching reduction of up to 15.2 kg ha-1 at the economic optimum. However, in a minority of cases NO3- leaching was greater at the economic optimum, indicating that managing to maximize ROI rather than yield may not always reduce environmental impacts. Our results suggest that managing cropping systems for the economic optimum is plausible using publicly available data with our framework and may likely lead to improved environmental outcomes.

Simulating Growth and Development Processes of Quinoa (Chenopodium quinoa Willd.): Adaptation and Evaluation of the CSM-CROPGRO Model

In recent years, the intra-annual yield variability of traditional food crops grown in Europe increased due to extreme weather events driven by climate change. The Andean crop quinoa (Chenopodium quinoa Willd.), being well adapted to drought, salinity, and frost, is considered to be a promising new crop for Europe to cope with unfavorable environmental conditions. However, cultivation guidelines and cropping experiences are missing on a long-term scale. The adaptation of a mechanistic crop growth model will support the long-term evaluation of quinoa if grown under the diverse environmental conditions of Europe. The objective of this study was to adapt the process-based cropping system model (CSM) CROPGRO, which is included in the Decision Support System for Agrotechnology Transfer (DSSAT). Therefore, species and genetic coefficients were calibrated using literature values and growth analysis data, including crop life cycle, leaf area index (LAI), specific leaf area (SLA), dry matter partitioning and nitrogen concentrations in different plant tissues, aboveground biomass, and yield components, of a sowing date experiment (covering two cultivars and four sowing dates) conducted in southwestern Germany in 2016. Model evaluation was performed on the crop life cycle, final aboveground biomass, and final grain yield for different sowing dates using an independent data set collected at the same site in 2017. The resulting base temperatures regarding photosynthetic, vegetative, and reproductive processes ranged between 1 and 10 °C, while the corresponding optimum temperatures were between 15 and 36 °C. On average, the crop life cycle was predicted with a root mean square error (RMSE) of 4.7 and 3.0 days in 2016 and 2017, respectively. In 2016, the mean predicted aboveground biomass during the growth cycle showed a d-index of 0.98 (RMSE = 858 kg ha−1). Furthermore, the LAI, SLA, and leaf nitrogen concentrations were simulated with a high accuracy, showing a mean RMSE of 0.29 (d-index = 0.94), 25 cm2 g−1 (d-index = 0.88), and 0.51% (d-index = 0.95). Evaluations on the grain yield and aboveground biomass across four sowing dates in 2017 suggested a good robustness of the new quinoa model. The mean predicted aboveground biomass and grain yield at harvest maturity were 6479 kg ha−1 (RMSE = 898.9 kg ha−1) and 3843 kg ha−1 (RMSE = 450.3 kg ha−1), respectively. Thus, the CSM-CROPGRO model can be used to evaluate the long-term suitability, as well as different management strategies of quinoa under European conditions. However, further development on the simulation of small seed sizes and under water or nitrogen-limited environments are needed.

Can multi-strategy management stabilize nitrate leaching under increasing rainfall?

The increased spring rainfall intensity and amounts observed recently in the US Midwest poses additional risk of nitrate (NO3) leaching from cropland, and contamination of surface and subsurface freshwater bodies. Several individual strategies can reduce NO3 loading to freshwater ecosystems (i.e. optimize N fertilizer applications, planting cover crops, retention of active cycling N), but the potential for synergistic interactions among N management practices has not been fully examined. We applied portfolio effect (PE) theory, a concept originally developed for financial asset management, to test whether implementing multiple N management practices simultaneously produces more stable NO3 leaching mitigation outcomes than what would be predicted from implementing each practice independently. We analyzed simulated data generated using a validated process-based cropping system model (APSIM) that covers a range of soils, weather conditions, and management practices. Results indicated that individual management practices alone explained little of the variation in drainage NO3 loads but were more influential in the amount of residual soil NO3 at crop harvest. Despite this, we observed a general stabilizing effect from adopting well-designed multi-strategy approaches for both NO3 loads and soil NO3 at harvest, which became more pronounced in years with high spring rainfall. We use the PE principle to design multi-strategy management to reduce and stabilize NO3 leaching, which resulted in 9.6% greater yields, 15% less NO3 load, and 61% less soil NO3 at harvest than the baseline typical management. Our results make the case for applying the PE to adapt NO3 leaching mitigation to increased climate variability and change, and guide policy action and on-the-ground implementation.

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies.

Genomic prediction of complex traits, say yield, benefits from including information on correlated component traits. Statistical criteria to decide which yield components to consider in the prediction model include the heritability of the component traits and their genetic correlation with yield. Not all component traits are easy to measure. Therefore, it may be attractive to include proxies to yield components, where these proxies are measured in (high-throughput) phenotyping platforms during the growing season. Using the Agricultural Production Systems Simulator (APSIM)-wheat cropping systems model, we simulated phenotypes for a wheat diversity panel segregating for a set of physiological parameters regulating phenology, biomass partitioning, and the ability to capture environmental resources. The distribution of the additive quantitative trait locus effects regulating the APSIM physiological parameters approximated the same distribution of quantitative trait locus effects on real phenotypic data for yield and heading date. We use the crop growth model APSIM-wheat to simulate phenotypes in three Australian environments with contrasting water deficit patterns. The APSIM output contained the dynamics of biomass and canopy cover, plus yield at the end of the growing season. Each water deficit pattern triggered different adaptive mechanisms and the impact of component traits differed between drought scenarios. We evaluated multiple phenotyping schedules by adding plot and measurement error to the dynamics of biomass and canopy cover. We used these trait dynamics to fit parametric models and P-splines to extract parameters with a larger heritability than the phenotypes at individual time points. We used those parameters in multi-trait prediction models for final yield. The combined use of crop growth models and multi-trait genomic prediction models provides a procedure to assess the efficiency of phenotyping strategies and compare methods to model trait dynamics. It also allows us to quantify the impact of yield components on yield prediction accuracy even in different environment types. In scenarios with mild or no water stress, yield prediction accuracy benefitted from including biomass and green canopy cover parameters. The advantage of the multi-trait model was smaller for the early-drought scenario, due to the reduced correlation between the secondary and the target trait. Therefore, multi-trait genomic prediction models for yield require scenario-specific correlated traits.

Agronomic management and climate change scenario simulations on productivity of rice, maize and wheat in central Nepal using DSSAT ver 4.5 crop model

Average productivity of 3.50 t/ha of rice, 2.50 t/ha of maize and 2.45 t/ha of wheat in Nepal have been very less than their potential productivity for which précised agronomic management and changing climatic scenarios have been reported the most challenging factors at present. Cropping system Model (CSM)-Crop Estimation through Resource and Environment Synthesis (CERES)- Rice, Maize and Wheat, embedded under Decision Support System for Agro-technology Transfer (DSSAT) ver. 4.5 was evaluated from a datasets of farmers’ field experimentations of the central Nepal (Terai-Nawalpur and mid-hill-Kaski districts), and showed high sensitivity of model over change in different agronomic management and climate change scenarios. Model calibration was done by using maximum attainable yield treatments for all tested cultivars while validation was accomplished by using the remaining treatments for predicting growth, phenology and yield of all crop cultivars and results were found perfectly matched with the observed results. Further, the different agronomic management options and climate change scenarios as advocated by IPCC for 2020, 2050 and 2080 from base line of 1995 was studied to simulate the growth and yield performance of diverse crop cultivars. The hybrids and short duration cultivars of all three cereals were found more affected due to climate change than the local and long duration crop cultivars. The model simulation results obtained on rice, maize and wheat using DSSAT ver 4.5 model highlighted that there is utmost importance to develop new climate ready crop cultivars to feed the future generation over different climate change scenarios as suggested by IPCC, 2007 and the simulation results should be extrapolated to the major domains of similar agro-ecozones in Nepal. It is suggested that CSM- CERES- model would be reliable and valid approach for getting strategic decision support system especially with regards to the climate change adaptation measures in Nepal.

Agricultural Adaptation to Global Warming in the Tibetan Plateau.

The Tibetan plateau is one of the most sensitive areas in China and has been significantly affected by global warming. From 1961 to 2017, the annual air temperature increased by 0.32 °C/decade over the Tibetan Plateau, which is the highest in the whole of China. Furthermore, this is a trend that is projected to continue by 0.30 °C/decade from 2018 to 2050 due to global warming using the Regional Climate Model version 4 (RegCM4). The increased temperature trend in recent decades has been highest in winter, which has been positive for the safe dormancy of winter wheat. In order to investigate agricultural adaptation to climate change in the Tibetan plateau, we used the World Food Studies (WOFOST) cropping systems model and weather data from the regional climate model RegCM4, to simulate winter wheat production in Guide county between 2018 and 2050. The simulated winter wheat potential yields amounted to 6698.3 kg/ha from 2018 to 2050, which showed the wheat yields would increase by 81%, if winter wheat was planted instead of spring wheat in the Tibetan Plateau with the correct amount of irrigation water. These results indicate that there are not only risks to crop yields from climate change, but also potential benefits. Global warming introduced the possibility to plant winter wheat instead of spring wheat over the Tibetan Plateau. These findings are very important for farmers and government agencies dealing with agricultural adaptation in a warmer climate.

Evaluation of crop-growth-stage-based deficit irrigation strategies for cotton production in the Southern High Plains

Identification of efficient crop-growth-stage-based deficit irrigation strategies for cotton (Gossypium hirsutum L.) can play a pivotal role in optimizing the use of available irrigation water in the Southern High Plains (SHP) region, which is facing severe challenges from rapidly declining groundwater levels in the underlying Ogallala Aquifer. The objective of this study was to suggest efficient crop-growth-stage-based deficit irrigation strategies for cotton under nine different climate variability classes using the CROPGRO-Cotton module available in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM). Cotton growth stages considered in this study include: i) germination and seedling emergence (GS1), ii) squaring (GS2), iii) flower initiation/ early bloom (GS3), iv) peak bloom (GS4), and v) cutout, late bloom and boll opening stage (GS5). The amount of seasonal irrigation water applied was varied from 120 to 540 mm under eight different irrigation scheduling scenarios with four irrigation application rates of 3, 6, 8 and 9 mm d−1 using the subsurface drip irrigation method. Under each scenario, six growth-stage-based irrigation treatments were adopted, resulting in a total of 48 irrigation strategies. Results indicated that imposing water deficit in the initial (GS1 to GS2) or final (GS5) growth stages had little effect on seed cotton yield. The peak bloom growth stage (GS4) was found to be the most sensitive stage to water stress, and imposing water deficit during this stage resulted in the lowest irrigation water use efficiency (IWUE) and seed cotton yield under most climate variability classes. Application of higher than 420 mm irrigation did not significantly contribute to an increase in seed cotton yield and resulted in a decline in IWUE. The results from this study are useful for the SHP producers to make appropriate crop-growth-stage-based deficit irrigation management decisions for achieving higher seed cotton yield while conserving precious irrigation water resources from the Ogallala Aquifer.

Simulation‐based Maize–Wheat Cropping System Optimization in the Midhills of Nepal

Rainfed maize (Zea mays L.)–wheat (Triticum aestivum L.) production in the midhill region of Nepal is limited in the winter season by cool temperatures, low rainfall, and low fertility. Here, we use the Decision Support System for Agricultural Transfer Cropping System Model (DSSAT‐CSM) to simulate potential productivity for conservation agriculture (CA) and conventional practice (CP), contrasting N application rates, and wheat planting dates ranging from 1 September to 15 December under six different weather scenarios in the midhills of Nepal. Total maize plus wheat aboveground biomass increased up to 56% with longer vs. shorter duration maize varieties, but the subsequent dry‐season wheat failed 71% of the time after late‐planted, long‐season maize in areas where mean seasonal temperature was below 14°C. The optimal wheat planting date increased from August to December as seasonal rainfall amount increased. Increasing N application rate in wheat from 50 to 150 kg ha–1 resulted in significant yield and net profit gains for all weather scenarios given current grain and fertilizer prices in the midhills, especially where conditions were cool and rainfall exceeded 400 mm. Principal component analysis of soil and climate parameters indicated that CA benefits most from higher N application rates when conditions are cool and wet due to increased residue retention coupled with slow soil organic matter decomposition.Core Ideas Conservation agriculture out‐yields conventional practice in wheat for weather scenarios with high seasonal rainfall. Insufficient growing degree days may force earlier than optimal wheat planting in cooler midhill weather scenarios. Optimal wheat planting date occurs before October in warmer and drier midhill weather conditions.

Dairy-CropSyst: Gaseous emissions and nutrient fate modeling tool

Dairy concentrated animal feeding operations (CAFO) are required to implement nutrient management plans enforced by environmental protection agencies to minimizing the risk of water resource degradation and to report gaseous emissions when they exceed certain threshold values. Although tools exist to aid in completing such tasks, few of the tools integrate the impact of on-farm manure treatment unit operations such as anaerobic digestion, solids separation, and nutrient recovery. Furthermore, existing tools do not estimate the nutrient value of recovered products and effluent leaving the dairy system or the nutrient fate after effluent is applied to crop fields.Dairy-CropSyst is a decision support tool for researchers and CAFO managers aimed at evaluating the effects of different manure treatment unit operations on gaseous emission and nutrient fate in dairy systems. The model tracks nutrients through the dairy system, including inorganic and organic forms of carbon, nitrogen, and phosphorus. This is accomplished by integrating established transformation and emission equations, performance parameters of manure treatments from industrial data and literature, and using a cropping system model for the land application evaluation. Comparison of simulated values with observed emissions indicated that model performance was reasonable. The overall correlation coefficient of observed and predicted ammonia and methane emission from dairy manure management was 0.72 and 0.58 respectively. Simulated and observed N2O emission were found in close agreement before and after the irrigation event, however, the model over estimated N2O emission after irrigation events (Coefficient of Residual Mass = −0.87). The use of Dairy-CropSyst has the potential to assist the dairy industry in decision making on manure management treatment strategies and as a tool for reporting GHG and ammonia emissions.

Taking the climate risk out of transplanted and direct seeded rice: Insights from dynamic simulation in Eastern India

Rice productivity in Eastern Indo-Gangetic plains (EIGP) is extremely low, in part due to the prevailing practice of cultivating long-duration transplanted rice under rainfed conditions which leads to water stress and significant yield losses in many seasons. Rice establishment alternatives such as direct seeded rice (DSR) require less water at planting but also are accompanied by climate risks that constrain adoption. For both conventional transplanted and DSR systems, successfully addressing climate-based production risks may provide a strong basis for sustainable rice intensification in EIGP. In this ex ante study of rice yield and yield variability, the APSIM cropping system model was used to evaluate the efficacy of risk-reducing management practices in both transplanted and DSR systems. Simulations were conducted with 44 years (1970–2013) of historical weather data from central Bihar, India. Results confirm that the prevailing farmer practice of transplanting long-duration cultivars under rainfed conditions (fTR) often results in delayed transplanting and the use of older seedlings, leading to low (median 1.6 t ha−1) and variable (Standard deviation (SD) 2.1 t ha−1) rice yields. To improve the fTR system, simulations suggest that adoption of medium-duration hybrid rice (3.2 t ha−1), provision of supplemental post-establishment irrigation (3.2 t ha−1), or transplanting appropriately aged seedlings (3.4 t ha −1) can double yields as single interventions while, in the case of supplemental irrigation, significantly reducing inter-annual production variability. Additional gains are achievable when interventions are layered: supplemental irrigation paired with medium-duration hybrids increased median rice yields to 4.6 t ha−1 with much lower variability (SD 1.0 t ha−1). In these improved systems where irrigation is used to transplant the crop, simulations revealed the importance of timely planting: high and stable yields are achievable for long-duration cultivars when transplanting is completed by 2 August with this window of opportunity extending to 16 August for medium-duration hybrids. In rainfed DSR systems, the potential pay-offs from single interventions were even higher with medium-duration hybrids resulting in a median yield of 4.5 t ha-1 against 1.8 t ha−1 with long-duration cultivars. For irrigated DSR systems, an optimum sowing window of early to mid-June was identified which resulted in higher and more stable yields with lower water requirements. Simulation results suggest several risk-reducing intensification pathways that can be selectively matched to farmer risk preferences and investment capabilities within the target region in EIGP.

A rapid, spatially explicit approach to describe cropping systems dynamics at the regional scale

Land managers need spatially explicit information about agricultural practices to address issues that arise from the use of natural resources in agriculture. One main characteristic of agriculture is its great variability in space and time. However, describing the spatial distribution of “cropping systems”, i.e. crop sequences and crop management systems at the regional scale, remains a major scientific challenge. This study presents a new, simple and rapid approach to model the spatial distribution of irrigation management practices. It was developed in two large watersheds in southwestern France (about 1500 and 3000 km2). Based on a previous study consisting of 27 farmer interviews in a study area about one-sixth the size of these watersheds, we interviewed 12 key informants who had an integrative vision of the study area and spent only one-fourth as much time collecting and processing the relevant data. One major innovation was to combine knowledge from generic databases and ad-hoc intermediate objects, such as diagrams, tables and maps, to interact with the key informants. These objects helped them focus on specific local information that we had missed and facilitated data processing. Interview results were used to spatially allocate cropping systems formalized as dynamic IF-THEN decision rules. We evaluated our approach by using a cropping system model to simulate irrigation withdrawals over a ten-year period. Its predictions reproduced well annual amounts and inter-annual dynamics of irrigation water withdrawals recorded by the regional Water Agency. This approach, combining diagrams with IF-THEN rules, appears easy to adapt to study other areas and agricultural practices besides irrigation, as well as to manage annual and perennial crops.

Long-term effect of cover crop on rainwater balance components and use efficiency in the no-tilled and rainfed corn and soybean rotation system

Incorporating cover crops into row crop production systems can affect soil water dynamics and crop production. However, the effect of this practice has not been well investigated in the northeast Mississippi USA. We calibrated and validated the cropping system model (Root Zone Water Quality Model, RZWQM2) using 4-yr (2014–2017) field data in the humid Mississippi Blackland Prairie, and also used this model to simulate the long-term (1938–2017) effects of a winter wheat (Triticum aestivum L.) cover crop on hydrological variables, crop yield, and water use efficiency (WUE, grain yield per unit of evapotranspiration) in a rainfed and no-tilled corn (Zea mays L.) and soybean (Glycine max L.) cropping system. Compared to no cover crop scenarios, long-term simulation demonstrates that average annual percolation under the cover crop system was decreased by 36 mm. Average annual actual evapotranspiration were 36 mm higher under cover crop system than no cover crop system. Simulated annual runoff for cover crop scenario was not different from values simulated for no cover crop scenario. Predicted actual evaporation during cash crop growth periods under cover crop plots was 25% less than under no cover crop plots due to mulch cover when averaged over the whole modeling period. Compared with no cover crop scenarios, the estimated crop evapotranspiration under cover crop scenario was reduced by 6.6% (31 mm) during corn growth period and by 3.7% (19 mm) during soybean growth period. Yearly predicted crop yields for corn and soybean did not improve, respectively, in the cover crop-based cropping system. Compared to the plots with no cover crop, the simulation of WUE for corn and soybean were respectively improved by 6.4% (1.49 kg m−3 versus 1.40 kg m−3) and by 5.0% (0.63 kg m−3 versus 0.60 kg m−3) for the plots with cover crop, largely due to the decrease in surface evaporation without sacrificing crop growth. These results suggest that long-term use of wheat cover crop to summer crops rotation is a promising practice to decrease deep percolation and restrict surface evaporation, and also improved crops WUE in the corn and soybean rotation in subtropical agro-system.

Assessing climate adaptation options for cereal-based systems in the eastern Indo-Gangetic Plains, South Asia

AbstractNew farming systems and management options are needed in South Asia as the intensive rice–wheat production system is set to become increasingly unsustainable under climate change. In the current study, six cropping systems options/treatments varying in tillage, crop establishment method, residue management, crop sequence and fertilizer and water management were evaluated using a cropping systems model under current (1980–2009) and future (2030 and 2050) climate scenarios in the state of Bihar, India. The treatments were current farmers' practice (CP), best fertilizer and water management practices, zero tillage (ZT) with no crop residue retention, ZT with partial crop residue retention (ZTPR), future conservation agriculture-based rice–wheat intensive cropping system (FCS-1) and future conservation agriculture-based maize–wheat intensive cropping system (FCS-2). The results indicate that climate change is likely to reduce rice–wheat system productivity under CP by 4% across Bihar. All the crop management options studied increased yield, water productivity and net returns over that of the CP under the current and future climate scenarios. However, the ZTPR treatment gave significantly higher relative yield, lower annual yield variability and a higher benefit-cost-ratio than the other treatments across cropping system components and climate periods. Although all the new cropping system treatments had a positive yield implication under the current climate (compared to CP), they did not contribute to adaptation under the future climate except FCS-2 in wheat. It is concluded that adaptation to future climate must integrate both cropping system innovations, and genetic improvements in stress tolerance.

Assessing the Performance of the WOFOST Model in Simulating Jujube Fruit Tree Growth under Different Irrigation Regimes

Cropping system models are widely employed to evaluate plant water requirements and growth situations. However, these models rarely focus on growth studies of perennial fruit trees. The aim of this study was to evaluate the performance of the WOFOST (WOrld FOod STudies) model in simulating jujube fruit tree growth under different irrigation treatments. The model was calibrated on data obtained from full irrigation treatments in 2016 and 2017. The model was validated on four deficit percentages (60%, 70%, 80%, and 90%) and one full irrigation treatment from 2016 to 2018. Calibrated R2 and RMSE values of simulated versus measured soil moisture content, excluding samples on the day of irrigation and first day after irrigation, reached 0.94 and 0.005 cm3 cm−3. The model reproduced growth dynamics of the total biomass and leaf area index, with a validated R2 = 0.967 and RMSE = 0.915 t ha−1, and R2 = 0.962 and RMSE = 0.160 m2 m−2, respectively. The model also showed good global performance, with R2 = 0.86 and RMSE = 0.51 t ha−1, as well as good local agreement (R2 ≥ 0.8 ) and prediction accuracy (RMSE ≤ 0.62 t ha−1) for each growth season. Furthermore, 90% of full irrigation can be recommended to achieve a balance between jujube yields and water savings (average decline ratio of yield ≤ 3.8%).

Modeling irrigation and nitrogen management of wheat in northern Ethiopia

Wheat (Triticum aestivum) is one of the most important staple food crops in Ethiopia. However, its production is limited by moisture and nutrient stresses. A field experiment was conducted in northern Ethiopia to: (i) evaluate the effects of irrigation and nitrogen (N) and phosphorus (P) fertilizer application rates on yield, biomass and irrigation water productivity (IWP) of wheat; (ii) calibrate and validate a crop model for simulating yield and biomass of wheat under different levels of nitrogen and irrigation applications; (iii) evaluate consecutive above-ground biomass accumulation as affected by different combinations of irrigation and N fertilizer rates. The Decision Support for Agro technology Transfer Cropping System Model (DSSAT-CSM) was calibrated and validated with experimental data. The calibrated and validated DSSAT-CSM was used to simulate wheat biomass, yield, and irrigation water productivity under rainfed and three irrigation scenarios: I0, rainfed; DI1, two irrigations from heading to flowering; DI2, four irrigations from heading to early grain filling; and DI3, six irrigations from heading to mid grain filling period in combination with nine N rates (0, 16, 32, 64, 80, 96, 112, 128 and 160 kg/ha). Simulation results showed that both irrigation and nitrogen applications positively affected wheat yield, biomass and IWP. Much of the increase in biomass and yield was due to increased N than to increased irrigation. Yield increased with increase in N application rates, however, at a diminishing rate yielding a curvilinear relationship. Four irrigation (DI2) starting from heading to early grain filling stage resulted in a yield similar to DI3, six irrigation applications from heading to mid grain filling stage. Simulations showed that two irrigation applications strategy (DI1) yielded relatively higher IWP (1.8 kg/m3) at the highest application rate of 160 kg N/ha. Further economic analysis would help to identify most efficient practices for wheat production in northern Ethiopia.