Cropping System Model Research Articles

Grass-fed vs. grain-fed beef systems: performance, economic, and environmental trade-offs.

Between increasing public concerns over climate change and heightened interest of niche market beef on social media, the demand for grass-fed beef has increased considerably. However, the demand increase for grass-fed beef has raised many producers' and consumers' concerns regarding product quality, economic viability, and environmental impacts that have thus far gone unanswered. Therefore, using a holistic approach, we investigated the performance, carcass quality, financial outcomes, and environmental impacts of four grass-fed and grain-fed beef systems currently being performed by ranchers in California. The treatments included 1) steers stocked on pasture and feedyard finished for 128 d (CON); 2) steers grass-fed for 20 mo (GF20); 3) steers grass-fed for 20 mo with a 45-d grain finish (GR45); and 4) steers grass-fed for 25 mo (GF25). The data were analyzed using a mixed model procedure in R with differences between treatments determined by Tukey HSD. Using carcass and performance data from these systems, a weaning-to-harvest life cycle assessment was developed in the Scalable, Process-based, Agronomically Responsive Cropping Systems model framework, to determine global warming potential (GWP), consumable water use, energy, smog, and land occupation footprints. Final body weight varied significantly between treatments (P < 0.001) with the CON cattle finishing at 632kg, followed by GF25 at 570kg, GR45 at 551kg, and GF20 478kg. Dressing percentage differed significantly between all treatments (P < 0.001). The DP was 61.8% for CON followed by GR45 at 57.5%, GF25 at 53.4%, and GF20 had the lowest DP of 50.3%. Marbling scores were significantly greater for CON compared to all other treatments (P < 0.001) with CON marbling score averaging 421 (low-choice ≥ 400). Breakeven costs with harvesting and marketing for the CON, GF20, GR45, and GF25 were $6.01, $8.98, $8.02, and $8.33 per kg hot carcass weight (HCW), respectively. The GWP for the CON, GF20, GR45, and GF25 were 4.79, 6.74, 6.65, and 8.31 CO2e/kg HCW, respectively. Water consumptive use for CON, GF20, GR45, and GF25 were 933, 465, 678, and 1,250L/kg HCW, respectively. Energy use for CON, GF20, GR45, and GF25 were 18.7, 7.65, 13.8, and 8.85 MJ/kg HCW, respectively. Our results indicated that grass-fed beef systems differ in both animal performance and carcass quality resulting in environmental and economic sustainability trade-offs with no system having absolute superiority.

Geo-CropSim: A Geo-spatial crop simulation modeling framework for regional scale crop yield and water use assessment

Remote sensing derived datasets (e.g. Leaf Area Index (LAI)) are increasingly being used in process based cropping system models to improve the prediction skill of the simulations when implementing operationally at regional scale. However, challenges such as inadequate quality of the available remote sensing data products and high reliance of models on climate variables and their uncertainties still exist. To address these challenges, we developed Geo-CropSim, a spatial modeling framework to use high quality remote sensing products in the Environmental Policy Integrated Climate (EPIC) agroecosystem model to regulate simulated processes and improve predictions of crop yield and evapotranspiration. Geo-CropSim comprises three main features 1) pixel level model initialization using crop emergence dates; 2) ability of the EPIC model to read in the PROSAIL (i.e. combined PROSPECT leaf optical properties model and SAIL canopy bidirectional reflectance model) inversion-based crop type LAI; and 3) a stress adjustment function to regulate simulated stress using LAI anomalies. To understand its performance, we implemented it over the State of Nebraska to estimate corn (Zea mays L.) and soybean (Glycine max [Merr.]) yields and evapotranspiration (ET) for 2012 (drought year) and 2015 (normal year) at 500-m resolution. Results showed that emergence dates and seasonal LAI captured spatial and temporal differences in crop progression (e.g. delayed planting in 2015) and growth (e.g. declined LAI in 2012) driven by regional differences in crop management and weather conditions very well. These differences were reflected in Geo-CropSim yield estimates, and showed improved spatial and temporal details compared over those from EPIC simulations obtained without using remote sensing derived emergence and LAI. Results revealed that Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) of Geo-CropSim yield estimates, computed based on USDA-NASS reported yields, were 18.85% and 1.22 Mg ha−1 for corn, and 17.90% and 0.46 Mg ha−1 for soybeans, respectively, which are substantially lower than those of original EPIC estimates (MAPE = 33.74% and RMSE = 2.18 Mg ha−1 for corn; and MAPE = 40.71% and RMSE = 0.98 Mg ha−1 for soybeans). Further, Geo-CropSim was able to capture ET and transpiration dynamics reasonably well (e.g. 10–12 % lower values for soybeans compared to corn values), and showed good agreement with flux measurements (i.e. R2 values of 0.63 and 0.72, RMSE values of 29.88 and 33.41 mm, and MAPE values of 5.0% and 6.8% for corn and soybean, respectively). Overall, this study demonstrated that Geo-CropSim has considerable potential to serve as a reliable operational tool to assess crop yields and water use under various cropping systems and to help in regional yield monitoring and water resource management.

Investigation of satellite-related precipitation products for modeling of rainfed wheat production systems

Precipitation is a very important weather variable for growth and yield of rainfed crops. In many agricultural regions of the world, high-quality precipitation records are not available, and thus, gridded precipitation products (GPPs) have to be applied as an alternative. The main objective of this study was to identify the most accurate GPP for simulating crop yield over a major rainfed wheat production zone in Iran. For this purpose, fifteen global GPPs were evaluated versus the observed precipitation records for the simulation of rainfed wheat growth and development and yield estimation using the Cropping System Model (CSM) CERES-Wheat model embedded in the Decision Support System for Agrotechnology Transfer (DSSAT). The findings showed that multi-source GPPs had generally higher skill for the yield estimation. Considering all statistical and simulation results obtained from three sites during 2000–2010, MSWEP (Multi-Source Weighted-Ensemble Precipitation) was found as the best alternative GPP to the observed precipitation data for rainfed wheat grain yield simulation with normalized root mean square error (NRMSE) of 4.6 and Nash–Sutcliffe efficiency (NSE) of 0.79, while CMORPH (the Climate Prediction Center morphing method) was the weakest with NRMSE of 13.3 and NSE as − 0.81. The results point to differences among GPP, but there is a need to evaluate in other regions if multi-purpose GPPs are in general more reliable than GPPs based on specific sources.

Building Climate-Resilient Cotton Production System for Changing Climate Scenarios Using the DSSAT Model

Cotton production is highly vulnerable to climate change, and heat stress is a major constraint in the cotton zone of Punjab, Pakistan. Adaptation is perceived as a critical step to deal with forecasted and unexpected climatic conditions. The objective of this study was to standardize and authenticate a cotton crop model based on climate and crop husbandry data in order to develop an adaptation package for cotton crop production in the wake of climate change. For the study, the data were collected from the cotton-growing areas of Punjab, viz. Bahawalpur and Khanewal. After the calibration and validation against field data, the Cropping System Model CSM–CROPGRO–Cotton in the shell of the decision support system for agro-technology transfer (DSSAT) was run with a future climate generated under two representative concentrations pathways (RCPs), viz. RCPs 4.5 and 8.5 with five global circulation models (GCMs). The whole study showed that a model is an artistic tool for examining the temporal variation in cotton and determining the potential impact of planting dates on crop growth, phenology, and yield. The results showed that the future climate would have drastic effects on cotton production in the project area. Reduction in seed cotton yield (SCY) was 25.7% and 32.2% under RCPs 4.5 and 8.5, respectively. The comparison of five GCMs showed that a hot/wet climate would be more damaging than other scenarios. The simulations with different production options showed that a 10% and 5% increase in nitrogen and plant population, respectively, compared to the present would be the best strategy in the future. The model further suggested that planting conducted 15 days earlier, combined with the use of water and nitrogen (fertigation), would help to improve yield with 10% less water under the future climate. Overall, the proposed adaptation package would help to recover 33% and 37% of damages in SCY due to the climate change scenarios of RCP 4.5 and 8.5, respectively. Furthermore, the proposed package would also help the farmers increase crop yield by 7.5% over baseline (current) yield.

Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model

Cropping system models are widely used to assess the impacts of and adaptation practices to climate change on agricultural production. However, crop growth simulations at large scales have often lacked consideration of variation in crop cultivars, which were represented by different sets of genetic coefficients in crops models. In this study, taking the phenology of spring wheat (Triticum aestivum L.) as an example, we compared four different strategies for upscaling genetic parameters in phenology simulations at large scales with two experimental datasets. The first dataset was from field experiments comprising 40 different spring wheat cultivars at Altay (2014) and Yangling (2015–2017) station; the second dataset was historical (2010–2014) observed phenology records from 57 national agro-meteorological observation stations in China. The four strategies were the representative cultivar estimated at a single site (SSPs), the representative cultivar estimated at the 57 sites (NRPs), the various representative cultivars estimated at different agro-ecological zones (RRPs), and the virtual cultivars generated from the posterior distributions (VCPs). The posterior distributions aforesaid were established based on the calibrated parameter values of the 40 different spring wheat cultivars planted in Yangling. Then, 1000 sets of VCPs were randomly sampled from the posterior distributions. The results indicated that both the SSPs and NRPs strategy obtained large errors and uncertainties in spring wheat phenology simulations in China since only one representative cultivar was used. The RRPs strategy achieved the second high and the highest accuracy in anthesis and maturity data simulations. The VCPs strategy obtained the highest accuracy in anthesis simulation but relative larger errors in maturity simulation. The VCPs strategy can be directly used in large-scale crop growth simulations without tedious process of calibration. Hence, this strategy is recommended in areas where observations are scarce and for model users who not good at model parameter estimation.

Effect of winter cereal sowing time on yield and nitrogen leaching based on experiments and modelling

AbstractEarly sowing of winter cereals has been promoted as an alternative to catch crops for reducing nitrogen (N) leaching in intensively managed cropping systems. Underlying data for this are, however, scarce and it is unclear how year‐to‐year variations affect potential reductions in N leaching. Well‐tested cropping system models can be used to assess the impact of early sowing on crop yield and N leaching. We tested the ability of the Agricultural Production System Simulator (APSIM) to simulate grain yield, biomass, phenology, N uptake and N leaching from timely and early‐sown winter cereals. The evaluation was based on two extensive field experiments carried out in Denmark in continuous winter cereals systems with different N fertilization applications, and a farm experiment in Germany with a grass ley/winter wheat crop sequence and two different N fertilization rates. Additionally, observations from growth stages throughout Denmark were used. After changing a few crop model parameters, APSIM reproduced the general effects of N fertilization and crop sequence on yields and N uptake, as well as N leaching. The model was then used to simulate the effect of sowing time on grain yield over a period of 10 years, to capture year‐to‐year variability and enable a better evaluation of the efficacy of early sowing for reducing N leaching. In the continuous winter cereal systems, early sowing reduced N leaching at both sites in Denmark. In contrast, in the grass ley/winter wheat system in Germany early sowing increased N leaching, due to enhanced mineralization of grass residues.

A GIS-based framework for local agricultural decision-making and regional crop yield simulation

CONTEXTIn agricultural activities, the decision-making process is central to agricultural system management and subsequent crop yield. As a powerful tool in field-specific decision-making processes, crop simulation models have the potential to simulate crop yields on a large scale. However, their performance is often biased by the spatial heterogeneity of environment and management factors when applied over a large scale. OBJECTIVEThe major objectives of this study include: (1) Predicting and evaluating the annual yields of dominant crops with real rotation scenarios; (2) Locating fields with low crop yield and determining possible reasons; and (3) Evaluating the improvement for crop yield with different management strategies. METHODSThis study proposed a crop yield simulation framework at the regional level by coupling a cropping system model (CropSyst) with a geographic information system (QGIS) to provide more reliable information for the decision-making process. In the study of a cropland concentrated USGS sub-watershed (Hydrologic Unit Code: 031402030101) in Geneva County, Alabama, we estimated the annual yields of four regionally dominant crops (i.e., corn, cotton, soybean, and peanuts) from 2016 to 2018. Low yield fields were identified in the simulation results visualization. Moreover, four management strategies were tested at a field scale to improve annual yields. RESULTS AND CONCLUSIONSOverall, the simulated crop yields were significantly correlated with the recorded values (Pearson's r = 0.99). However, the performance of the regional model varied for different crops. The model achieved the best performance for soybean with a high index of agreement (0.93) and modeling efficiency (0.86). For cotton, the model achieved positive model efficiency (0.23) and a good index of agreement (0.59). For peanut and maize, the model fitted records well but not sensitive enough. According to the visualization of simulation results, we located fields with low yields. The low organic matter content and high sand percentage of the soil were the potential causes of the nitrogen deficiency, which leads to the low yield subsequently. In field scale tests, four proposed management strategies could increase the cotton yields as high as 74.4%. But some strategies would also increase greenhouse gas emissions at the same time. SIGNIFICANCEThis study bridges the gap between local cropping system models and the regional estimation of crop yields. The GIS-based crop simulation framework developed here demonstrates the potential of cropping system models to provide reliable information at a regional scale and hence significantly broadens their application in the agricultural decision-making process.

Identifying Suitable Genotypes for Different Cassava Production Environments—A Modeling Approach

Crop simulation models can be used to identify appropriate genotypes and growing environments for improving cassava yield. The aim of this study was to determine the best genotypes for different cassava production environments using the cropping system model (CSM)–MANIHOT–Cassava. Data from cassava experiments that were conducted from 2009–2011 and 2014–2015 at Khon Kaen, Thailand, were used to evaluate the model. Simulations were then conducted for different scenarios using four cassava genotypes (Kasetsart 50, Rayong 9, Rayong 11, and CMR38–125–77), twelve planting dates (at monthly intervals starting in January and ending in December), and ten locations in Thailand under fully irrigated and rainfed conditions using 30 years of historical weather data. Model evaluation with the experimental data for total biomass and storage root yield indicated that the model classified well for relative productivity among different planting dates. The model indicated that growing cassava under irrigated conditions generally produced higher biomass and storage root yield than under rainfed conditions. The cassava genotype CMR38–125–77 was identified for high biomass, while the genotype Rayong 9 was identified as a good genetic resource for high yield. The December planting date resulted in the highest biomass for all locations, while the February planting date produced the highest storage root yield for almost all locations. The results from this study suggest that the CSM–MANIHOT–Cassava model can assist in determining suitable genotypes for different cassava production environments for Thailand, and that this approach could be applicable to other cassava growing areas.

Genotype-specific P-spline response surfaces assist interpretation of regional wheat adaptation to climate change

Abstract Yield is a function of environmental quality and the sensitivity with which genotypes react to that. Environmental quality is characterized by meteorological data, soil and agronomic management, whereas genotypic sensitivity is embodied by combinations of physiological traits that determine the crop capture and partitioning of environmental resources over time. This paper illustrates how environmental quality and genotype responses can be studied by a combination of crop simulation and statistical modelling. We characterized the genotype by environment interaction for grain yield of a wheat population segregating for flowering time by simulating it using the the Agricultural Production Systems sIMulator (APSIM) cropping systems model. For sites in the NE Australian wheat-belt, we used meteorological information as integrated by APSIM to classify years according to water, heat and frost stress. Results highlight that the frequency of years with more severe water and temperature stress has largely increased in recent years. Consequently, it is likely that future varieties will need to cope with more stressful conditions than in the past, making it important to select for flowering habits contributing to temperature and water-stress adaptation. Conditional on year types, we fitted yield response surfaces as functions of genotype, latitude and longitude to virtual multi-environment trials. Response surfaces were fitted by two-dimensional P-splines in a mixed-model framework to predict yield at high spatial resolution. Predicted yields demonstrated how relative genotype performance changed with location and year type and how genotype by environment interactions can be dissected. Predicted response surfaces for yield can be used for performance recommendations, quantification of yield stability and environmental characterization.

Irrigation and shifting planting date as climate change adaptation strategies for sorghum

Climate change is projected to have a global impact that affect food production and security. The objectives of this study were to determine the potential impact of climate change on sorghum yield for rainfed production systems and to evaluate the potential of irrigation and shifting planting dates as adaptation options for two major sorghum production regions in Ethiopia. The Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM)-CERES-Sorghum model was used to simulate the impact of climate change on sorghum yield for two Representative Concentration Pathways (RCPs; RCP 4.5 and RCP 8.5) and for three future periods including the 2025s (2010–2039), 2055s (2040–2069), and 2085s (2070–2099). The Agricultural Model Improvement and Inter-comparison Project (AgMIP) framework was used to select five representative GCMs for hot/dry, cool/dry, middle, hot/wet, and cool/wet climate scenarios. Two climate change adaptation practices including supplemental irrigation at two levels (deficit and full) to the current rainfed production system and shifting planting dates were evaluated. The CSM-CERES-Sorghum model was calibrated and evaluated using eight years of experimental data from Meisso, eastern Ethiopia. The model was then run for Kobo and Meisso under different climate change and crop management scenarios. Based on model evaluation results, the model performed well for simulating sorghum yield (R2=0.99), anthesis (R2=0.86, RMSE=1.3), and maturity (R2=0.79, RMSE=4.4). The results showed that the average temperature for Kobo and Meisso is expected to increase by up to 6 °C under RCP8.5 in 2085. For the rainfed production systems without adaptation practices, drought stress is projected to intensify during anthesis, which was reflected by projected yield reductions by up 2 tha−1 for the two sites. Full irrigation was effective in reducing moisture stress and, thereby, increasing sorghum yield by up to 3 tha−1 for Kobo and 2 tha−1 for Meisso. On average, full irrigation resulted in a 1 tha−1 yield increase compared with deficit irrigation. Early planting dates also resulted in an increase in yield compared to the baseline planting dates, especially when combined with supplemental irrigation, although late planting was consistently disadvantageous even with supplemental irrigation. This study highlighted that the CSM-CERES-Sorghum model can be effectively used to simulate climate change effects on sorghum yield and evaluate different climate change adaptation practices. The outcomes of this study can also help to implement management decisions towards climate change adaptation for the current subsistence and fragile rainfed crop production system in Ethiopia and similar ecoregions across the globe.

Rotating maize reduces the risk and rate of nitrate leaching

There is a strong link between nitrate (NO3-N) leaching from fertilized annual crops and the rate of nitrogen (N) fertilizer input. However, this leaching-fertilizer relationship is poorly understood and the degree to which soil type, weather, and cropping system influence it is largely unknown. We calibrated the Agricultural Production Systems sIMulator process-based cropping system model using 56 site-years of data sourced from eight field studies across six states in the U.S. Midwest that monitored NO3-N leaching from artificial subsurface drainage in two cropping systems: continuous maize and two-year rotation of maize followed by unfertilized soybean (maize-soybean rotation). We then ran a factorial simulation experiment and fit statistical models to the leaching-fertilizer response. A bi-linear model provided the best fit to the relationship between N fertilizer rate (kg ha−1) and NO3-N leaching load (kg ha−1) (from one year of continuous maize or summed over the two-year maize-soybean rotation). We found that the cropping system dictated the slopes and breakpoint (the point at which the leaching rate changes) of the model, but the site and year determined the intercept i.e. the magnitude of the leaching. In both cropping systems, the rate of NO3-N leaching increased at an N fertilizer rate higher than the N rate needed to optimize the leaching load per kg grain produced. Above the model breakpoint, the rate of NO3-N leaching per kg N fertilizer input was 300% greater than the rate below the breakpoint in the two-year maize-soybean rotation and 650% greater in continuous maize. Moreover, the model breakpoint occurred at only 16% above the average agronomic optimum N rate (AONR) in continuous maize, but 66% above the AONR in the maize-soybean rotation. Rotating maize with soybean, therefore, allows for a greater environmental buffer than continuous maize with regard to the impact of overfertilization on NO3-N leaching.

Integrating plant-plant competition for nitrogen into a 3D individual-based model simulating the effects of cropping systems on weed dynamics

• We complemented a 3D mechanistic cropping-system model of crop-weed interactions. • We made it the first one to include competition for nitrogen, in addition to light. • With only a few new parameters, simulations were consistent with previous knowledge. • We illustrate the role of this model to analyze crop-weed interactions in the field. • We show its potential as a tool to design biological weed management strategies. Promoting biological weed regulation via competition for resources requires better understanding the functioning of heterogeneous canopies in nitrogen-deficient situations. Mechanistic simulation models are powerful tools to reach this goal. Our objective was to integrate plant-plant competition for nitrogen into the preexisting F lor S ys model simulating the effects of cropping systems on weed dynamics and crop production. The formalisms were either created or inspired from other models and adapted to make them compatible with the individual-based representation of F lor S ys . Plant nitrogen uptake was simulated by confronting plant nitrogen demand (driven by shoot growth) to plant nitrogen supply (depending on root characteristics, soil-nitrogen availability and the presence of neighboring plants with roots in the same soil zone). A nitrogen stress index allowed accounting for the impact of plant nitrogen nutrition on plant photosynthesis, biomass allocation and morphology. The new formalisms consisted of only seven species-specific parameters. Despite simplifying hypotheses in formalisms, predictions were in good agreement with knowledge on canopy functioning and crop-weed interactions. We provide the first mechanistic cropping system model focusing on weeds that simulates plant-plant competition for nitrogen (in addition to competition for light). It will be useful to understand the role of nitrogen in crop-weed interactions and identify agroecological management strategies promoting weed regulation by competition.

Performance of a model in simulating growth and stability for cassava grown after rice

AbstractSelecting the appropriate genotype for growing cassava (Manihot esculenta Crantz) after rice (Oryza sativa L.) can help increase the supply of cassava and improve land use efficiency. However, conducting the selection requires data from many years of multi‐environment yield trials. A systems analysis approach using crop models can support this process. This study aimed to evaluate a potential application of the Cropping System Model (CSM)‐MANIHOT‐Cassava in determining genotype stability across different upper paddy field conditions when planted following rice. Four cassava genotypes, that is, Kasetsart 50, Rayong 9, Rayong 11, and CMR38‐125‐77, were evaluated for the six different environments in Thailand from the 2015 through 2018 growing seasons. The data required for the model were recorded, including biomass and yield. The cultivar coefficients of the CSM‐MANIHOT‐Cassava model for the four genotypes were calibrated and evaluated with the experimental data. The model was then run for historical weather data from 1988 to 2018 for the six environments for production under rain‐fed conditions in upper paddy fields following rice. The overall results showed that the model was able to simulate biomass and yield of the four cassava genotypes quite well when compared to the experimental data. The model identified the same stable genotypes as presented in the actual trials. The genotype CMR38‐125‐77 was found to be a stable genotype and had the highest mean performance for both the actual yield trials and the simulation study. Therefore, the CSM‐MANIHOT‐Cassava model could be used to help identify the favorable genotype for planting in various paddy field conditions after rice harvest.

Performance of the CSM-CROPGRO-soybean in simulating soybean growth and development and the soil water balance for a tropical environment

Continuous monitoring of soil water content is a crucial element for sustainable agricultural water management. The goal of this study was to use the Cropping System Model (CSM)-CROPGRO-Soybean model in conjunction with field data to determine the impact of different irrigation regimes, soil texture, and tillage practices on soybean [Glycine max (L.) Merr.] growth, development, and yield for tropical conditions. Field experiments were conducted at two sites: (i) Piracicaba with conventional tillage (PI-1, season 2016–2017), and no-tillage practices (PI-2, season 2017–2018), where the experiments were irrigated with full water requirements; and (ii) Teresina under conventional tillage (season 2019) with two irrigation treatments of full (TE-1) and 50% (TE-2) water requirements. Soil water content was measured for all experiments using an electromagnetic probe installed at several depths. The results showed that the model was able to simulate soybean growth and development for the different sites, with a very good agreement (D-statistic > 0.8) between the simulated and observed data. In addition, the soil water content was simulated with satisfactory accuracy (D-statistic > 0.5). Following model evaluation, long-term hypothetical scenarios for different soil tillage practices and water management regimes were simulated for Piracicaba and Teresina sites. The results showed that the use of no-tillage could reduce the average amount of irrigation in Piracicaba by 30% and in Teresina by 17%, achieving the same yield level as conventional tillage. Thus, the CSM-CROPGRO-Soybean can be used as a tool for determining optimum water management practices for tropical environments.

Deriving genetic coefficients from variety trials to determine sorghum hybrid performance using the CSM–CERES–Sorghum model

AbstractSorghum (Sorghum bicolor L.) is well‐known to adapt to stressful environmental conditions. However, yield variability of sorghum has not been thoroughly investigated for different growing conditions in a subtropical climate. The overall goal of this study was to identify yield potential and management options for sorghum production in the southeastern United States. Specific objectives were to calibrate and evaluate the Cropping System Model (CSM)–CERES–Sorghum model with sorghum variety trial data and to apply the model for determining hybrid performance as a function of sowing date for different environments in Georgia. The model was calibrated and evaluated with data for six grain sorghum hybrids from 31 variety trials conducted in Georgia and Florida. Following calibration and evaluation, the model was used to simulate grain yield of the six hybrids in response to seven sowing dates under rainfed and irrigated conditions at 10 locations in Georgia using 40 yr of historical weather data. The results indicate that normalized root mean square error (RMSEn) between simulated and observed yield for the six hybrids ranged from 1.4 to 19% for model calibration and from 12 to 28% for model evaluation, suggesting that the model can be calibrated and evaluated using limited data from variety trials. For the long‐term scenarios, differences in simulated grain yield were found for hybrids, sowing dates, and locations under both rainfed and irrigated conditions. This demonstrated that the CSM–CERES–Sorghum model can be used to investigate the effects of climate variability on crop yield and to develop management practices for optimizing sorghum production.

Sustainable intensification of crop production under alternative future changes in climate and technology: The case of the North Savo region

CONTEXTSustainable intensification (SI) is needed to cope with the challenges agriculture faces with respect to climate change and increasing food demand. Northern cropping systems may benefit from longer and warmer growing seasons, but the sustainability of production will be challenged due to increased production risks. Concentrated efforts are needed to find ways to adapt cropping to changing conditions and sustainably intensify production. OBJECTIVEThis study combined stakeholder knowledge and simulation modelling to find means for the sustainable intensification of cereal production in the North Savo region in Finland. METHODSStakeholders identified promising intensification measures in two workshops. Alternative options for sustainable intensification and climate adaptations and their combinations were assessed using the APSIM cropping system model. The model was used to assess cereal yields, the grain nitrogen (N) content, nitrate leaching and water productivity for a historical baseline (1981–2010) and mid-century conditions (2041–2070) projected by five general circulation models for different emission scenarios. Simulated management options included improved cultivars with later maturing characteristics, improved heat/drought resistance and nitrogen-use efficiency, increased N fertilisation levels, improved crop rotations together with improved soil, as well as supplementary and full irrigation. RESULTS AND CONCLUSIONSThe simulation results indicated that although a warming climate in conjunction with elevated atmospheric CO2 concentrations generally increase yield levels, N uptake and water productivity, risks associated with higher N leaching due to increased precipitation are a challenge for the sustainability of crop production. Overall, different SI options affected the sustainability indicators studied more than future projected climate, strongly suggesting that there is a large potential for sustainably intensifying crop production in northern conditions, particularly when applying more than one intensification measure at a time. Among a wide set of SI options tested for their sustainability impacts, improved crop cultivars showed the firmest positive impacts. This was supported by the views of agricultural stakeholders in the region. SIGNIFICANCEWhile the agricultural stakeholder's suggestions for alternative SI options challenged the simulation approach to some extent, the simulations provided robust information for comparing the sustainability impacts of alternative measures.

Evaluation of vegetable farming system

At the upstream of Jeneberang watershed, horticultural cultivation is a primadonna that is very much in demand by the farmer community, although the cropping system practiced by farmers is still far from the rules of soil and water conservation. The research aims to obtain information about the cropping system models and soil and water conservation techniques implemented by farmers on the upstream of Jeneberang WATERSHED. The results showed that the vegetable cropping system, practiced by farmers, was to grow vegetables in line with the slopes and the application of conservation techniques was not carried out by reason: 1) The high groundwater content that resulted in Plant tuber to be quickly rotten/damaged, 2) high moisture content on the soil will result in pests will develop rapidly.

Simulated Dryland Cotton Yield Response to Selected Scenario Factors Associated With Soil Health

In the Texas High Plains (THP), diminishing irrigation well-capacities, and increasing costs of energy and equipment associated with groundwater extraction and application are contributing factors to a transition from irrigated to dryland agriculture. The primary goal of this modeling exercise was to investigate whether and to what extent hypothetical changes in factors putatively associated with soil health would affect dryland cotton (Gossypium hirsutum L.) yields. The factors selected were drainage, surface runoff, soil water holding capacity, soil organic carbon (SOC) and albedo. As a first analysis to evaluate these factors, we used the CROPGRO-Cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) cropping system model. Specifically, we evaluated the effects of reduced surface runoff, increased soil water holding capacity, and SOC, doubling of the soil albedo through stubble mulching, and of soil drainage by enhancing infiltration with no-tillage/cover crops on yield by adjusting related soil properties. In our analysis, we used mean yields simulated with soil properties of a Pullman clay loam soil at Halfway, TX on the THP as baseline, which were compared to values obtained with the adjusted factors using weather data from 2005 to 2019. Simulated mean yield increased by 27% when the soil water holding capacity was increased by 25 mm, 7% when the runoff curve number was decreased from 73 to 60, 16% when soil albedo was increased from 0.2 to 0.4, and by 58% when the soil drainage factor (fraction day−1) was doubled from 0.2. No significant statistical change in simulated mean yield was calculated when SOC was increased by 1%. Further, effects of a 50 mm pre-plant irrigation were also assessed, simulating limited irrigation in the transition to dryland agriculture that resulted in a statistically insignificant 12% increase in seed-cotton yield. Simultaneous implementation of the four statistically significant individual scenarios (increased water holding capacity, infiltration, albedo, and drainage) resulted in the highest increase (93%) in mean seed-cotton yield. An economic and risk analysis of simulated yields under different scenarios indicated that these factors could reduce revenue risk for dryland cotton producers, with most of the effect from soil drainage improvements.

Coconut based cropping system model with spices and tuber crops - A novel approach for higher economic return

Coconut based cropping system model with spices and tuber crops - A novel approach for higher economic return

Climate change impacts and adaptation strategies on rainfed and irrigated maize in the agro-pastoral ecotone of Northwestern China

The agro-pastoral ecotone of Northwestern China (APENC) is one of the major agricultural production areas in China and a region where climate change is evident. Maize is a widely cultivated crop in the APENC, but the potential impact of climate change on maize, and potential adaptation strategies in response to this, are poorly understood. In this study, we used the Cropping System Model (CSM)-CERES-Maize to evaluate the impacts of climate change on maize yield, as well as the feasibility of 2 adaptation strategies; namely, adjusting the planting date and supplying irrigation. CSM-CERES-Maize was driven by an ensemble of 20 global climate models under 2 Representative Concentration Pathways (RCPs: RCP4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). CSM-CERES-Maize performed well in simulating phenology, leaf area index (LAI), maize yield, and soil water dynamics. The results showed that irrigated maize yield would change by +3.9, -16.3, and -20.4% under the RCP4.5 scenario and +0.1, -31.2, and -53.1% under the RCP8.5 scenario in the 2030s, 2060s, and 2090s, respectively. Rainfed maize yield during the 2030s, 2060s, and 2090s would change by +21.7, +16.4, and +12.6% under the RCP4.5 scenario and +25.1, +4.8, and -12.3% under the RCP8.5 scenario, respectively. Evaluation of adaptation strategies suggests that delaying planting dates and supplying irrigation at the tasseling and grain filling stages are the best strategies to increase maize yield under climate change. These results will provide comprehensive information for local policymakers to combat the adverse impacts of climate change.