Decision Support System For Agrotechnology Transfer Research Articles

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.

Development of a Cereal–Legume Intercrop Model for DSSAT Version 4.8

Intercropping is extensively used to increase land productivity and agricultural benefits. In developing countries, intercropping has historically been one of the most widely used cropping systems. Crop models have been used to assess risk productivity over time and space, particularly in monocropping systems. Crop models, such as the Decision Support System for Agrotechnology Transfer (DSSAT), have been widely used to improve crop growth, development, and yield predictions; however, this model has some limitations when assessing interspecific competition in intercropping systems (e.g., it does not have a subroutine capable of running two crops simultaneously). Therefore, in this study, we developed a new approach to allow DSSAT to run two crop species in intercropping systems. A light interception algorithm and modified source code were integrated into the DSSAT to simulate the relay-strip intercropping system. The intercrop model developed in this study is the first intercrop model for DSSAT. This model is generic and can be employed to build other cereal–legume intercrop models for DSSAT Version 4.8. Regarding risk assessment of crop production, the model can evaluate long-term cereal–legume intercrop yields in low-input cropping systems. Therefore, before officially launching the new model in DSSAT, more field trials are recommended to rigorously evaluate and improve the model with data from different environments. The intercrop model developed in this study is simple, so this modeling approach can be employed to develop other cereal–noncereal intercrop models.

In‐season crop phenology using remote sensing and model‐guided machine learning

AbstractAccurate in‐season crop phenology estimation (CPE) using remote sensing (RS)‐based machine‐learning methods is challenging because of limited ground‐truth data. In this study, a biophysical crop model was used to guide neural network (NN)‐based, in‐season CPE. Using the Decision Support System for Agrotechnology Transfer (DSSAT), we conducted uncalibrated simulations for corn (Zea mays L.) across Iowa and Illinois in the U.S. Midwest with in‐season weather and historical information for planting and harvest. We investigated guiding the NN CPE method with growth stage (GSTD) and water stress factor (WSF) outputs from these simulations. Results show that guided NNs are able to estimate onset and progression of phenological stages more accurately than an unguided baseline and a crop model‐only method. GSTD guidance improved CPE during seasons when progress deviated from a regional average because of temperature but was detrimental during seasons of delayed planting and harvest. WSF guidance improved CPE during seasons when planting and harvest were delayed by heavy rainfall but performed less well during grainfill and mature stages. Neural network‐based CPE guided by both GSTD and WSF provided the most accurate estimates for pre‐emergence, emerged, silking, and grainfill stages as well as lower RMSE for the median stage transition date than reported in three full‐season CPE studies. An accurate RS method for estimating planting could link DSSAT simulations to the current planting window and improve upon these results. This model‐guided approach can be extended to other crops and regions to unlock in‐season crop risk assessments that are directly linked to crop phenology.

Impact of Spatial Soil Variability on Rainfed Maize Yield in Kansas under a Changing Climate

As the climate changes, a growing demand exists to identify and manage spatial variation in crop yield to ensure global food security. This study assesses spatial soil variability and its impact on maize yield under a future climate in eastern Kansas’ top ten maize-producing counties. A cropping system model, CERES-Maize of Decision Support System for Agrotechnology Transfer (DSSAT) was calibrated using observed maize yield. To account for the spatial variability of soils, the gSSURGO soil database was used. The model was run for a baseline and future climate change scenarios under two Representative Concentration Pathways (RCP4.5 and RCP8.5) to assess the impact of future climate change on rainfed maize yield. The simulation results showed that maize yield was impacted by spatial soil variability, and that using spatially distributed soils produces a better simulation of yield as compared to using the most dominant soil in a county. The projected increased temperature and lower precipitation patterns during the maize growing season resulted in a higher yield loss. Climate change scenarios projected 28% and 45% higher yield loss under RCP4.5 and RCP8.5 at the end of the century, respectively. The results indicate the uncertainties of growing maize in our study region under the changing climate, emphasizing the need for developing strategies to sustain maize production in the region.

ESTIMATION OF MAIZE GENETIC COEFFICIENTS FOR THE CERES-MAIZE MODEL USING NON-DESTRUCTIVE METHODS

The Crop Environment Resource Synthesis-Maize (CERES-Maize) mechanistic model, included in the Decision Support System for Agrotechnology Transfer (DSSAT), is a useful and powerful tool that simulates the growth and yield of maize in different environments. The qualitative and quantitative information provided to the CERES-Maize model guarantees reliability in the simulations obtained. However, it requires a lot of information such as soil characteristics, daily climate, crop characteristics and management, as well as six genetic coefficients. This research assessed a non-destructive methodology for estimating the six required parameters (genetic coefficients): P1, P2, P5, G2, G3 and PHINT, based on the maize physiology, measured from the Growing Degree Days (GDD) base 10. An experiment was established at the experimental station of the International Maize and Wheat Improvement Center (CIMMYT) in Tlaltizapan, Morelos, Mexico, where 27 white maize hybrids and 14 yellow maize hybrids were manually sown in an irrigation conservation tillage system. Once the simulations of maize growth and yield were obtained with CERES-Maize model, the genetic coefficients were calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE). After calibration of the six genetic coefficients for all hybrids, an average values of P1, G2 and G3 were within the typical range, while P2 and P5 were greater than the typical range, and PHINT was below typical range. However, the simulation model showed good performance after calibration, according to the average R2 of 0.9809 and 0.9730 between measured and simulated yields for white and yellow hybrids respectively. The coefficients estimated in this study can be used in the CERES-Maize model to simulate maize yields in different regions of the country for the hybrids used here.

Assessing Future Climate Change Impacts on Potato Yields — A Case Study for Prince Edward Island, Canada

Crop yields are adversely affected by climate change; therefore, it is crucial to develop climate adaptation strategies to mitigate the impacts of increasing climate variability on the agriculture system to ensure food security. As one of the largest potato-producing provinces in Canada, Prince Edward Island (PEI) has recently experienced significant instability in potato production. PEI's local farmers and stakeholders are extremely concerned about the prospects for the future of potato farming industries in the context of climate change. This study aims to use the Decision Support System for Agrotechnology Transfer (DSSAT) potato model to simulate future potato yields under the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios (including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The study evaluates the combined effects of changing climatic conditions at local scales (i.e., warming temperature and changing precipitation patterns) and increasing carbon dioxide (CO2) concentration in the atmosphere. The results indicate future significant declines in potato yield in PEI under the current farming practices. In particular, under the high-emission scenarios (e.g., SSP3-7.0 and SSP5-8.5), the potato yield in PEI would decline by 48% and 60% in the 2070s and by 63% and 80% by 2090s; even under the low-emission scenarios (i.e., SSP1-1.9 and SSP1-2.6), the potato yield in PEI would still decline by 6-10%. This implies that it is important to develop effective climate adaptation measures (e.g., adjusting farming practices and introducing supplemental irrigation plans) to ensure the long-term sustainability of potato production in PEI.

Evaluation of MSWX gridded data for modeling of wheat performance across Iran

Cropping system models (CSMs) are useful tools for developing and optimizing crop management strategies under specific soil and climate conditions. This requires good quality weather data, which is often lacking in many agricultural areas of the world. Studies exposed the capability of gridded weather data to be a proper source in situations where measured weather data is not available. This study assessed the accuracy of a recently published gridded weather dataset called MSWX for modeling of wheat performance in Iran. MSWX has a 0.1°× 0.1° spatial and 3–hourly temporal resolution and covers 1979 to present, and it was initially generated using European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 gridded data. For this study, the CSM-NWheat model within Decision Support System for Agrotechnology Transfer (DSSAT) was applied for modeling of wheat production of three irrigated and three rainfed cultivars at six sites in Iran during 2000–2010 with observed daily weather data. Results showed that MSWX maximum and minimum temperatures (Tmax and Tmin) had higher accuracy than AgERA5, while MSWX solar radiation had weaker performance than AgERA5. The bias of MSWX solar radiation was significantly greater than AgERA5 in some of sites. Comparing the NWheat model outputs based on measured and MSWX temperature (solar radiation) data, grain yield was estimated using MSWX with a MAE of 172 (133) kg/ha and a NRMSE of 11.4% (7.6%). The MSWX temperature (solar radiation) data had high accuracy for simulation of flowering and maturity dates with MAE of 3 and 5 (1 and 3) days and NRMSE of 5.4% and 7% (2.1% and 4.5%). The MSWX temperature data was found as the best replacement for measured temperature data in the study area. Also, it seems that MSWX solar radiation has the potential to be an adequate substitution for measured solar radiation data after further bias correction.

Seasonal prediction of crop yields in Ethiopia using an analog approach

As Ethiopia's population grows, crop yield predictions are becoming increasingly important for national food security. Accurate, easy-to-implement, and computationally efficient forecast approaches are desirable for broad applications in emerging regimes like Ethiopia. In this study, we develop and test an analog approach for pre-season crop yield prediction conditioned on antecedent precipitation and planting time soil moisture content indices to guide cultivation decision making. Historical planting time soil moisture at four selected sites were simulated using the Coupled Routing and Excess STorage (CREST) hydrological model and classified into five levels. Likewise, a historical crop yield database for each of the five classes of planting time soil moisture were constructed using the Decision Support System for Agrotechnology Transfer (DSSAT) agricultural model. Adopting maize as a representative crop, analog models based on different indexes or predictors with various lead times were constructed and used to conduct hindcasts during 1979–2014 and real-time forecast in 2018 and 2019. Both the hindcast and real-time forecasts were then evaluated against yield observations. To verify the applicability at locations with various environments, the analog models were then applied in different Agroecological Zones. The analog models were shown to be accurate and easy to implement, which may incentivize adoption by local extension agents and regional agricultural agencies to inform farmers’ crop choices.

Optimization of sowing date for spring maize in China's loess plateau based on presowing temperature and soil water content.

The sowing date of spring maize in China's Loess Plateau is often restricted by the presowing temperature and soil water content (SWC). The effective measurement of the effects of presowing temperature and SWC on the sowing date is a major concern for agricultural production in this region. In this paper, we considered the average daily air temperature of ˃10 °C in the 7 days before sowing. The Decision Support System for Agrotechnology Transfer (DSSAT) model was used to simulate a spring maize yield under distinct combinations of SWC and sowing date for 51 years (1970-2020). Subsequently, through the cumulative probability distribution function, the contribution of presowing SWC to the spring maize yield was quantified, and the optimal sowing date of spring maize in each production location was determined. The results revealed that the daily average temperature of ˃10 °C for 7 days consecutively can be used effectively as the basis for the simulation of spring maize sowing date. The presowing SWC affected the spring maize yield but did not change the optimal sowing date. When the SWC of each study site is about 70% of the field capacity, Wenshui and Yuanping can properly delay sowing, and Lin county can sow early to obtain a higher yield. This study provides an effective approach for optimizing presowing soil moisture management and the sowing date of spring maize in the semiarid regions of the Loess Plateau. © 2023 Society of Chemical Industry.

Regional integrated assessment of climate change impact on cotton production in a semi-arid environment

Climate change has a negative impact on the productivity of agricultural crops at local, regional and global levels. Foodstuff security and sustainable livelihood of cotton farmers in the core cotton growing region in Punjab, Pakistan is under threat because of decreased yield due to climate change. The quantification of the integrated impact assessment of climate change for developing adaptation approaches for cotton is vital for improving productivity at a regional level and improving food security at a national level. Two crop models were evaluated with on-farm survey data of 165 farms employing stratified random sampling techniques. Representative agricultural pathways (RAPs) were developed for characterizing future cotton production. Global climate models (GCMs) depicted a rise of 3.6 and 4.3°C for maximum and minimum temperature, respectively, along with a decreased in rainfall of 600 mm under Representative Concentration Pathway (RCP) 8.5. The expected temperature rise for the hot-dry climate would cause a reduction in productivity of 35.3 and 39.2% by mid-century for RCP4.5 and RCP8.5, respectively, according to the Decision Support System for Agrotechnology Transfer (DSSAT) model, while the Agricultural Production System Simulator (APSIM) showed a reduction of 51.1 and 59.6%, respectively. Increases under the current adaptation of a 15% increase in each of nitrogen and planting density ranged from 1.1 to 6.3% for DSSAT and 2.6 to 8.2% for APSIM. Climate-adapted cotton productivity was projected to rise from 18.7 to 35.9% for DSSAT and from 13.8 to 42% for APSIM for all GCMs. Results showed that current and future cotton systems are adversely impacted by climate change; however, climate-change-adapted management approaches could offset possible reductions in productivity. Sustained cotton productivity in the core cotton zone requires capacity building amongst farmers, enabling them to improve their crop management in the face of seasonal climate variability and future climate change.

Exploring the Potential of Cowpea-Wheat Double Cropping in the Semi-Arid Region of the Southern United States Using the DSSAT Crop Model

Information is limited on the potential of double-cropping cowpea (Vigna unguiculata L.) and wheat (Triticum aestivum L.) in the semiarid region of the southern United States. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and weather data of 80 years, we assessed the possibility of cowpea-wheat double-cropping in this region for grain purpose as affected by planting date and N application rate. Results showed that the possibility of double-cropping varied from 0% to 65%, depending on the cropping system. The possibility was less with systems comprising earlier planting dates of wheat and later planting dates of cowpea. Results indicated that cowpea-wheat double-cropping could be beneficial only when no N was applied, with wheat planted on October 15 or later. At zero N, the double-crops of cowpea planted on July 15 and wheat planted on November 30 were the most beneficial of all the 72 double-cropping systems studied. With a delay in planting cowpea, the percentage of beneficial double-cropping systems decreased. At N rates other than zero, fallow-wheat monocropping systems were more beneficial than cowpea-wheat double-cropping systems, and the benefit was greater at a higher N rate. At 100 kg N ha-1, the monocrop of wheat planted on October 15 was the most beneficial of all the 94 systems studied. Results further showed that fallow-wheat yields increased almost linearly with an increase in N rate from 0 to 100 kg∙ha-1. Fallow-wheat grain yields were quadratically associated with planting dates. With an increase in N rate, wheat yields reached the peak with an earlier planting date. Wheat yields produced under monocropping systems were greater than those produced under double-cropping systems for any cowpea planting date. Cowpea yields produced under monocropping systems were greater than those produced under any double-cropping system. The relationship between cowpea grain yields and planting dates was quadratic, with July 1 planting date associated with the maximum yields.

The El Niño-Southern Oscillation (ENSO) Effects on Cowpea and Winter Wheat Yields in the Semi-Arid Region of the Southern US

Information is limited on the effects of climate variability on cowpea (Vigna unguiculata L.) and winter wheat (Triticum aestivum L.) yields in the semiarid region of the southern US. Using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model and weather data spanning 81 years, we assessed the impact of El Niño-Southern Oscillation (ENSO) on the grain yields of these crops in the Llano Estacado region of the southern US as affected by cowpea and wheat planting dates and N application rate. Simulated results showed that the El Niño phase of ENSO produced about 30% more yields of mono-cropped cowpea than those produced under the La Niña phase, especially with the cowpeas planted in July. The cowpea yields under El Niño were about 10% more than the 81-year average normal yield, whereas those under La Niña were about 20% less. At the N rates of 0, 50, and 100 kg·ha−1, regardless of wheat planting dates, the El Niño years produced, respectively, about 8%, 40%, and 60% higher wheat yields than those produced in the La Niña years, and about 5%, 20%, and 27% more than the 81-year average normal yield. In the La Niña years, the wheat yields at 0, 50, and 100 kg N ha−1 were, respectively, about 5%, 15%, and 20% less than the normal yield with similar N levels. The impact of ENSO on wheat yields under cowpea-wheat double-cropping systems was significant, especially for the wheat crops planted on October 15 (October 30) or later following the cowpea crops planted in June (July). At zero N, the mono-cropped wheat yields were not impacted by ENSO due to N limitation. However, the double-cropped wheat yields were impacted by ENSO even when no N fertilizer was applied due to high soil N status caused by N transfer from cowpea stover residues and roots. Results indicated that management strategies need to be attentive to ENSO forecasts and adjust potential planting dates and N application rates with the ENSO phase to avert risks of crop failure and economic loss.

Comparing CSM-CROPGRO and APSIM-OzCot Simulations for Cotton Production and Eddy Covariance-Based Evapotranspiration in Mississippi

Optimizing irrigation water use efficiency (WUE) is critical to reduce the dependency of irrigated cotton (Gossypium spp.) production on depleting aquifers. Cropping system models can integrate and synthesize data collected through experiments in the past and simulate management changes for enhancing WUE in agriculture. This study evaluated the simulation of cotton growth and evapotranspiration (ET) in a grower’s field using the CSM-CROPGRO-cotton module within the Decision Support System for Agrotechnology Transfer (DSSAT) and APSIM (Agricultural Production Systems simulator)-OzCot during 2017–2018 growing seasons. Crop ET was quantified using the eddy covariance (EC) method. Data collected in 2017 was used in calibrating the models and in 2018 validating. Over two cropping seasons, the simulated seedling emergence, flowering, and maturity dates were varied less than a week from measured for both models. Simulated leaf area index (LAI) varied from measured with the relative root mean squared errors (RRMSE) ranging between 20.6% to 38.7%. Daily ET deviated from EC estimates with root mean square errors (RMSEs) of 1.90 mm and 2.03 mm (RRMSEs of 63.1% and 54.8%) for the DSSAT and 1.95 mm and 2.17 mm (RRMSEs of 64.7% and 58.8%) for APSIM, during 2017 and 2018, respectively. Model performance varied with growing seasons, indicating improving ET simulation processes and long-term calibrations and validations are necessary for adapting the models for decision support in optimizing WUE in cotton cropping systems.

Impact of climate change and planting date shifts on growth and yields of double cropping rice in southeastern China in future

CONTEXTIt is predicted that climate change characterized by global warming will adversely affect the rice. To study the impacts of future climate change on rice growth and yields are of significance to ensure world food supply. OBJECTIVEThis study aims to explore the growth and yield changes of double cropping rice in Southeast China, determine the key meteorological factors affecting the yield of double cropping rice and analyze the impact of planting date shifts on the phenology and yield of double cropping rice. METHODSThe phenology and yield of double cropping rice in the future was simulated based on Decision Support System for Agrotechnology Transfer (DSSAT) v4.7.5. The relationship between double cropping rice yield and meteorological factors is explored by using feature importance (IF) based on Random Forests model and copula entropy (CE). The impact of planting date shifts on phenology and yield of double cropping rice was determined by changing the planting date. RESULTS AND CONCLUSIONSThe result suggested that the yield of double cropping rice increased by 2.6%, 1.8%, −2.0% and 0.1% respectively under the scenarios for SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 when considered the effects of CO2. With the increase of radiative forcing, the precipitation (Pre) and solar radiation (SRAD) on yield of double cropping rice would become less and less important, and the importance of temperature on yield of double cropping rice would be greater and greater. Early planting of early rice and late planting of late rice will increase the length of phenology and yield of double cropping rice. However, the increased phenological length for early rice is mainly due to the increase for length of planting to anthesis (P-A), and late rice is due to the increase for length of anthesis to maturation (A-M). When the optimal planting date is adopted, the rice yield will increase by 4.6%, 5.4%, 3.4% and 10.2%, respectively. SIGNIFICANCEThis study provides adaptive measures and suggestions for local decision makers on how to deal with the impact of future climate change.

Combining remote sensing-derived management zones and an auto-calibrated crop simulation model to determine optimal nitrogen fertilizer rates

CONTEXTCotton is an economically important crop in Australia that requires high resource application, particularly that of nitrogen (N) fertilizers. Determining optimal N fertilizer rates that reach both economic and environmental objectives is a key challenge in cotton systems because of the inherent within-field variability and relatively low N fertilizer use efficiency (NFUE). OBJECTIVEThis study aimed to model optimal N fertilizer rates by accounting for within-field variability through management zone (MZ) delineation across a cotton field in Queensland, Australia. METHODSMZs were delineated using satellite-derived normalized difference vegetation index (NDVI) and the crop simulation model, Decision Support System for Agrotechnology Transfer (DSSAT), was automatically calibrated with a grid-search optimization algorithm and validated across two cotton seasons. A total of 336 different N fertilizer scenarios were subsequently evaluated at pre-planting and top-dressing to observe the effect on profit margin and NFUE. RESULTS AND CONCLUSIONSThe MZ delineation analysis determined that within-field variability could be best represented by two MZs, one of which displaying subsoil constraints due to high carbonate concentrations. The use of the auto-calibration algorithm led to a successful validation of the model with a Wilmott d-index of agreement ranging between 0.75 (soil nitrate) and 0.96 (aboveground biomass and plant N), respectively. The subsequent N scenario simulations indicated that by reducing N fertilizer rates by 80 and 30 kg N ha−1 across the two MZs, respectively, compared to the current industry average, profits could be maximized through maintaining yields while reducing N inputs. SIGNIFICANCEOverall, these results demonstrate the potential of combining remote sensing-derived MZs and crop model auto-calibration techniques to support the cotton industry in achieving improved resource efficiency and profit margins.

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.

Effects of Conventional Tillage and No-Tillage Systems on Maize (Zea mays L.) Growth and Yield, Soil Structure, and Water in Loess Plateau of China: Field Experiment and Modeling Studies

Cropping system models can be useful tools for assessing tillage systems, which are both economically and environmentally viable. The objectives of this study were to evaluate the decision support system for agrotechnology transfer (DSSAT) CERES-Maize model’s ability to predict maize growth and yield, as well as soil water dynamics, and to apply the evaluated model to predict evapotranspiration processes under conventional tillage (CT) and no-tillage (NT) systems in a semi-arid loess plateau area of China from 2014 to 2016. The field experiment results showed that NT increased the surface soil bulk density and water-holding capacity but decreased the total porosity for the surface soil and the maize grain yield. Model calibration for maize cultivar was achieved using grain yield measurements from 2014 to 2016 for CT, and model evaluation was achieved using soil and crop measurements from both CT and NT for the same 3 yr period. Good agreement was reached for CT grain yields for model calibration (nRMSE = 4.02%; d = 0.87), indicating that the model was successfully calibrated. Overall, the results of model evaluation were acceptable, with good agreement for NT grain yields (nRMSE = 4.26%; d = 0.86); the agreement for LAI ranged from good to moderate (RMSE = 0.30‒0.31; d = 0.84‒0.85); the agreement for soil water content was good for NT (RMSE = 0.03‒0.08; d = 0.81‒0.95), but ranged from good to poor for CT (RMSE = 0.06‒0.09; d = 0.42‒0.88); the overall agreement between measured and simulated soil water varied from poor to good depending on soil depth and tillage. It was concluded that the DSSAT CERES-Maize model provided generally good-to-moderate simulations of continuous maize production (yield and LAI) for a short-term tillage experiment in the loess plateau, China, but generally good-to-poor simulations of soil water content.

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.

Climate change impacts and adaptation strategies: An assessment on sorghum for Burkina Faso

Adaptation strategies sustaining agricultural production under climate change are urgently required in Sub-Saharan Africa. To quantify the impacts of different adaptation options in Burkina Faso, this study simulated sorghum yields under current and projected climatic conditions with and without adaptation. We used the Decision Support System for Agrotechnology Transfer (DSSAT) at 0.5° spatial resolution (around 55 km) and forced the model with two climate change scenarios. Our calibrated model showed good agreement between reported and simulated yields (Pearson’s r = 0.77; out-of-sample r = 0.68). DSSAT was configured to mimic four distinct adaptation measures: integrated soil fertility management (ISFM), irrigation, an improved variety, and agroforestry. Results show that nationally averaged sorghum yields are projected to decrease by 5.5% under high emissions by 2090 without adaptation. Major yield losses (up to 35%) would occur in the southern and western parts of the country. Our assessments identify ISFM as the most effective adaptation strategy, increasing yield up to 300%, followed by agroforestry (up to 125%), an improved variety (up to 90%), and irrigation (up to 43%) at the regional scale. ISFM is effective across all regions, while irrigation and an improved variety are most effective in the northern and western parts. Agroforestry, meanwhile, is most effective in the south and eastern part of the country. We conclude that climate change in Burkina Faso could negatively affect sorghum yields, but adequate adaptation options exist to enhance agricultural resilience.