Decision Support System For Agrotechnology Transfer Research Articles

Assessment of Different Frameworks for Addressing Climate Change Impact on Crop Production and Water Requirement

Various methodologies are used to estimate the impact of changing climatic factors, such as precipitation, temperature, and solar radiation, on crop production and water demand. In this study, the changes in rice yield, water demand, and crop phenology were estimated with varying CO2 concentration and an ensemble of general circulation models (GCMs), using a decision support system for agrotechnology transfer (DSSAT), a crop growth model. The measured CO2 concentration of 400 ppm from the Keeling curve, was used as the default CO2 concentration to estimate yield, water demand, and phenology. These outputs, obtained with the default concentration, were compared with the results from climate change scenarios’ concentrations. Further, the outputs corresponding to the ensembled GCMs’ climate data were obtained, and the results were compared with the ensembled crop model outputs simulated with each GCM. The yield was found to increase with the increase in CO2 concentration up to a certain threshold, whereas water demand and phenology were observed to decrease with the increase in CO2 concentration. The two approaches of the ensemble technique to obtain final outputs from DSSAT results did not show a large difference in the predictions.

Improving prediction accuracy of CSM-CERES-Wheat model for water and nitrogen response using a modified Penman-Monteith equation in a semi-arid region

Context or problemThe implementation of crop models for water and nitrogen management are important in preserving water resources and reducing groundwater pollution. The Decision Support System for Agrotechnology Transfer (DSSAT) is a collection of field-scale process-based crop models that simulate crop growth and yield in response to water and nitrogen management as well as many other factors. Objective or research questionThe objective of this study was to improve the simulation of irrigation and nitrogen fertilizer effects on growth and yield of winter wheat using locally modified Penman-Monteith (PM) equation. Specifically, we focused on improving the estimation of ETo using a locally modified Penman-Monteith (PM) equation in the CSM-CERES-Wheat model. MethodsField measured data from a two-year experiment (2009–2010 and 2010–2011) in a semi-arid region were used to calibrate and evaluate the CSM-CERES-Wheat model in a region with strong advective condition. In this experiment, the irrigation treatments were 1.2, 1.0, 0.8, 0.5 times by full irrigation requirements and rain-fed, and nitrogen treatments were 0, 46, 92, and 136 kg N ha−1. ResultsUsing the CSM-CERES-Wheat model with the unmodified PM equation resulted in some growth variables such as: aboveground dry matter, grain yield, and grain nitrogen uptake with high normalized root mean square error (NRMSE) of 0.51, 0.35, and 0.32, respectively. However, using the modified PM equation for ETo module under semi-arid condition improved the soil water content estimation and the model outputs, such as the above ground dry matter, grain yield and grain nitrogen uptake with NRMSE of 0.2, 0.15 and 0.16, respectively, that are accurate. This modification provided more accurate estimations, particularly in our experimental site in semi-aid region with strong advective conditions. ConclusionsThe findings of this study indicated that the locally modified PM equation in the CSM-CERES-Wheat model enhanced the accuracy of estimating irrigation and nitrogen fertilizer on the growth and yield of winter wheat. By improving the estimation of ETo value, the model's performance in simulating crop responses to water and nitrogen management was significantly enhanced. Implications or significanceThe utilization of the accurately estimated ETo value of the modified PM equation has significant importance in the practical application of the CSM-CERES-Wheat model. Furthermore, this modification greatly enhances the accuracy of model outputs, particularly at our experimental site located in a semi-arid region with robust advective conditions.

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran.

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0

Abstract. Elevated surface ozone (O3) concentrations can negatively impact growth and development of crop production by reducing photosynthesis and accelerating leaf senescence. Under unabated climate change, future global O3 concentrations are expected to increase in many regions, adding additional challenges to global agricultural production. Presently, few global process-based crop models consider the effects of O3 stress on crop growth. Here, we incorporated the effects of O3 stress on photosynthesis and leaf senescence into the Decision Support System for Agrotechnology Transfer (DSSAT) crop models for maize, rice, soybean, and wheat. The advanced models reproduced the reported yield declines from observed O3-dose field experiments and O3 exposure responses reported in the literature (O3 relative yield loss RMSE <10 % across all calibrated models). Simulated crop yields decreased as daily O3 concentrations increased above 25 ppb, with average yield losses of 0.16 % to 0.82 % (maize), 0.05 % to 0.63 % (rice), 0.36 % to 0.96 % (soybean), and 0.26 % to 1.23 % (wheat) per ppb O3 increase, depending on the cultivar O3 sensitivity. Increased water deficit stress and elevated CO2 lessen the negative impact of elevated O3 on crop yield, but potential yield gains from CO2 concentration increases may be counteracted by higher O3 concentrations in the future, a potentially important constraint to global change projections for the latest process-based crop models. The improved DSSAT models with O3 representation simulate the effects of O3 stress on crop growth and yield in interaction with other growth factors and can be run in the parallel DSSAT global gridded modeling framework for future studies on O3 impacts under climate change and air pollution scenarios across agroecosystems globally.

Modeling performances of maize cultivars under current and future climate scenarios in southern central Ethiopian rift valley

BackgroundIn southern central rift valley of Ethiopia, maize is an important crop because of its adaptation to wider agro-ecologies and higher yield potential. However, most cultivars were not parameterized to include in the database of Decision Support System for Agro-technology Transfer (DSSAT). As a result simulation of growth and yield of those cultivars was not possible under changing climate.MethodsTwo set of independent crop, management and soil data were used for calibration and validation of genetic coefficients of maize cultivars (BH-540, BH-546, BH-547, Shala and Shone) under condition of historic weather (1990–2020). Later, we simulated the growth and yield of maize using twenty multimodel climate ensembles across RCP 4.5 and 8.5 during early, medium and late century across Shamana, Bilate, Hawassa and Dilla clusters using DSSATv4.8 model.ResultsCultivars BH-540, BH-546, BH-547, Shala and Shone produced yields of 5.7, 5.4, 5.2, 6.9 and 7.4 t ha−1 with the corresponding error percentage of − 0.1, − 0.8, − 1.0, − 6.1 and 2.6%. The results of normalized root mean square were 1.14–4.2 and 3.0–3.9%, for grain yield during calibration and validation, respectively showing an excellent rating. The simulation experiment produced 5.4–9.2 t ha−1 for grain yield of maize cultivars across the study areas, which is likely to fall close to 63.3% by 2070 if right adaptation options are not introduced necessitating switch in cultivars and production areas.ConclusionsThere is critical need for reduction of GHGs emissions, generation of innovative adaptation strategies, and development of drought and heat stress tolerant maize cultivars. Hence, researchers and policy makers shall act with utmost urgency to embark with breeding programs that target climate change adaptation traits in maize crop.

Assessing Climate Change Effects on Winter Wheat Production in the 3H Plain: Insights from Bias-Corrected CMIP6 Projections

Climate change exerts significant impacts on regional agricultural production. This study assesses the implications of climate change on winter wheat yields in the Huang-Huai-Hai Plain (3H Plain), utilizing bias-corrected climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for mid-21st century (2041–2060) and late 21st century (2081–2100) periods under two shared socioeconomic pathways (SSP2–4.5 and SSP5–8.5). These projections were incorporated into the decision support system for agrotechnology transfer (DSSAT) CERES-Wheat model to forecast potential alterations in winter wheat production. Initial findings reveal that uncorrected CMIP6 projections underestimated temperature and precipitation while overestimating solar radiation across the southern 3H Plain. Following bias correction through the equidistant cumulative distribution function (EDCDF) method, the regional average biases for temperature, precipitation, and solar radiation were reduced by 18.3%, 5.6%, and 30.7%, respectively. Under the SSP2–4.5 and SSP5–8.5 scenarios, mid-21st century simulations predicted a 13% increase in winter wheat yields. Late 21st century projections indicated yield increases of 11.3% and 3.6% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, with a notable 8.4% decrease in yields south of 36° N under the SSP5-8.5 scenario. The analysis of climate change factors and winter wheat yields in the 3H Plain under both scenarios identified precipitation as the key contributing factor to yield increases in the northern 3H Plain, while temperature limitations were the primary constraint on yields in the southern region. Consequently, adaptive strategies are essential to mitigate climate change impacts, with a particular focus on addressing the challenges posed by elevated temperature in the southern 3H Plain.

Optimizing Crop Yield Estimation through Geospatial Technology: A Comparative Analysis of a Semi-Physical Model, Crop Simulation, and Machine Learning Algorithms

This study underscores the critical importance of accurate crop yield information for national food security and export considerations, with a specific focus on wheat yield estimation at the Gram Panchayat (GP) level in Bareilly district, Uttar Pradesh, using technologies such as machine learning algorithms (ML), the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and semi-physical models (SPMs). The research integrates Sentinel-2 time-series data and ground data to generate comprehensive crop type maps. These maps offer insights into spatial variations in crop extent, growth stages and the leaf area index (LAI), serving as essential components for precise yield assessment. The classification of crops employed spectral matching techniques (SMTs) on Sentinel-2 time-series data, complemented by field surveys and ground data on crop management. The strategic identification of crop-cutting experiment (CCE) locations, based on a combination of crop type maps, soil data and weather parameters, further enhanced the precision of the study. A systematic comparison of three major crop yield estimation models revealed distinctive gaps in each approach. Machine learning models exhibit effectiveness in homogenous areas with similar cultivars, while the accuracy of a semi-physical model depends upon the resolution of the utilized data. The DSSAT model is effective in predicting yields at specific locations but faces difficulties when trying to extend these predictions to cover a larger study area. This research provides valuable insights for policymakers by providing near-real-time, high-resolution crop yield estimates at the local level, facilitating informed decision making in attaining food security.

A case study on the impacts of future climate change on soybean yield and countermeasures in Fujin city of Heilongjiang province, China

Global climate change poses a great impact on crop growth, development and yield. Soybean production in Northeast China, which is one of the traditional dominant soybean production areas in China, is of great significance for developing the domestic soybean industry and reducing dependence on imported soybeans. Therefore, it is crucial to evaluate the impacts of future climate change on soybean yield in Northeast China, and to propose reasonable adaptation measures. In this study, we took Fujin city of Heilongjiang province in Northeast China as an example, and used the CROPGRO-soybean model in DSSAT (Decision Support System for Agrotechnology Transfer) to simulate the impacts of future climate change on soybean yield in the four periods of the 2020s (2021-2030), 2030s (2031-2040), 2040s (2041-2050) and 2050s (2051-2060) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), and further determine the best agronomic management practices. The results showed that the calibrated and validated model is suitable for simulating soybean in the study area. By analyzing the meteorological data under future climate scenarios RCP4.5 and RCP8.5 from the PRECIS regional climate model, we found that the average temperature, cumulative precipitation and cumulative solar radiation would mostly increase during the growing season in Fujin city of Heilongjiang province. Combined with the model simulation results, it is shown that under the effect of CO2 fertilization, future climate change will have a positive impact on soybean yield. Compared to the baseline (1986-2005), the soybean yield would increase by 0.6% (7.4%), 3.3% (5.1%), 6.0% (16.8%) and 12.3% (20.6%) in the 2020s, 2030s, 2040s and 2050s under RCP4.5 (RCP8.5).Moreover, the optimal sowing dates and the optimal supplemental irrigation amount under RCP4.5 (RCP8.5) are May 10 (May 5) and 50 mm (40mm), respectively. Under future climate conditions, the agronomic management practices, such as advancing the sowing date and supplementary irrigation in the key stage of soybean growth would increase soybean yield and make soybean growth more adaptable to future climate change.

A new adaptive identification strategy of best crop management with farmers

Identification of best performing fertilizer practices with on-farm trials is challenging, in particular in rainfed farming due to weather uncertainty. However, it remains crucial to test a range of viable practices to ascertain their performances, given that they are not known beforehand. This process also involves the testing of practices that could potentially yield inferior results in comparison to the best available practice(s). To identify a best management practice, an “intuitive strategy” typically sets up multi-year, multi-location field trials, wherein each practice is tested in a proportionally equal manner over a set number of years. Our objective was to provide an identification strategy for nitrogen fertilizer management designing a bandit learning algorithm. We aimed for the bandit algorithm to be better at minimizing farmers’ losses occurring from the testing of management practices that do not perform best, compared with the “intuitive strategy” that was formulized as the Explore-Then-Commit strategy. Our case study was for maize production in southern Mali. Bandit framework is a machine learning approach in which an agent learns from the feedback over time and accordingly selects actions in order to maximize its cumulative reward in the long term. To mimic the maize responses to nitrogen fertilization, we used the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. We compared nitrogen fertilizer practices using a risk-aware measure, the Conditional Value-at-Risk (CVaR), and a novel agronomic metric, the Yield Excess (YE). The YE accounts for both grain yield and agronomic nitrogen use efficiency. The bandit algorithm performed better than the intuitive strategy: it minimized farmers’ yield losses during the identification process. This study is a methodological step which opens up new horizons for risk-aware identification of the performance of a range of crop management practices in real conditions.

Integrating machine learning and empirical evapotranspiration modeling with DSSAT: Implications for agricultural water management

The availability of accurate reference evapotranspiration (ETo) data is crucial for developing decision support systems for optimal water resource management. This study aimed to evaluate the accuracy of three empirical models (Hargreaves-Samani (HS), Priestly-Taylor (PT), and Turc (TU)) and three machine learning models (Multiple linear regression (LR), Random Forest (RF), and Artificial Neural Network (NN)) in estimating daily ETo compared to the Penman-Monteith FAO-56 (PM) model. Long-term data from 42 weather stations in Florida were used. Moreover, the effect of ETo model selection on sweet corn irrigation water use was investigated by integrating simulated ETo data from empirical and ML models using the Decision Support System for Agrotechnology Transfer (DSSAT) model at two locations (Citra and Homestead) in Florida. Furthermore, a linear bias correction calibration technique was employed to improve the performance of empirical models. Results were consistent in that the NN and RF models outperformed the empirical models. The empirical models tended to underestimate and overestimate small and high daily ETo values, respectively, with the HS model exhibiting the least accuracy. However, calibrated PT and TU models performed comparably to the ML models. Results also revealed that using an inappropriate ETo model could lead to over-irrigation by up to 54 mm during a single crop season. Overall, ML models have proven reliable alternatives to the PM model, especially in regions with access to long-term data due to their site-independent performance. In areas without long-term data for ML model training and testing, calibrating empirical models is viable, but site-specific calibration is needed. It is important to highlight that distinct plant species exhibit varying transpiration characteristics and, consequently, have different water requirements. These differences play a pivotal role in shaping the overall impact of ETo models on crop water use.

Evaluation of a regional crop model implementation for sub-national yield assessments in Kenya

CONTEXTCropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVEThis study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODSThe RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONSThe median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ∼1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCEOne of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

Impacts of Multi-Strategy Nitrogen Fertilizer Management on Potato Yield and Economic Gains in Northeast China

Differentiated nitrogen (N) application styles and dosages can directly affect crop yield and planting costs. To maximize the efficiency of N fertilizer utilization and solve the problem of increasing production without increasing income, the Decision Support Systems for Agrotechnology Transfer (DSSAT) model was used in this study to carry out experiments simulating different N application styles and dosages for potatoes at 15 meteorological observation stations in the northeast region of China during 2013–2017. The present study aims to analyze the relationship among yield, cost, and net gains under different N fertilizer management scenarios by combining the cost-benefit method. The main conclusions are as follows: (1) the staged and incremental application of N fertilizer during the potato growth period can promote a yield increase. The lowest yield was achieved by a one-time basal application of N fertilizer at sowing, and the highest yield was observed when N fertilizer was applied in a 1:1 ratio at sowing and the end of tuber formation, respectively. (2) The range for N fertilizer application to achieve the highest potato yield at each station is 275–330 kg·ha−1. (3) The highest potato yield under sufficient N application treatments did not correspond to the highest net gains. Potato net gains at all stations showed an increasing-then-decreasing trend with the increase in N application. The highest gains were obtained when 220 kg·ha−1, 220 kg·ha−1, and 275 kg·ha−1 of N fertilizer were applied in a 1:1 ratio as a basal fertilizer and supplementary fertilizer at sowing, and the end of tuber formation in Heilongjiang, Jilin, and Liaoning provinces, respectively. This study provides a reference program for potato planting with the highest benefits for regional governments, farmers, and enterprises and aims to improve the status quo of potato planting, increasing production without increasing income.

Soil and Its Interaction with the Climate Jointly Drive the Change in Basic Soil Productivity under Long-Term Fertilizer Management

Basic soil productivity (BSP) is the productive capacity of farmland soils with their own physical and chemical properties during a specific crop season under local field management. Improving BSP as an effective way to increase or maintain crop yield, and researching its changes and potential driving factors under long-term fertilization are crucial for ensuring high and stable crop yields. In this study, the yields of BSP were simulated using the decision support system for agrotechnology transfer (DSSAT) crop model based on three long-term experiments, and its changing characteristics and driving factors were investigated under various fertilization treatments in wheat–maize rotation systems during 1991–2019. Five treatments were included: (1) unfertilized control (CK); (2) balanced mineral fertilization (NPK); (3) NPK plus manure (NPKM); (4) high dose of NPK plus manure (1.5NPKM); and (5) NPK plus crop straw (NPKS). This study found that the BSP of wheat and maize exhibited a fluctuating increase or stable change trend under four fertilization treatments at the Yangling (YL) and Zhengzhou (ZZ) sites, while a fluctuating reduction trend was observed at the Qiyang (QY) site. Compared with CK, NPKM, 1.5NPKM and NPKS significantly (p < 0.05) improved the BSP of wheat and maize at the YL and ZZ sites. The BSP of both maize and wheat under NPKM and 1.5NPKM was significantly (p < 0.05) higher than that under NPK or NPKS, whereas no statistically significant difference was found between NPK and NPKS at the QY site. The contribution rates of basic soil productivity (CBSP) of maize and wheat at the YL (41.5–60.7% and 53.0–64.3%) and ZZ sites (44.4–59.2% and 56.8–66.7%) were overall higher than that at the QY site (25.8–37.6% and 48.3–60.1%). In most cases, the difference in CBSP among different fertilization treatments was consistent with that in BSP. Moreover, a significant positive correlation was recorded between BSP and soil pH (r = 0.73, p < 0.01; r = 0.87, p < 0.01), TN (r = 0.56, p < 0.01; r = 0.62, p < 0.01) and TK content (r = 0.49, p < 0.01; r = 0.58, p < 0.01) in maize and wheat. Soil pH significantly correlated positively with the BSP of maize (R2 = 0.54, p < 0.001) and wheat (R2 = 0.49, p < 0.001) at the QY site, but negative correlation (R2 = 0.20, p < 0.001; R2 = 0.30, p < 0.001) was only found in maize at the YL and ZZ sites. The BSP of maize and wheat showed a significant negative linear correlation with MAP (R2 = 0.49–0.67, p < 0.001) and MAT (R2 = 0.36–0.62, p < 0.001). Random forests (RF) and variance partitioning analysis (VPA) revealed that soil properties and its interaction with the climate showed a higher explanation rate for BSP, indicating that these factors are the key drivers of BSP change. Overall, chemical fertilizers combined with manure can effectively increase BSP, while the effects of fertilizer combined with straw on BSP vary by region. The changes in BSP in wheat–maize cropping were mainly driven by both the soil and its interactions with the climate.

High spatial resolution seasonal crop yield forecasting for heterogeneous maize environments in Oromia, Ethiopia

Seasonal climate variability determines crop productivity in Ethiopia, where rainfed smallholder farming systems dominate in the agriculture production. Under such conditions, a functional and granular spatial yield forecasting system could provide risk management options for farmers and agricultural and policy experts, leading to greater economic and social benefits under highly variable environmental conditions. Yet, there are currently only a few forecasting systems to support early decision making for smallholder agriculture in developing countries such as Ethiopia. To address this challenge, a study was conducted to evaluate a seasonal crop yield forecast methodology implemented in the CCAFS Regional Agricultural Forecasting Toolbox (CRAFT). CRAFT is a software platform that can run pre-installed crop models and use the Climate Predictability Tool (CPT) to produce probabilistic crop yield forecasts with various lead times. Here we present data inputs, model calibration, evaluation, and yield forecast results, as well as limitations and assumptions made during forecasting maize yield. Simulations were conducted on a 0.083° or ∼ 10 km resolution grid using spatially variable soil, weather, maize hybrids, and crop management data as inputs for the Cropping System Model (CSM) of the Decision Support System for Agrotechnology Transfer (DSSAT). CRAFT combines gridded crop simulations and a multivariate statistical model to integrate the seasonal climate forecast for the crop yield forecasting. A statistical model was trained using 29 years (1991–2019) data on the Nino-3.4 Sea surface temperature anomalies (SSTA) as gridded predictors field and simulated maize yields as the predictand. After model calibration the regional aggregated hindcast simulation from 2015 to 2019 performed well (RMSE = 164 kg/ha). The yield forecasts in both the absolute and relative to the normal yield values were conducted for the 2020 season using different predictor fields and lead times from a grid cell to the national level. Yield forecast uncertainties were presented in terms of cumulative probability distributions. With reliable data and rigorous calibration, the study successfully demonstrated CRAFT’s ability and applicability in forecasting maize yield for smallholder farming systems. Future studies should re-evaluate and address the importance of the size of agricultural areas while comparing aggregated simulated yields with yield data collected from a fraction of the target area.

Generic optimization approach of soil hydraulic parameters for site-specific model applications

Site-specific crop management is based on the postulate of varying soil and crop requirements in a field. Therefore, a field is separated into homogenous management zones, using available data to adapt management practices environment to maximize productivity and profitability while reducing environmental impacts. Due to advancing sensor technologies, crop growth and yield data on more minor scales are common, but soil data often needs to be more appropriate. Crop growth models have shown promise as a decision support tool for site-specific farming. The Decision Support System for Agrotechnology Transfer (DSSAT) is a widely used point-based model. To overcome the problem of inappropriate soil input data problem, this study introduces an external plug-in program called Soil Profile Optimizer (SPO), which uses the current DSSAT v4.8 to calibrate soil profile parameters on a site-specific level. Developed as an inverse modelling approach, the SPO can calibrate selected soil profile parameters by targeting available in-season plant data. Root Mean Square Error (RMSE) and normalized RMSE as error minimization criteria are used. The SPO was tested and evaluated by comparing different simulation scenarios in a case study of a 3-yr field trial with maize. The scenario with optimized soil profiles, conducted with the SPO, resulted in an R2 of 0.76 between simulated and observed yield and led to significant improvements compared to the scenario conducted with field scale soil profile information (R2 0.03). The SPO showed promise in using spatial plant measurements to estimate management zone scale soil parameters required for the DSSAT model.

Climate change impacts assessment and developing adaptation strategies for rainfed foxtail millet in northern Shanxi, China

Climate change is considered to be a serious threat to crop production around the globe. In the northern Shanxi Province of China (SXN), foxtail millet (or millet) is the main crop and generally grows under rainfed conditions. Existing literature has confirmed the millet yield reductions due to climate change in the past. However, little is known about the impacts of future climate change on millet production in the SXN. The main objectives of this study were to investigate the future climate change impacts under two emission scenarios (SSP245 and SSP585) and the potentials of shifting sowing date combined with supplemental irrigation as an adaptation strategy to climate change for millet production in the SXN using the Decision Support System for Agrotechnology Transfer (DSSAT). Two supplemental irrigation strategies were tested: one presowing irrigation only (PSI), and two supplementary irrigations (PSGI) adopting one presowing irrigation and one irrigation during the growing season. We found that the air temperature, solar radiation and precipitation during the millet growing season in the future (2021–2060) are higher than those in the baseline (1981–2020). The rainfed millet yield will decrease by 8.6 % (SSP245) and 16.5 % (SSP585) in the future without an adaptation strategy, compared to the baseline yield (2.43 t ha−1) in 1981–2020 under the conventional sowing date. The reduction in rainfed millet yield caused by climate change cannot be reversed by shifting sowing date, but can be slightly mitigated by a suitable advancement of the sowing date. Adopting supplemental irrigation in the future can achieve higher millet yield than the baseline yield, especially PSGI. Compared to PSI, PSGI consumes more irrigation water but has higher irrigation water use efficiency (IWUE). There is a synergy between shifting sowing date and supplemental irrigation in coping with climate change, and a later sowing date than the conventional one (30 April) is recommended under supplemental irrigation in the future. In 2021–2060, the best adaptation strategy to climate change for maximizing grain yield, IWUE and economic benefit in the millet production of the SXN is that supplementary irrigation is applied before sowing and on 20 July–30 July combined with the sowing dates of 10 May–20 May.

Field-level rice yield estimations under different farm practices using the crop simulation model for better yield

Crop yield estimation is essential for decision-making systems and insurance policy makers. Numerous methodologies for yield estimation have been developed, encompassing crop models, remote sensing techniques, and empirical equations. Each approach holds unique limitations and advantages. The primary aim of this study was to assess the accuracy of the DSSAT (Decision Support System for Agro Technology Transfer) model in predicting rice yields and LAI (Leaf Area Index) across various management methods. Additionally, the study sought to identify the optimal management practice for attaining higher yields. Crop models facilitate the expeditious evaluation of management strategies aimed at improving crop yield and analyzing the balance between production, resource efficiency, and environmental impacts. The study region selected for analysis is Karimnagar district of Telangana state. DSSAT has been chosen as the preferred tool due to its high efficiency in evaluating crop yield. The model's simulated yield was compared to the observed yield obtained from crop-cutting experiments. The results indicate a correlation of 0.81 and 0.85 between observed and simulated yields, as well as between model LAI and yield. An observation was made regarding a discrepancy between predicted and actual yields, which can be attributed to biotic stress. However, it should be noted that the current model does not account for this factor. The observed average yield was 5200 kg ha-1, whereas the projected yield was 5400 kg ha-1. The findings indicate that the model's performance is influenced by both the timing of sowing and the amount of nitrogen applied. The findings indicate that the DSSAT model has demonstrated a high level of accuracy in predicting both yields and leaf area index (LAI) across various management strategies. This study showcases the potential use of crop simulation models as a technology-driven tool to identify the most effective management strategies for rice production.

Climate Change Impacts on Rainfed Maize Yields in Kansas: Statistical vs. Process-Based Models

The changing climate and the projected increase in the variability and frequency of extreme events make accurate predictions of crop yield critically important for addressing emerging challenges to food security. Accurate and timely crop yield predictions offer invaluable insights to agronomists, producers, and decision-makers. Even without considering climate change, several factors including the environment, management, genetics, and their complex interactions make such predictions formidably challenging. This study introduced a statistical-based multiple linear regression (MLR) model for the forecasting of rainfed maize yields in Kansas. The model’s performance is assessed by comparing its predictions with those generated using the Decision Support System for Agrotechnology Transfer (DSSAT), a process-based model. This evaluated the impact of synthetic climate change scenarios of 1 and 2 °C temperature rises on maize yield predictions. For analysis, 40 years of historic weather, soil, and crop management data were collected and converted to model-compatible formats to simulate and compare maize yield using both models. The MLR model’s predicted yields (r = 0.93) had a stronger association with observed yields than the DSSAT’s simulated yields (r = 0.70). A climate change impact analysis showed that the DSSAT predicted an 8.7% reduction in rainfed maize yield for a 1 °C temperature rise and an 18.3% reduction for a 2 °C rise. The MLR model predicted a nearly 6% reduction in both scenarios. Due to the extreme heat effect, the predicted impacts under uniform climate change scenarios were considerably more severe for the process-based model than for the statistical-based model.

Coupling the CSM-CROPGRO-Soybean crop model with the ECOSMOS Ecosystem Model – An evaluation with data from an AmeriFlux site

Process-based simulation models, such as land surface (LSM) and crop models, are useful tools for studying the impacts of the environment, management and genotype on agricultural production. LSM are capable of simulating crop development and growth, but not in as much detail as the processes embedded in crop models. Crop models, on the other hand, do not usually have the ability to solve the surface water, energy and carbon balances, which can help to assess the feedbacks between climate and agricultural systems. The goals of this study were first to implement the well-known Cropping System Model (CSM)-CROPGRO-Soybean model of the Decision Support System for Agrotechnology Transfer (DSSAT) into the Ecosystem Model Simulator (ECOSMOS) LSM and then to evaluate this coupling to simulate surface fluxes and crop performance of irrigated and rainfed soybean agroecosystems with comprehensive data from an AmeriFlux site. After model coupling, simulations were benchmarked against multiple flux (carbon dioxide, energy, and water), phenology, growth, and yield observations obtained from field scale experiments across 14 seasons from irrigated and rainfed fields near Mead, Nebraska. Calibration of the physiological parameters of ECOSMOS and of the CROPGRO-Soybean genetic coefficients was conducted. Common metrics were employed to assess model performance. Overall, the coupled model reproduced both the magnitude and seasonal patterns of the energy balance, carbon dioxide exchange, evapotranspiration and soil water balance. The crop model coupling into ECOSMOS preserves the known high skill of the CROPGRO-Soybean model in simulating soybean phenology and growth dynamics. The simulated yields were consistent with the observations at field level. Although the ECOSMOS-CROPGRO-Soybean model is sufficiently robust to simulate surface fluxes and crop performance of soybean agroecosystems at field scale for the environments of eastern Nebraska, further evaluation for a wide range of climatic and soil conditions and management practices is warranted.

Exploring deficit irrigation as a water conservation strategy: Insights from field experiments and model simulation

Deficit irrigation has emerged as a viable approach to increase agricultural water productivity. The objectives of this study were to 1) develop water conservation strategies by investigating responses of green bean (Phaseolus vulgaris) and sweet corn (Zea mays var. saccharata) to four irrigation treatments and 2) evaluate the performance of the Decision Support System for Agrotechnology Transfer (DSSAT) model in simulating growth and yield responses of green bean and sweet corn under four irrigation treatments. A field experiment was conducted using 32 experimental plots, established under a linear move sprinkler irrigation system equipped with variable rate irrigation (VRI) technology, at the Tropical Research and Education Center (TREC), Homestead for two cropping seasons between November and March in 2020–2021 and 2021–2022. The experiment involved four irrigation treatments: full irrigation (FI) (T4), and three levels of deficit irrigation, 75% FI (T3), 50% FI (T2), and 25% FI (T1) with four replications arranged in a completely randomized block design (RCBD) for each crop. The average soil moisture content of the full irrigation treatments was maintained above the management allowable depletion (MAD). Field data collection included various parameters of the crops including plant phenology, plant height, canopy width, fresh and dry biomass, and yield. Results confirmed that all three deficit irrigation treatments did not significantly affect the yield and yield components of the two crops during both seasons compared to the T4. This finding revealed that the highest crop water productivity was achieved from T1, 38.3 and 41.4 kg m−3 for green bean and 54 and 53 kg m−3 for sweet corn during the 2020–2021 and 2021–2022 seasons and up to 75% irrigation water saving could be achieved without affecting plant growth and yield. The DSSAT model performed well in simulating yield and yield components for the two crops in response to the four irrigation treatments. Model evaluation results showed that the minimum mean absolute error (MAE) for green bean fresh pod yield simulation was 2.1 Mg ha−1 with an index of agreement (d-Stat) of 0.6, while for biomass, it was 0.24 Mg ha−1 with a d-Stat of 1. Similarly, for sweet corn, the model simulated fresh cob yield with a minimum MAE of 6.51 Mg ha−1 and d-Stat of 0.9 and biomass with MAE of 1.63 Mg ha−1 and d-Stat of 0.9. Overall, implementing deficit irrigation in green bean and sweet corn fields could result in considerable water savings while achieving acceptable crop yield. The DSSAT model could be also a useful tool to simulate green bean and sweet corn responses to different irrigation scenarios and aid water management decisions under South Florida conditions.