Decision Support System For Agrotechnology Transfer Model Research Articles

Estimating soil mineral nitrogen from data-sparse field experiments using crop model-guided deep learning approach

Sandy soils are susceptible to excessive nitrogen (N) leaching under intensive crop production which is linked with the soil’s low nutrient holding capacity and high-water infiltration rate. Estimating soil mineral nitrogen (SMN) at the daily time-step is crucial in providing fertilizer recommendations balancing plant nitrogen use efficiency (NUE) and N losses to the environment. Crop models [e.g., Decision Support System for Agrotechnology Transfer (DSSAT)] can simulate SMN trend under varied fertilizer application rates and timings but struggle with accuracy in high-water table conditioned sub-irrigated soil. Alternatively, time-series deep learning (DL) models based on a long short-term memory (LSTM) are promising in understanding nonlinearity among complex variables. Yet, purely data-driven DL models for crops are challenging to develop due to limited field data availability and the excessive costs to collect more field data. Hence, a hybrid model (hybrid-LSTM) was developed by leveraging both the DSSAT and LSTM models to estimate daily SMN primarily using daily weather, applied fertilizer rates-timings, and the SMN sparse observations. This study used the observations from field trials conducted between 2011–2014 in Hastings, FL. The first step was to calibrate the DSSAT-SUBSTOR-Potato model to produce reliable SMN of the topsoil for treatments with varied N applied fertilizer rates split among the pre-planting, emergence, and tuber-initiation stages of the potato. Thereafter, the hybrid-LSTM model was trained on the calibrated DSSAT simulated SMN time-series and fine-tuned its predictions using the observed SMN to improve DSSAT simulated SMN. The hybrid-LSTM model was then tested on both calibrated and uncalibrated DSSAT SMN simulations where it outperformed the DSSAT model (improvement ranged ∼18–30 % on comparing the normalized root mean squared error) in providing reliable estimates of SMN across most of the farms and years. This novel hybrid modeling approach could guide stakeholders and farmers to build sustainable N management with improved crop NUE and yield and help in minimizing environmental losses.

Calibration and Evaluation of CERES-Maize and CROPGRO-Dry Bean Crop Simulation Models of the DSSAT in the Great Rift Valley Region of Ethiopia

Maize (Zea mays L.) is one of the most principal cereal crops ranking first in production in Ethiopia, predominantly produced and consumed directly by the smallholder farmers in the Great Rift Valley (GRV) of Ethiopia. Common bean (Phaseolus vulgaris) is also the most important legume crops as the source of protein and export commodity in the GRV. However, the average maize and common bean yields in Ethiopia are still low due to abiotic, biotic and socioeconomic constraints. In this regard, Crop simulation models (CSMs) are used in predicting growth and yield of crops and associated yield gaps under various management options and changing climatic parameters that are profitable with minimal unwanted impacts on the environment. Before using the CSMs, it is necessary to specify model parameters and understand the uncertainties associated with simulating variables that are needed for decision-making. Therefore, the research objective of this study was to calibrate and evaluate the performance of the CERES-Maize and CROPGRO-Dry bean CSMs of the Decision Support System for Agrotechnology Transfer (DSSAT) in the GRV of Ethiopia. The generalized likelihood uncertainty estimation (GLUE) method was used to estimate the genetic parameters of the CSM-CERES-Maize and CROPGRO-Dry bean models. Root mean squared error (RMSE) and Index of agreement (I) were used to evaluate the performance of the models. The DSSAT model reasonably reproduced observations for days to anthesis, days to physiological maturity, and grain yields, with values for the index of agreement of 0.97, 0.88 and 0.61 for CERES-Maize and 0.84, 0.75 and 0.51 for CROPGRO-Dry bean. Similarly, root mean square errors were moderate for days to anthesis (1.2 and 1.2 days), maturity (4.1 and 1.6 days), and yield (0.8 and 1.1 t/ha) for CERES-Maize and CROPGRO-Dry bean, respectively. The model has been successfully calibrated and evaluated for maize and common bean crop varieties and can now it can be taken for further applications in evaluating various crop and soil management options including climate smart agriculture technologies and climate change impact studies.

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.

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.

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.

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.

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.

Modeling climate change impact on dryland wheat production for increased crop yield in the Free State, South Africa, using GCM projections and the DSSAT model

Introduction: The impact of climate change on food production in South Africa is likely to increase due to low rainfall and frequent droughts, resulting in food insecurity in the future. The use of well-calibrated and validated crop models with climate change data is important for assessing climate change impacts and developing adaptation strategies. In this study, the decision support system for agrotechnology transfer (DSSAT) crop model was used to predict yield using observed and projected climate data.Materials and Methods: Climate, soil, and crop management data were collected from wheat-growing study sites in Bethlehem, South Africa. The DSSAT wheat model (CROPSIM-CERES) used was already calibrated, and validated by Serage et al. (Evaluating Climate Change Adaptation Strategies for Disaster Risk Management: Case Study for Bethlehem Wheat Farmers, South Africa, 2017) using three wheat cultivar coefficients obtained from the cultivar adaptation experiment by the ARC-Small Grain Institute. The model was run with historical climate data for the eastern Free State (Bethlehem) from 1999 to 2018 as the baseline period. To determine the effects of climate change, the crop model simulation for wheat was run with future projections from four Global Climate Models (GCM): BCC-CSM1_1, GFDL-ESM2G, ENSEMBLE, and MIROC from 2020 to 2077.Results: The average wheat yield for the historic climate data was 1145.2 kg/ha and was slightly lower than the highest average yield of 1215.9 kg/ha from GCM ENSEMBLE during Representative concentration pathways (RCP) 2.6, while the lowest yield of 29.8 kg/ha was produced during RCP 8.5 (GCM GFDL-ESM2G). Model GFDL-ESM2G produced low yields (29.8–47.74 kg/ha) during RCP 8.5 and RCP 6.0, respectively. The yield range for GCM BCC-CSM1_1 was 770.2 kg/ha during RCP 2.6 to 921.68 kg/ha during RCP 4.5 and 547.84 kg/ha during RCP 8.5 to 700.22 kg/ha during RCP 2.6 for GCM MIROC.Conclusion: This study showed a declining trend in yield for future climate projections from RCP2.6 to RCP8.5, indicating that the possible impacts of higher temperatures and reduced rainfall in the projected future climate will slightly decrease wheat production in the eastern Free State. Adaptation measures to mitigate the potential impact of climate change could include possible changes in planting dates and cultivars. Using a crop model to simulate the response of crops to variations in weather conditions can be useful to generate advisories for farmers to prevent low yield.

Meta analysis on the evaluation and application of DSSAT in South Asia and China: Recent studies and the way forward

The Decision Support System for Agrotechnology transfer (DSSAT) is a global modelling platform that encompasses crop models for more than 40 different crops. The models have been used extensively throughout the world, including South Asia and China. From the web of science database, we reviewed 205 papers that were published from January 2010 to February 2022 containing examples of the evaluation and application of the DSSAT crop simulation models. In South Asia and China, more than 50 traits and variables were analyzed for various experiments and environmental conditions during this period. The performance of the models was evaluated by comparing the simulated data with the observed data through different statistical parameters. Over the years and across different locations, the DSSAT crop models simulated phenology, growth, yield, and input efficiencies reasonably well with a high coefficient of determination (R2), and Willmott d-index, together with a low root mean square error (RMSE), normalized RMSE (RMSEn), mean error (ME) or percentage error difference. The CERES models for rice, wheat and maize were the most used models, followed by the CROPGRO models for cotton and soybean. Grain yield, anthesis and maturity dates, above ground biomass, and leaf area index were the variables that were evaluated most frequently for the different crop models. The meta-analysis of the data of the most common simulated variables (Anthesis, maturity, leaf area index, grain yield and above ground biomass) for the four commonly used DSSAT models (CERES-Rice, CERES-Wheat, CERES-Maize and CROPGRO-Cotton) showed that the models predicted anthesis with an RMSE of ~2 (CERES-Maize) and -4 days (CERES-Wheat), a normalized RMSE of ~2.5 (CERES-Maize) and -3.8% (CERES-Rice), and a R2 ~ 0.98-0.99. The maturity was predicted with an RMSE~ 3.0 (CERES-Maize)-6.1 days (CROPGRO-Cotton), normalized RMSE~2.3 (CERES-Wheat)-5.0% (CERES-Rice) and R2 ~ 0.90-0.99. The leaf area index was predicted with an RMSE~ 0.3-0.7, normalized RMSE~6 (CROPGRO-Cotton)-16% (CERES-Maize) and R2 ~ 0.75-0.98. The model performance for simulating grain yield was best with CROPGRO-cotton with a normalized RMSE of 4.4%, RMSE of 138.8 kg and R2 of 0.99. The lowest R2 and highest RMSEn was found for CERES-Wheat. Among all the variables that were evaluated, above ground biomass was least accurately simulated with a RMSEn as high as 18% and R2 as small as 0.50 by CERES-Wheat. The models were used for studying the crop response under various soil, weather, and management conditions. The review will be helpful to identify the research gap in the use of crop models for different crops in South Asia and China. It can also aid scientists to target their research for specific applications to address food and nutrition security based on sustainable management practices.

Impact of rainfall onset date on crops yield in Ghana

Rainfall onset date (ROD) influences farmer planting decisions, yet there is a dearth of information on the extent to which ROD influences crop yield. This study assesses the effect of ROD on the yield of four crops (Maize, millet, rice, and sorghum) in Ghana. It uses crop yields from the Ministry of Food and Agriculture (MoFA) and the Food and Agriculture Organization (FAO), and employs the Decision Support System for Agro-technology Transfer (DSSAT) crop model to simulate maize yields from 1985 to 2004. The crop model simulations were forced with weather data from the gridded Global Meteorological Forcing Dataset (GMFD). The relationship between crop yields and RODs from three datasets (observed, satellite, and GMFD) are studied. The results of the study show a good correlation between MoFA and FAO crop yield data (with correlation coefficient (r) of 0.97, 0.92, 0.77, and 0.99 for maize, millet, rice, and sorghum, respectively). RODs from satellite observation feature a high correlation with RODs from station observation (r = 0.72), but RODs from GMFD feature weak correlations (r < 0.3) with both observation datasets. The study finds a negative correlation between observed RODs and crop yields (i.e. an early onset corresponds to high yields) but a positive correlation between GMFD RODs and crop yields (i.e. an early onset correspondence to low yields). The DSSAT model reproduces the observed yield pattern, but with substantial biases. The findings of this study can be used to advise small-holder farmers on planting dates and crop variety selection.

Process-Based Crop Models in Soil Research: A Bibliometric Analysis

Different types of soil data are used in process-based crop models as input data. Crop models have a diverse range of applications, and soil research is one of them. This bibliographic analysis was conducted to assess the current literature on soil-related applications of crop models using two widely used crop models: Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT). The publications available in the Scopus database during the 2000–2021 period were assessed. Using 523 publications, a database on the application of process-based crop models in soil research was developed and published in an online repository, which is helpful in determining the specific application in different geographic locations. Soil-related applications on APSIM and DSSAT models were found in 41 and 43 countries, respectively. It was reported that selected crop models were used in soil water, physical properties, greenhouse gas emissions, N leaching, nutrient dynamics, and other physical and chemical properties related to applications. It can be concluded that a crop model is a promising tool for assessing a diverse range of soil-related processes in different geographic regions.

Modeling effects of biodegradable film mulching on evapotranspiration and crop yields in Inner Mongolia

The use of biodegradable film has been increasingly recommended as an alternative to conventional plastic films. Several studies have evaluated the applicability of biodegradable film mulching (BRM). However, the large-scale effects of BRM on the spatiotemporal distribution of evapotranspiration (ET) and crop yields remain unclear. Furthermore, existing DSSAT (decision support system for agrotechnology transfer) models cannot accurately represent crop yields, as they fail to account for the impact of the disintegration rates of biodegradable films (DR) on air temperature (Ta) compensation, which represents the effect of soil temperature changes in mulched conditions on crop growth in the form of air temperature changes. Therefore, this study proposed an improved DSSAT model under BRM (BDSSAT) by considering the impact of DR on Ta compensation, and the performance of the model was compared with those of the DSSAT and modified DSSAT (MDSSAT). These models were validated using observed data from nine typical experimental sites in Inner Mongolia from 2015 to 2018. Moreover, 96 meteorological stations and soil textures were selected to evaluate the effect of BRM on soil water storage (SWS) (the amount of water stored in a given soil body, mm), ET, and crop yields of the primary cultivated crops (sunflower, potato, corn). Our findings demonstrated that the BDSSAT model could accurately simulate the SWS, ET, and crop yield under BRM. The normalized root mean square error (nRMSE) of ET simulated by the BDSSAT model was 49.4 % and 66.3 % lower than that of the MDSSAT and DSSAT models, respectively. An apparent spatial difference in SWS, ET, and crop yield was found in different regions due to the differences in soil texture, meteorological data, and agricultural management strategy. The spatial distributions of ET and crop yield of sunflower, potato, and corn in different regions were largely consistent with the spatial distribution of SWS. Furthermore, the ET and crop yield of sunflower and potato crops in different regions increased with time (2015–2018) due to an increase in rainfall. However, these effects were not observed in corn yields in the eastern region. Additionally, the DR in the western, middle, and eastern regions should not exceed 55 %, 73 %, and 90 %, respectively, to ensure high field production (crop yield was higher than the historic average). Crop yields in different regions increased in response to future climate change, as the average Ta was projected to increase by 0.8 °C from 2019 to 2050, except for sunflower yields in the western region.

Application of the CERES-Rice Model for Rice Yield Gap Analysis

Aim: This study aimed to quantify and identify the trends in the yield gaps over the 15 years (2006-07 to 2020-21) in the Karimnagar district of Telangana State in India. The DSSAT v4.7.5 CERES-Rice model was used to calculate the potential yields and then yield gaps were calculated. For the yield gaps, linear and compound growth rates were determined.
 Results: (Crop Environment Resource Synthesis) CERES-Rice model has simulated the potential yields with the given weather and soil data of Karimnagar district in India. Yield gap I has recorded positive trend, whereas the yield gap II and total yield gaps have recorded negative trend. Linear and Compound growth rates for yield gap I, yield gap II and total yield gaps were calculated in which only yield gap I showed positive growth rates whereas yield gap II and total yield gaps showed negative growth rates. However, at 5% level, the growth rates in yield gap-II are significant.
 Conclusion: It has been observed that, in Karimnagar district of Telangana, the production of rice has increased during the period under study, however there are yield gaps in rice. Using the DSSAT (Decision-support system for Agro-technology Transfer) model, potential rice yields in the Karimnagar district were predicted. There was no much variation in the yield gap I and it was noticed negative trends in yield gap II and total yield gaps. These gaps are to be filled to boost the productivity.

Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield

This study evaluated the effects of water stress on rice yield over Punjab and Haryana across North India by integrating Weather Research Forecasting (WRF) and Decision Support System for Agrotechnology Transfer (DSSAT) models. Indian Remote Sensing Satellite datasets were used to define land use/land cover in WRF. The accuracy of simulated rainfall and temperature over Punjab and Haryana was evaluated against Tropical Rainfall Measuring Mission and automated weather station data of Indian Space Research Organization, respectively. Data from WRF was used as weather input to DSSAT to simulate rice yield in Punjab and Haryana for 2009 and 2014. After simulated yield has been evaluated against district-level observed yield, the water balance components within the DSSAT model were used to analyze the impact of water stress on rice yield. The correlation (R2) between the crop water stress factor and the rice yield anomaly at the vegetative and reproductive stage was 0.64 and 0.52 for Haryana and 0.73 and 0.68 for Punjab, respectively. Severe water stress during the flowering to maturity stage inflicted devastating effects on yield. The study concludes that the regional climate simulations can be potentially used for early water stress prediction and its impact on rice yield.

Simulation of rice yield with resource conserving technologies for early, mid and end centuries under changing climatic conditions using DSSAT model

Rice (Oryza sativa L.) is one of the major crop of India and the lifeline of the Eastern states. The challenges however are to produce more food for the increasing population by utilizing limited resources and maintain it under climate change conditions. This can be achieved by devising appropriate agro-techniques. We calibrated and evaluated CERES-Rice model of Decision Support System for Agro-Technology Transfer (DSSAT) for different resource conserving technologies (RCTs) to assess their effects on yield of transplanted rice under projected climate change scenarios. Yields were projected for 2030, 2050 and 2070 using data downloaded from MarkSim® DSSAT weather file generator for RCP 4.5 climate change scenario. The simulated result indicated that the productivity of transplanted rice under resource conserving technologies is likely to increase by 11.6-14.9% in 2030, 18.6-22.3% in 2050 and 17.6-20.2% in 2070 compared to the present yield levels (2015) with current management practices. Yield can further be enhanced through adoption of appropriate resource conservation technologies (RCTs). The DSSAT model predicted the effects of RCTs in transplanted rice very well and can be a useful tool for evaluating the effects of climate change on rice under these managements.

Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu

Extended Range of Forecast Service (ERFS) is highly useful for planning of cropping season and midterm correction at the farm level. The medium-range and long-range forecast validation have many studies, whereas ERF has less that needs to be studied. Maize is an important field crop in India after rice and wheat. Therefore, the prediction of maize yield has significant importance. In the present study, ERFS data were validated by correlation analysis using monthly observed rainfall frequency and intensity. This data was imported to DSSAT (Decision Support System for Agro-technology Transfer) to simulate maize yield of Erode district of Tamil Nadu. The model output and actual yield data from Erode were compared. Forecasted monthly total rainfall was correlated at a rate of 0.97r value with that observed. Yield simulation of maize was done using DSSAT by integrating ERFS data and the observed monthly data. Mean per cent deviation among the yields of observed weather and the disaggregated one tended to be -15.7 %. The average deviation between the yields of ERF forecasted weather data and actual yield was very high ( -29.7 % ) for Erode. Mean % deviation between the yields of observed weather and the actual yield was -14.7 %. Downscaled and accurate weather forecasts could be facilitated for yield prediction of crops by DSSAT model. Yield prediction by the model under observed weather was convenient and usable. Model under-predicted the yields when using ERF data. Both model and ERF forecast need to be improved further for higher resolution.

Impact of Climate Change on the Yield and Water Footprint of Winter Wheat in the Haihe River Basin, China

Climate change can impact the yield and water footprint of crops. Therefore, assessing such impacts carries great significance for regional water and food security. This study validated and verified the variety parameters of winter wheat for the Decision Support System for Agrotechnology Transfer (DSSAT) model, using the long-term (1993–2013) growth and yield data observed from six agricultural experiment stations in the Haihe River Basin (HRB), China. The growth process was simulated under three representative concentration pathways (RCPs), named RCP2.6, RCP4.5, and RCP8.5—climate scenarios driven by the HadGEM2-ES model. The variety parameters of winter wheat showed high accuracy in the simulation of the anthesis and maturity dates, and could be used for long-term prediction of the growth process. The trends of climate change had positive impacts on the water footprint of winter wheat but adverse impacts on the yield. The growing period was shortened by 3.6 days, 4.7 days, and 5.0 days per decade in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, due to the rapid accumulation of heat. The yield would be increased in lower emissions scenarios (17% in RCP2.6), but decreased in high-emissions scenarios due to high temperatures, which may restrict the growth of wheat. The water footprint was decreased by 10%, 11%, and 13% in the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, indicating that the water-use efficiency could be improved in the future. The results showed broad application prospects of the DSSAT model in simulating the response of crop growth to climate change.

Assessing the sweet sorghum-based ethanol potential on saline\u2013alkali land with DSSAT model and LCA approach

BackgroundThe key problem of non-grain energy plants’ scale development is how to estimate the potential of GHG emission reduction accurately and scientifically. This study presents a method coupled DSSAT (the Decision Support System for Agrotechnology Transfer) and the life cycle assessment (LCA) method to simulate the spatial distribution of sweet sorghum-based ethanol production potential on saline–alkali land. The GHG (greenhouse gas) emission mitigation and net energy gains of the whole life of sweet sorghum-based ethanol production were then analyzed.ResultsThe results of the case study in Dongying, Shandong Province, China showed that developing sweet sorghum-based ethanol on saline–alkali land had GHG emission mitigation and energy potentials. The LC-GHG emission mitigation potential of saline–alkali land in Dongying was estimated at 63.9 thousand t CO2 eq, equivalent to the carbon emission of 43.4 Kt gasoline. The LC-NEG potential was predicted at 5.02 PJ, equivalent to the caloric value of 109 Kt gasoline. On average, LC-GHG emission mitigation and LC-NEG were predicted at 55.09 kg CO2 eq/t ethanol and 4.33 MJ/kg ethanol, respectively.ConclusionsThe question of how to evaluate the potential of sweet sorghum-based ethanol development scientifically was solved primarily in this paper. The results will provide an important theoretical support for planning the bioenergy crops on saline–alkali land and develop the fuel ethanol industry.

Using DSSAT model to simulate water management practices on soybean under different soil types

Water management plays a crucial role in optimizing plant growth and production. The amount of water supplied and the timing of its application are the keys of irrigation management. The right irrigation system will result in optimal crop production and high water use efficiency (WUE). Modeling in DSSAT (Decision Support System for Agrotechnology Transfer) software can simulate the effect of water management on various types of plants. The study in this paper aims to find out the effect of water management systems on soybean in two types of soil with simulation. This study conducted descriptive quantitative methods. Combination between all irrigated systems and Tha tum type soil (AITT000001) obtained the highest yield due to soil texture, loam better than sand on water holding capacity. The highest WUE was reached the automatically irrigation system, both in the Tha Tum and IBSB910015 soil type. It means automatically irrigation system more precise than others.

Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model

Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.