DSSAT Model Research Articles

Assessment of DSSAT and AquaCrop models to simulate soybean and maize yield under water stress conditions

Aim of study: To evaluate the performance of DSSAT and AquaCrop models in the estimation of soybean and grain maize yield under water stress conditions in a semi-arid region. Area of study: Kermanshah, Iran. Material and methods: AquaCrop and DSSAT were assessed to simulate soybean and maize. Both models were calibrated using field data. Field experiments were performed in a randomized complete block design with eight and four irrigation treatments for soybeans and maize, respectively with three replications. Measures of Normalized Root Mean Square Error (nRMSE) and Nash-Sutcliffe Model Efficiency were used to evaluate the accuracy of the models. For this purpose, simulated values of leaf area index / green crop canopy, grain yield, biomass, and soil moisture were compared with measured data. Main results: Results indicated that the CROPGRO-Soybean in DSSAT software simulated more accurate crop growth of soybean than AquaCrop. The average nRMSE of the DSSAT model for estimating soil moisture, leaf area index, grain yield, and biomass were 6%, 14%, 16% and 20%, respectively. For maize, AquaCrop simulated crop growth more reliably than CERES-maize. The average nRMSE of 3%, 10%, 13% and 27% of the Aquacrop model in simulating the parameters of soil moisture, green crop canopy, biomass, and grain yield. Research highlights: Considering the better performance of AquaCrop for maize and DSSAT for soybean in the study area, it is not possible to propose a specific model to simulate the growth of all crops in a region.

Sustainable Maize Based Cropping Sequences for Northern Transition Zone of Karnataka, India

Maize in SW monsoon and sorghum, wheat and chickpea during winter are the important crops in Northern Transition Zone (NTZ) of Karnataka, India. But, due to rising temperature and erratic rainfall patterns the productivity and profitability of maize based cropping sequences are being threatened under rainfed condition. As a result, a modelling research was conducted utilising the DSSAT model's seasonal analysis tool to test and determine sustainable and profitable cropping sequences for current climates of NTZ in Karnataka state. Field experiments were conducted from 2015 to 2018 to calibrate and validate the DSSAT model for four crop cultivars (maize, chickpea, wheat, and sorghum) grown under rainfed conditions on deep black soils, and then the DSSAT model's sequential analysis tool was run for 32 years (1985-2016) for three cropping sequences (maize-sorghum, maize-wheat, and maize-chickpea). The yield, number of years the crop failed throughout different seasons, and the B:C ratio of each sequence were used in the simulated output study. Out of 32 years maize crop, grown during kharif, failed three times whereas, during rabi season wheat, sorghum and chickpea failed nine, eight and five years, respectively. Out of 32 years maize-sorghum sequence recorded the highest B:C ratio (2.86) followed by maize-chickpea (2.82) and maize-wheat (2.66). Considering chances of crop failure, B:C ratio and owing to cereal followed by short duration legume, maize-chickpea sequence under rainfed condition was proven to be the most reliable and profitable system for NTZ of Karnataka under of current climate.

The Estimation of Maize Grain Protein Content and Yield by Assimilating LAI and LNA, Retrieved from Canopy Remote Sensing Data, into the DSSAT Model

The assimilation of remote sensing data into mechanistic models of crop growth has become an available method for estimating yield. The objective of this study was to explore an effective assimilation approach for estimating maize grain protein content and yield using a canopy remote sensing data and crop growth model. Based on two years of field experiment data, the remote sensing inversion model using assimilation intermediate variables, namely leaf area index (LAI) and leaf nitrogen accumulation (LNA), was constructed with an R2 greater than 0.80 and a low root-mean-square error (RMSE). The different data assimilation approaches showed that when the LAI and LNA variables were used together in the assimilation process (VLAI+LNA), better accuracy was achieved for LNA estimations than the assimilation process using single variables of LAI or LNA (VLAI or VLNA). Similar differences in estimation accuracy were found in the maize yield and grain protein content (GPC) simulations. When the LAI and LNA were both intermediate variables in the assimilation process, the estimation accuracy of the yield and GPC were better than that of the assimilation process with only one variable. In summary, these results indicate that two physiological and biochemical parameters of maize retrieved from hyperspectral data can be combined with the crop growth model through the assimilation method, which provides a feasible method for improving the estimation accuracy of maize LAI, LNA, GPC and yield.

Optimization of cotton irrigation management for different climatic conditions using the CROPGRO-Cotton model

To study the feasibility of CROPGRO_cotton model in the optimization of cotton irrigation management under different climatic conditions, the empirical values corresponding to 25%, 50% and 75% of the empirical frequencies of precipitation were obtained by ranking the frequency fitness line of precipitation data during 20 years of cotton fertility at the experimental site, and then the years corresponding to three empirical frequencies were selected as typical years: 2015 (abundant water year), 2001 (flat water year), and 2006 (dry water year). By combining cotton fertility stages, irrigation frequency and irrigation amount, 21 irrigation regimes (T1~T21) were identified and simulated using the calibrated DSSAT model for cotton irrigation regimes under the three precipitation year types, and the results showed that under the best combination of irrigation usage, yield and water utilization, 2015 (abundant water year), 2001 (flat water year), and T20 irrigation management should be selected in 2006 (dry water year).Under the three typical years, the effect of temperature change on cotton yield and water utilization was investigated, and it was found that the increase of temperature would reduce cotton yield and water utilization, but a reasonable irrigation management would reduce the negative effect of climate change on cotton yield and water utilization,According to the simulation results under different situations, T20 irrigation management can minimize the yield variation range under temperature change, and has a high water utilization rate, which has good applicability. Keywords: Cotton; DSSAT model; Irrigation management; Typical years; Applicability

Sustainability of cover cropping practice with changing climate in Illinois

Climate change could adversely impact the best management practices (BMPs) designed to build a sustainable agro-ecological environment. Cover cropping is a conservation practice capable of reducing nitrate-nitrogen (NO3–N) loadings by consuming water and nitrate from the soil. The objective of this study was to investigate how climate change would impact the proven water quality benefits of cereal rye as a winter cover crop (CC) over the climate divisions of Illinois using the DSSAT model. Moreover, this study explores the sustainability of the CC with the changing climate conditions by using five regional climate models (RCMs) projections of two warming scenarios-rcp45 (a medium emission scenario - radiative forcing of 4.5 W/m2) and rcp85 (a high emission scenario - radiative forcing of 8.5 W/m2)). The CC impact simulated in the warming scenarios for the near-term (2021–2040) and the far-term future (2041–2060) were compared with the baseline scenario (2001–2020). Our results conclude that the climate change may negatively impact [average of CC and no CC (NCC)] maize yield (−6.6%) while positively affecting soybean yield (17.6%) and CC biomass (73.0%) by the mid-century. Increased mineralization caused by rising temperature could increase the nitrate loss via tile flow (NLoss) and nitrate leached (NLeached) up to 26.3% and 7.6% on average by the mid-century in Illinois. Increasing CC biomass could reduce the NLoss more considerably in all the scenarios compared to the baselines. Nevertheless, the NLoss level in the CC treatment can increase from the near-term to far-term future and could get closer to the baseline levels in the NCC treatment. These results suggest that CC alone may not address nitrate loss goals via subsurface drainage (caused by increasing N mineralization) in future. Therefore, more robust and cost-effective BMPs are needed to aid the CC benefits in preventing nutrient loss from the agricultural fields.

Decision-making method for maize irrigation in supplementary irrigation areas based on the DSSAT model and a genetic algorithm

Decision-making method for maize irrigation in supplementary irrigation areas based on the DSSAT model and a genetic algorithm

Evaluation of long-term impact of cereal rye as a winter cover crop in Illinois

Extensive tile drainage usage combined with excess nitrogen fertilization has triggered nutrient loss and water quality issues in Illinois, which over time endorsed the hypoxia formation in the Gulf of Mexico. Past research reported that the use of cereal rye as a winter cover crop (CC) could be beneficial in reducing nutrient loss and improving water quality. The extensive use of CC may aid in reducing the hypoxic zone in the Gulf of Mexico. The objective of this study is to analyze the long-term impact of cereal rye on soil water‑nitrogen (N) dynamics and cash crops growth in the maize-soybean agroecosystem in the state of Illinois. A gridded simulation approach was developed using the DSSAT model for the CC impact analysis. The CC impacts were estimated for the last two decades (2001–2020) for two fertilization scheduling (FA-SD = Fall and side-dress N and SP-SD = Spring pre-plant and side-dress N) comparing between CC scenario (FA-SD-C/SP-SD-C) with no CC (NCC) scenario (FA-SD-N/SP-SD-N). Our results suggest that the nitrate-N loss (via tile flow) and leaching reduced by 30.6 % and 29.4 %, assuming extensive adaptation of cover crop. The tile flow and deep percolation decreased by 20.8 % and 5.3 %, respectively, due to cereal rye inclusion. The model performance was relatively poor in simulating the CC impact on soil water dynamics in the hilly topography of southern Illinois. Generalizing changes in the soil properties (due to cereal rye inclusion) from the field scale to whole state (regardless of soil type) could be one of the possible limitations in this research. Overall, these findings substantiated the long-term benefits of cereal rye as a winter cover crop and found the spring N fertilizer application reduced nitrate-N loss compared to fall N application. These results could be helpful in promoting the practice in the Upper Mississippi River basin.

Grain Yield and Water Productivity of Irrigated Rice Affected by Transplanting Dates in Bangladesh

Selection of suitable transplanting window is essential for getting desired crop yield and optimizing irrigation water. This study was conducted in four different locations of Bangladesh (Gazipur, Mymensingh, Cumilla and Bogura districts) to investigate the effect of transplanting period on irrigation water productivity during irrigated rice (Boro) cultivation. Ceres-rice model incorporated in DSSAT was used to estimate rice grain yield and agronomic parameters for Boro 2016-17. Daily weather data and soil data were collected from Bangladesh Meteorological Department (BMD) and Soil Resource Development Institute (SRDI). The estimated irrigation scheduling using CROPWAT- 8.0 model was used as input to the DSSAT model. Rainfall distribution showed only about 22% (2% in winter and 20% in pre-monsoon) of annual rainfall occurred in irrigated rice growing period. Delay transplanting after 15 December, the cultivar BRRI dhan28 faced higher mean daily temperature resulted shorter life span. The increased seasonal mean temperature by 2.8ºC in Gazipur and Bogura and 2.6ºC in Mymensingh and Cumilla from 15 December to 01 March reduced growth duration by 24 days in Gazipur, Mymensingh and Bogura and by 26 days in Cumilla district. Cumilla received the maximum rainfall, however Gazipur experienced the lowest among the four study locations. The received rainfall amount increased with the advancement of transplanting date from 15 December. The increased rainfall reduced the irrigation demand of the cultivar. On the contrary, reduced growth duration due to delay transplanting decreased the grain yield. Transplanting up to 1 February produced almost similar grain yield, while irrigation demand decreased from 15 December transplanting. Water productivity showed increasing trend for late transplanting. Considering grain yield and irrigation water productivity, 15 January to 1 February transplanting were found suitable transplanting period for the study locations. Rice crop establishment within the recommended period could be optimized the grain yield and irrigation water productivity in the selected study locations. Thus, maximum coverage of rainfall can be reduced the irrigation demand. Consequently, it may help to optimize groundwater use and to arrest the groundwater mining. Bangladesh Rice J. 25 (2) : 21-30, 2021

Strategies to cope with warming and reduced rainfall in green gram for northern transition zone of Karnataka, India

South West monsoon has become more erratic and unpredictable in the northern Transition Zone of Karnataka and in the coming decades this will continue further with warming. To cope with change in climate, agronomic adaptation strategies (optimum sowing window and application of irrigation at critical stages) are required to maintain higher yields of greengram. As a result, the DSSAT model was used to investigate the influence of climate change on greengram. The study included a combination of two temperature (+1 and +2 °C) and two reduced rainfall (˗10 and ˗20 %) scenarios in comparison with the baseline scenario (i.e., current climate). These scenarios were built for 32 years using historical weather data from 1985-2016. With regard to adaptation strategies, six dates of sowing; starting from June 1st week to July 2nd week at a weekly interval and four irrigation treatments each of 60 mm; one at pre-flowering stage, one at pod formation stage, one each at pre-flowering and pod formation stages, and no irrigation (rainfed) were included. Between sowing dates, the model's simulation of average grain yield across 32 years revealed that, 3rd (513 kg ha-1) and 4th (508 kg ha-1) week of June were found to be optimum under future climate. Irrigation at any of the critical stages increased the yield, but largest positive yield response was replicated with two irrigations: one at pre-flowering and the other during the pod formation stage (556 kg/ha). This study clearly showed that under future climates of 1 to 2 ℃ warming with reduced rainfall scenarios (-10 % & -20 %), sowing on 3rd and 4th week of June is best one, providing two irrigations (60 mm each) one at pre-flowering and the other at pod formation stage would more than compensate the loss in yield projected under changing climates in coming decades.

Evaluation of the DSSAT model for maize yield estimate under different irrigation regimes

This study has been designed to evaluate the performance of DSSAT model for yield estimation of maize under different irrigation levels and methods. Experiment has been conducted during rabi 2015-16 in water Technology Centre, Rajendranagar. Observed yields were compared with the models simulated yields. From the results it has been revealed that drip irrigation scheduled at 1.0 Epan produced higher growth, yield, and yield attributes than the other surface and drip irrigation treatments, while surface irrigation scheduled at 0.6 IW/CPE produced lower growth, yield, and yield attributes than the other treatments. Model results were also in quite agreement with the observed results where the maximum and minimum prediction errors in grain yields with DSSAT was 0.40 % and 14.86 %. Correlation coefficient of 0.98 and 0.97 was observed for water productivity and grain yield. From this study it can be concluded that DSSAT can be used for yield estimation in maize under different irrigation regimes with minimal error. Model should be used after proper calibration for better results.

Estimating rice yield by assimilating UAV-derived plant nitrogen concentration into the DSSAT model: Evaluation at different assimilation time windows

Accurately simulating crop growth and estimating yield play a paramount role in policy decision making and agriculture management. To improve the accuracy of rice yield estimation at the field scale, plant nitrogen concentration (PNC), a highly nitrogen-related variable, was derived from the UAV-based imagery through a two-year rice experiment by using the random forest (RF) algorithm; then the PNC data was assimilated into the CERES-Rice model with a particle swarm optimization (PSO) method. In this study, the sensitivity of assimilation process to the acquisition time of UAV remotely sensed PNC data, including the vegetative stage, heading stage, ripening stage and the whole growth stage, was investigated. The results showed that the relationship between the estimated and measured PNC after data assimilation at the vegetative stage was stronger than that at other growth stages. Moreover, assimilating PNC at the early growth stage could better simulate the dynamics of PNC with no nitrogen (N) stress. Due to the assumption of soil homogeneity under various N fertilization treatments, all data assimilation strategies had the tendency to overestimate PNC in the N stress condition. In addition, the accuracy of yield estimation obtained by assimilating PNC at the vegetative stage was the highest. Accordingly, the assimilation of PNC data at the early rice growth stage could provide a great potential for improving yield estimation.

The impact of 1.5 °C and 2.0 °C global warming on global maize production and trade

Climate change is becoming more and more remarkable which has an obvious impact on crop yields all over the world. Future climate scenario data was simulated by 5 climate models recommended by ISI-MIP under 4 RCP scenarios, in which the approximate scenarios with global warming by 1.5 °C and 2 °C were selected. Applying DSSAT and GTAP models, the per unit yield changes of maize in the world under global warming by 1.5 °C and 2.0 °C were analyzed and the market prices of maize at national and global levels were simulated. The results showed that, the risk of maize yield reduction under 2.0 °C scenario was much more serious than 1.5 °C scenario; the ratios of yield changes were separately 0.18% and − 10.8% under 1.5 °C and 2.0 °C scenarios. The reduction trend of total maize production is obvious in the top five countries and the main producing regions of the world, especially under the 2.0 °C scenario. The market price of maize would increase by around 0.7% and 3.4% under 1.5 °C and 2.0 °C scenarios. With the quickly increasing population in the world, it is urgent for all countries to pay enough attention to the risk of maize yield and take actions of mitigation and adaptation to climate change.

Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model

Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha−1 with a mean of 3654 kg ha−1. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha−1 with the mean of 3076 kg ha−1. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha−1 with the mean of 3855 kg ha−1. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R2 greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation.

Assessment of Projected Climate Change Impact on Green Gram Productivity through DSSAT Model under South Gujarat Environmental Condition

An investigation on climate change impact on green gram was conducted at Agronomy farm, NAU, Navsari, Gujarat during the rabi seasons of 2021-22. The calibrated and validated DSSAT CROPGRO simulation model was used to determine the productivity of green grams under various climate change scenarios. In this experiment, the effects of CO2 concentration, solar radiation and maximum and minimum temperatures on green gram productivity were examined individually and in combination. According to the sensitivity of the DSSAT CROPGRO model, under the conditions of projected climate change, the best case scenario for its favourable impact on green gram yield was a rise in CO2 concentration (600 ppm), along with solar radiation up to a certain level (1 unit), which may nullified the adverse effect of increases in temperature on green gram productivity.

Modeling the impact of winter cover crop on tile drainage and nitrate loss using DSSAT model

Increasing demand for food has amplified the use of fertilizer. Intensive agriculture practices in the Upper Mississippi basin have been linked to the formation of a hypoxic zone in the Gulf of Mexico. Previous studies have recommended the use of winter cover crop in the maize-soybean rotation as an eco-efficient solution in reducing the nitrate-nitrogen (NO3-N) loss via a sub-surface drainage system. Therefore, the objective of this study is to evaluate the impact of cereal rye as a winter cover crop in the maize-soybean system on reducing nutrient loss via tile drainage using the DSSAT model. The experiments include four treatments with a combination of two different nitrogen (N) application timing with cover crop (CC) and without cover crop (NCC): fall-applied N without cover crop (FN), spring-applied N without cover crop (SN), fall-applied N with cover crop (FCC), and spring-applied N with cover crop (SCC). The calibrated DSSAT model was utilized to assess the impact of the cover crop by comparing NO3-N losses and cash crop yields between CC and NCC treatments for different N fertilization timings. The model calibrated for cereal rye biomass in the FCC treatment estimated the observed cereal rye growth for SCC treatment considerably well (R2 >0.91). The model successfully predicted the impact of cereal rye on nitrate loss and tile drainage with 43.6% and 45.4% (48.6% and 47.8% observed) nitrate loss reduction and 21.3% and 21.0% (30.2% and 19.4% observed) tile drainage volume reduction in fall and spring N application treatments, respectively. The results from this research suggest that DSSAT can predict the cereal rye growth and assess the soil water-nutrient dynamics in both CC and NCC systems. However, the model was not able to replicate the impact of cereal rye on the cash crop yields due to the higher N mineralization simulated in the CC compared to the NCC treatments. This could be due to the use of glyphosate to terminate the cereal rye and the presence of tillage radish along with the cereal rye. The glyphosate application hastens the decomposition process; however, it also reduces the overall residue. The chemical termination and intercropping feature are not available in the DSSAT model currently. The cereal rye hosted pathogens and pests might also be responsible for lowering maize yield in the observations.

Simulation of climate change impact on water productivity of maize in central Punjab

Field experiments were conducted at the Research Farm, Department of Climate Change and Agricultural Meteorology, Punjab Agricultural University, Ludhiana during kharif 2016 and 2017. Maize variety PMH-1 was sown with a row spacing of 60 cm and plant spacing of 20 cm on three dates viz. D1-Third week of May, D2-Second week of June and D3- First week of July under two irrigation levels i.e. irrigation at IW: CPE of 1.00 (I1) and 0.75 (I2) and mulch viz. application of straw mulch @ 5 t ha–1 (M1) and without mulch (M2) in split plot design (SPD) with dates of sowing and mulch in main plots and irrigation levels in the sub-plots. Actual moisture depletion by the crop under different treatments was recorded by soil moisture depletion method. Among the dates of sowing, moisture extraction was observed to be highest under D1 (540.5 and 477.5 mm), whereas among the irrigation and mulch levels, it was lower under IW/CPE = 0.75 (461.2 and 376.9 mm) and mulch application @ 5t/ha (473.0 and 387.1 mm) during both the seasons. The ETo was calculated by open-pan evaporation, Priestley-Taylor, FAO-56, Papadakis and CROPWAT models. Among all the four methods, the Priestley-Taylor method gave higher ETo in all three dates of sowing and it was close to open-pan evaporation values except in first date of sowing during 2016, in which ETo was higher in FAO-56 (602.4 mm). The crop coefficients calculated by Papadakis method were comparatively higher (1.3 and 1.1 mm) as compared to that calculated by CROPWAT method (1.2 and 1.0 mm) and FAO-56 (1.0 and 1.1 mm) methods gave higher values of crop coefficients as compared to Priestley-Taylor method of DSSAT model (1.0 and 0.9 mm). The water use efficiency of maize was higher in D2 (11.02 kg/ha-mm, 13.43 kg/ha-mm) w.r.t grain yield as compared to D3 (10.72 kg/ha-mm, 12.97 kg/ha-mm) and D1 (9.69 kg/ha-mm, 10.65 kg/ha-mm) w.r.t grain yield, during 2016 and 2017 respectively. Among mulch and irrigation treatment was higher in M1 and I2 levels in both the years. Simulation results showed that rise in temperature would result in decreased water productivity of maize, but this decrease could be compensated by increase in CO2 concentration.

Wheat Yield Modelling using Infocrop and DSSAT Crop Simulation Models

Background: Wheat is an important crop of India That is grown in the winter season (October to April). A field study was conducted for nine plots having a dimension of 3 × 2 meter and different nutrient was applied to test the biomass yield. The objectives are to describe (a): To conduct field experiments under controlled conditions and vary the dosage of fertilizers for different treatments. (b): To observe the variation in crop yield of wheat with different dosages of fertilizers. (c): To model the crop yield of different treatments using InfoCrop and DSSAT crop models. Methods: An experiment was conducted for Wheat crop having a dimension of 3 × 2 meter at the Indian Institute of Technology, UK-India. Three plots (C1, C2, C3) was given control fertilizer supply while plot (F1, F2, F3) given -25%, +25%, +50% and plot (N1, N2, N3) given -25%, +25%, +50% w.r.t to control. The simulation model Infocrop and DSSAT were simulated to check test the model. Result: In this study, two crop simulation models namely Infocrop and DSSAT were used to predict the yield of wheat crops. Field experiments were carried out at a farm site at Roorkee, India and different treatments of fertilizers are applied to the field plots. In total, seven fertilizer treatments are used: C (controlled with a normal dosage of fertilizers), F1, F2, F3 (varying amount of applied farmyard manure), N1, N2 and N3 (varying amount of applied urea). The objective of the study was to evaluate yields of the seven treatments using Infocrop and DSSAT crop models and compare them to the observed values of yields. The grain yield of the controlled treatment (C1, C2, C3) was predicted accurately by both models. There was a significant variation in the grain yields of the F1, F2, F3 treatments with the application of farm yard manure (FYM). DSSAT under-predicted the grain yields for the plot F1, F2, F3 treatments and over-predicted these for N1, N2, N3 treatments. Overall, Infocrop, which was developed and validated for Indian conditions, simulated the grain yield more accurately as compared to the DSSAT model under varying applications of FYM and urea dosages.

Potential impact of future climate change on spatial variability of blackgram yield over Tamil Nadu

Climate change is no longer a distinct prognosis but become a reality and also proved to have its impacts on crop production. Blackgram, a C3 short duration pulse crop was considered for this study to clarify the controversial statements on adaptability of this crop for changing climate. Dynamically downscaled data (CCSM4 data using REGCM4.4 model) for 1971 to 2099 (RCP 4.5 scenario) was used for the study. The popular cultivars CO6 and VBN6 of Blackgram are employed for the study after calibration and validation of DSSAT model. The impact assessment was carried out with August 1st as sowing date. The yield of Blackgram was found to have beneficial stimulus towards the changing climate under enriched CO2. Considering cultivars, difference was noticed spatially and temporally. The average yield of VBN6 was less than CO6 during base and near century, but it got reverse with time.

Evaluating area-specific adaptation strategies for rainfed maize under future climates of India

This study investigates the spatio-temporal changes in maize yield under projected climate and identified the potential adaptation measures to reduce the negative impact. Future climate data derived from 30 general circulation models were used to assess the impact of future climate on yield in 16 major maize growing districts of India. DSSAT model was used to simulate maize yield and evaluate adaptation strategies during mid (2040-69) and end-centuries (2070-99) under RCP 4.5 and 8.5. Genetic coefficients were calibrated and validated for each of the study locations. The projected climate indicated a substantial increase in mean seasonal maximum (0.9–6.0 °C) and minimum temperatures (1.1–6.1 °C) in the future (the range denotes the lowest and highest change during all the four future scenarios). Without adaptation strategies, climate change could reduce maize yield in the range of 16% (Tumkur) to 46% (Jalandhar) under RCP 4.5 and 21% (Tumkur) to 80% (Jalandhar) under RCP 8.5. Only at Dharwad, the yield could remain slightly higher or the same compared to the baseline period (1980–2009). Six adaptation strategies were evaluated (delayed sowing, increase in fertilizer dose, supplemental irrigation, and their combinations) in which a combination of those was found to be effective in majority of the districts. District-specific adaptation strategies were identified for each of the future scenarios. The findings of this study will enable in planning adaptation strategies to minimize the negative impact of projected climate in major maize growing districts of India.

Calibration and Validation of CERES-Wheat Model for Wheat Varieties under Raipur District of Chhattisgarh


 The current study "Calibration and validation of CERES-Wheat model for wheat varieties under Raipur district of Chhattisgarh" was carried out in the Department of Agrometerology, IGKV, Raipur, for which a field experiment was done at the college of Agriculture's farm field Raipur at latitude of 21.25’ N, longitudes 81.62’E and altitude 289.5 m above mean sea level. Along with 3 wheat variety (Kanchan, HD2967 and CG1013) and three growing environment D1 (26th Nov), D2 (06th Dec) and D3 (16th Dec). This paper aimed to Genetic co-efficient of three wheat varieties determined and calibration & validation the DSSAT model. Result of study revealed that closer estimation of Validation shows days to anthesis, days to maturity and grain yields % of error and Root mean square erro (RMSE) observed values were 2.8%, 2.8%, 4.2% of error and 1.41%,1.41%, 2.12% RMSE in D1, D2, D3 for Kanchan variety and HD2967 were observed 1.4%, 4.1%, 5.6% of error and 0.71%, 2.12%, 2.83% of RMSE respectively. CG1013 observed in 1.4%, 4.2%, 7.0% of error and 0.71%, 2.12%, 3.54% RMSE respectively. Days to maturity observed in 2.7%, 6.5%, 8.6% of error and 2.12%, 4.93%, 6.36% RMSE in D1, D2, D3 for Kanchan variety and HD2967 were observed 2.7%, 1.8%, 7.4% of error and 2.12%, 1.41%, 5.66% RMSE respectively. CG1013 observed values were 0.9%, 4.7%, 7.7% and RMSE observed were 0.71%, 3.54%, 5.66% respectively. Grain yield observed were 3.9%, 1.1%, 0.6% of error and 81.32%, 21.92%, 11.31% RMSE in D1, D2, D3 for Kanchan variety and HD2967 were observed 2.7%, -2.1, 0.8% and 47.38%, 31.82%, 12.02% of RMSE respectively. CG1013 observed in 0.8%, 4.1%, 0.5% and 17.68%, 84.85%, 9.90% RMSE respectively. The model has been successfully calibrated and validated for wheat growing in Raipur, Chhattisgarh environment and can now it can be taken for further applications in natural resources management and climate change impact studies. The model performance was evaluated using % of error and RMSE and it was observed that DASST CERES model was able to predict the growth parameters like days to anthsis, day to maturity and grain yield with reasonably good accuracy (error% less than 10).