Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

Wajid, Aftab; Hussain, Khalid; Ilyas, Ayesha; Habib-ur-Rahman, Muhammad; Shakil, Qamar; Hoogenboom, Gerrit

doi:10.3390/agriculture11111166

Cited by 18 publications

(8 citation statements)

References 57 publications

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“…In the current study, the sensitivity of both the APSIM and DSSAT crop models estimated the lower yield predictions by 13.6–38.2% (APSIM) and 7.4–14.7% (DSSAT) for rice under different climate scenarios ( Table 7 ). The current crop production system in most parts of the Indo-Gangetic belt showed a decreasing rice yield trend for both APSIM and DSSAT models [ 36 ]. Moreover, increasing CO2 concentration (571 ppm) will influence the total global average temperature, which will affect rice and wheat yields [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, mean wheat yield is likely to increase by 0.8–21.8 per cent (APSIM) and mixed results–decline in wheat yield by 2.9–8.8 per cent (DSSAT). A recent study also suggested that both APSIM and DSSAT yield variabilities for wheat can be managed by strategic management of fertilizer applications in some cases [ 36 ]. Socio-economic analysis for the current study estimated the impact of climate change on future agricultural production systems on farm level.…”

Section: Discussionmentioning

confidence: 99%

“…The crop models used are CERES-Rice and CERES-Wheat for DSSAT Ver. 4.5.1.023 –Stub [ 35 ] and APSIM 7.5-Oryza and APSIM-Wheat [ 36 ]. The Agricultural Production Systems Simulator (APSIM) is a software package that facilitates sub-modules combined to simulate agricultural systems [ 13 ].…”

Section: Methodsmentioning

confidence: 99%

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Climate Change Impacts Assessment Using Crop Simulation Model Intercomparison Approach in Northern Indo-Gangetic Basin of Bangladesh

Chawdhery

Al-Mueed

Wazed

et al. 2022

IJERPH

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The climate change impacts of South Asia (SA) are inextricably linked with increased monsoon variability and a clearly deteriorating trend with more frequent deficit monsoons. One of the most climate-vulnerable nations in the eastern and central Indo-Gangetic Basin is Bangladesh. There have been numerous studies on the effects of climate change in Bangladesh; however, most of them tended to just look at a small fraction of the impact elements or were climatic projections without accounting for the effects on agriculture. Additionally, simulation studies using the CERES-Rice and CERES-Wheat models were conducted for rice and wheat to evaluate the effects of climate change on Bangladeshi agriculture. However, up to now, Bangladesh has not implemented farming system ideas by integrating cropping systems with other income-generating activities. This study was conducted as part of the Indo-Gangetic Basin (IGB) regional evaluations using the protocols and integrated assessment processes of the Agricultural Model Intercomparison and Improvement Project (AgMIP). It was also done to calibrate crop models (APSIM and DSSAT) using rice and wheat. To assist policymakers in creating national and regional plans for anticipated future agricultural systems, our work on the integrated evaluation of climate change impacts on agricultural systems produced realistic predictions. The outcome of this research prescribes a holistic assessment of climate change on future production systems by including all the relevant enterprises in the agriculture sector. The findings of the study suggested two major strategies to minimize the yield and increase the profitability in a rice–wheat cropping system. Using a short-term HYV (High Yielding Variety) of rice can shift the sowing time of wheat by 7 days in advance compared to the traditional sowing days of mid-November. In addition, increasing the irrigation amount by 50 mm for wheat showed a better yield by 1.5–32.2% in different scenarios. These climate change adaptation measures could increase the per capita income by as high as 3.6% on the farm level.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Climate Change Impacts Assessment Using Crop Simulation Model Intercomparison Approach in Northern Indo-Gangetic Basin of Bangladesh

Chawdhery

Al-Mueed

Wazed

et al. 2022

IJERPH

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“…Agricultural businesses, academia, NGO's and governments all recognize the issues faced in agriculture and are working toward providing solutions to food security in a sustainable manner, yet the approaches and methods for achieving these goals vary (West et al, 2014). The need for decision support systems that integrate data from farms and provide information for farmers to make decisions has long been recognized (McBratney et al, 2005b;Wajid et al, 2021). However, even with industry led efforts into decision support systems and software, adoption of precision agriculture for efficient application of agricultural inputs remains limited (Schimmelpfennig & Lowenberg-DeBoer, 2020).…”

Section: Introductionmentioning

confidence: 99%

Assessing performance of empirical models for forecasting crop responses to variable fertilizer rates using on-farm precision experimentation

2022

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Data-driven decision making in agriculture can be augmented by utilizing the data gathered from precision agriculture technologies to make the most informed decisions that consider spatiotemporal specificity. Decision support systems utilize underlying models of crop responses to generate management recommendations, yet there is uncertainty in the literature on the best model forms to characterize crop responses to agricultural inputs likely due for the most part to the variability in crop responses to input rates between fields and across years. Seven fields with at least three years of on-farm experimentation, in which nitrogen fertilizer rates were varied across the fields, were used to compare the ability of five different model types to forecast crop responses and net-returns in a year unseen by the model. All five model types were fit for each field using all permutations of the three years of data where two years were used for training and a third was held out to represent a “future” year. The five models tested were a frequentist based non-linear sigmoid function, a generalized additive model, a non-linear Bayesian regression model, a Bayesian multiple linear regression model and a random forest regression model. The random forest regression typically resulted in the most accurate forecasts of crop responses and net-returns across most fields. However, in some cases the model type that produced the most accurate forecast of grain yield was not the same as the model producing the most accurate forecast of grain protein concentration. Models performed best when the data used for training models was collected from years with similar weather conditions to the forecasted year. The results are important to developers of decision support tools because the underlying models used to simulate management outcomes and calculate net-returns need to be selected with consideration for the spatiotemporal specificity of the data available.

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“…The Decision Support System for Agricultural Technology Transfer (DSSAT) is a crucial tool for agricultural technology transfer and the prospect for DSSAT is really valuable [9]. Using long-term weather and soil data information, it is possible to scale out short-duration field experimental results [10,11].…”

Section: Introductionmentioning

confidence: 99%

Estimating Genetic Parameters for DSSAT CROPGRO-Cotton Model Calibration and Validation

Srinivasan

Pazhanivelan

Krishnasamy

et al. 2021

IJPSS

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DSSAT CROPGRO-Cotton Model (version 4.7.5) was generally used to forecast the effect of climate change on productivity. The objective of this study was to calibrate and validate this model in Tamil Nadu, India for simulation of development, growth and seed cotton yield of Suraj cotton cultivars under varied planting dates viz., 28th July, 11th August, 18th August, 25th August, 8th September and 15th September. The model was calibrated with data (phenology, biomass and yield components) collected during 2019. Calibration of CROPGRO-Cotton model with genetic coefficients of cultivar Suraj for seed cotton yield (kg ha-1). Simulation of days to flowering, days from planting to first pod and physiological maturity, LAI and seed cotton yield with normalized RMSE (NRMSE) values of less than 10% across all the various planting dates densities were considered excellent. Finally, we discovered that planting at the right time can mitigate many of the negative effects of fluctuating weather on cotton productivity. As a result conclude that DSSAT model will be used to make decision on cotton planting in changing climates.

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Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

Cited by 18 publications

References 57 publications

Climate Change Impacts Assessment Using Crop Simulation Model Intercomparison Approach in Northern Indo-Gangetic Basin of Bangladesh

Climate Change Impacts Assessment Using Crop Simulation Model Intercomparison Approach in Northern Indo-Gangetic Basin of Bangladesh

Assessing performance of empirical models for forecasting crop responses to variable fertilizer rates using on-farm precision experimentation

Estimating Genetic Parameters for DSSAT CROPGRO-Cotton Model Calibration and Validation

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