DSSAT Model Research Articles

Conventional versus agro-ecological intensification: assessing the effect of conservation agriculture in maize cropping systems with the DSSAT model in Côte d’Ivoire (West Africa)

Integrated soil fertility management options are being promoted as ways of adapting agricultural systems to sustain yields on highly degraded and poor soils encountered throughout West Africa. The efficiency of these practices may be affected by high variability and uncertainty associated with seasonal rainfall, especially for areas such as Cote d’Ivoire, where intra-seasonal rainfall has been observed to change from 1 year to another. The DSSAT crop simulation model was used in this study as a tool to evaluate the impacts of soil improvement options including inorganic fertilizer and conservation agriculture generating higher carbon sequestration and crop yield in maize agro-ecosystems. The model was calibrated using agronomic data for three cropping seasons from 2009 to 2010 in Goulikao (Center-West Cote d’Ivoire) and Aheremou 2 (Central Cote d’Ivoire), respectively and validated against independent datasets of yield of 2003–2004 seasons in the buffer zone of the Lamto Natural Reserve, Central Cote d’Ivoire. The model predicted average maize yields of 1454 kg ha−1 across the sites versus an observed average value of 1736 kg ha−1, R2 of 0.72, and RMSE of 597 kg ha−1 after the default values for stable soil organic matter fraction used in the model were substituted by the estimated one. For the validation, the predicted higher maize yield was consistently related to fallow biomass inputs and different rates of fertilizer, thus generating a RMSE of total aboveground biomass and grain yield of 606 kg ha−1 and 350 kg ha−1, respectively. The impact of fallow residues and cropping sequence on subsequent maize yield was simulated and compared with conventional fertilizer and control data using 12 years historic climate time series. We conclude that soil fertility improvements through conservation agriculture can sustain grain yield at the same level as conventional inputs of urea against larger climate variability. However, this system may be substituted by conventional agriculture when climate forecast reveals a dry cropping season year.

Contribution of simulation tools to study climate change effects on agriculture in an African country

African agriculture is highly vulnerable to climate change. The continuance of agricultural production in regions of the African continent with chronic water shortages depends upon understanding how major crops (such as wheat and maize) are impacted by climate change (i.e. higher temperatures and fluctuating rainfall). In the present study, a four-year field study (2015-2018) was conducted in an African country (Tunisia) to determine how the wheat yield and nitrate leaching were affected by climate change in 2050. DSSAT model was used as a simulation tool. The results of this study show that the wheat yield will decrease significantly by 2050 (decreasing percentage = 28%) due to climate change condition. Also, the simulation results showed that the used model (DSSAT model) is powerful tool for evaluating the effects of climate change on wheat yield and nitrate leaching. Furthermore, it is recommended to apply the adequate fertilizer amounts to enhance wheat yield resistance to future climate change. The findings of this study could be an effective guide to improve wheat management in Tunisia with respect to climate change. Overall, the study can be used to implement the most appropriate strategy of soil and crop management to minimize effects of climate changes on wheat yield.

Using the DSSAT Model to Support Decision Making Regarding Fertilizer Microdosing for Maize Production in the Sub-humid Region of Benin

Fertilizer microdosing is being widely promoted across sub-Saharan Africa, yet all recommendations regarding this technology are derived from short-term studies. Such studies are insufficient to properly assess the production risk caused by climatic variability. To address this issue while avoiding costly long-term experiments, a common and well accepted strategy is to combine results from short-term experiments with validated dynamic crop models. However, there have been few documented attempts so far to model fertilizer microdosing under sub-humid tropical conditions. The objective was therefore to evaluate the potential of the DSSAT model for simulating maize response to fertilizer microdosing, and to use the validated model to assess the effects of inter-annual rainfall variability on maize productivity and economic risk. The model was calibrated and validated against data from a 2-year on-station experiment (2014 and 2015) with 2 levels of hill-placed manure and five mineral fertilization options including broadcast and fertilizer microdosing. Model simulations were in good agreement with the observed grain and biomass yields for conventional broadcast fertilization, with relative RMSE and d-values of 12% and 0.96 for grain and 8% and 0.97 for biomass, respectively. For fertilizer microdosing, the N stress coefficient needed to be adjusted to avoid occurrence of large N stresses during simulation. After optimization, the model adequately reproduced grain yields for fertilizer microdosing, with relative RMSE of 10%. Considering the long-term scenario analysis, the use of the validated model showed that the application of 2 g of NPK15-15-15 fertilizer + 1 g urea per hill (equivalent to 23.8 kg N ha-1, 4.1 kg P ha-1 and 7.8 kg K ha-1) improved both the minimum guaranteed yield and the long-term average without increasing inter-annual variability and the economic risk compared to unfertilized plots. Even though combining microdosing with manure (1-3 t ha-1) was economically slightly riskier than microdosing alone, this risk remained low since a value-cost ratio of 2 could be achieved in almost 100% of the years. Furthermore, combined application consistently reduced the inter-annual yield variability.

DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions

Maintain high agriculture production levels while reducing environmental impacts is of primordial importance in intensive irrigated areas. Adequate management of water and nitrogen are critical for a sustainable agriculture. The objectives of this work were to (i) calibrate and validate DSSAT model for maize under different nitrogen availability conditions and (ii) assess the effect of best management practices on irrigation water needs and nitrogen losses by leaching using different scenarios in an intensive irrigated area. For model calibration and validation, three field experiments were conducted with a total of 134 plots. Then, the model application was performed in three soil types in the ‘Del Reguero’ watershed (Spain) considering (i) real irrigation applied by farmers and DSSAT automatic irrigation and (ii) a recommended dose of N fertilizer (250 kg N ha−1) compared to a traditional dose of 390 kg N ha−1. Among all plots, the model simulated reasonably well grain yield with a Root Mean Square Error (RMSE) lower than 708 kg ha−1 and high Willmott agreement index (d statistic) (>0.9). Very similar trends were observed for total biomass and total N uptake with a RMSE of 2018 kg ha−1 and 36.6 kg N ha−1. The prediction of residual nitrate in soil was acceptable with a RMSE of 43 kg N ha−1.Modeling results showed that adjusted irrigation would reduce (on average for different soil types) the total amount of seasonal irrigation water by 31% and the nitrate leaching by 97% without a significant reduction in grain yield. Regarding to N fertilizer scenarios, results showed that farmers can reduce the N fertilizer currently applied leading to a significant decrease in the N leached between 33 and 53% depending on soil types.

Estimating the potential yield and ETc of winter wheat across Huang-Huai-Hai Plain in the future with the modified DSSAT model

The DSSAT model, integrated the calibrated Hargreaves ET model and dynamic crop coefficient, was run with the generated weather data by SDSM4.2 and CanESM2 to predict the potential yield and crop water requirement (ETC) of winter wheat in the Huang-Huai-Hai Plain in China under RCP4.5 and RCP8.5 scenarios. The results showed that the spatial distribution of potential yield in the future under RCP4.5 and RCP8.5 were similar, characterized by an increasing trend from the northwest inland to the southeast coast. The spatial distribution of ETC decreased gradually from the Shandong Peninsula to the surrounding area, and the minimum ETC was observed in the southern part of Huang-Huai-Hai Plain. The potential yield, ETC, and effective precipitation during winter wheat growing seasons might increase in the future under RCP4.5, while irrigation water requirements (IWR) would decrease. Under RCP8.5, the effective precipitation during the wheat growing seasons decreased first and then increased. However, the potential yield, ETC, and IWR of winter wheat increased first and then decreased. This study can provide some scientific evidence to mitigate the negative effects of climate change on agricultural production and water use in the Huang-Huai-Hai Plain.

Sensitivity of evapotranspiration to climate change using DSSAT model in sub humid climate region of Eastern Uttar Pradesh

Climate variability impacts the components of hydrological cycle especially evapotranspiration (ET) and soil moisture, that plays a crucial role in determining water flux of an agriculture system and is thus, essential to study the response of ET to climate change. The present study is an attempt to understand the trend in observed ET (1978–2003) and variation in projected ET RegCM4.0, RCP 4.5 scenario during 2040–2060. Observed ET is compared with simulated ET using NCEP, NASA Power, RegCM4.0 and agriculture field data. Apart from studying the effect of relative humidity (RH), solar radiation (SLR), minimum and maximum temperature and wind speed (WS) on ET, the FAO Penman–Monteith and Priestly–Taylor methods in CERES Rice and CERES Wheat crop model were used to simulate ET. Further, the cumulative impact of rainfall and ET on agriculture drought has been estimated based on standardized Reconnaissance Drought Index (RDIst). The result shows a declining trend of ET during 1978–2003, but an increase during 2040s (2040–2061) for both wheat and rice. Overall, the ET simulated using weather data input from agriculture field shows highest concordance with observed ET, followed by NASA/RegCM4 and NCEP. Moreover, the FAO Penman–Monteith gives more accurate result in comparison to the Priestley–Taylor method. Environmental modification suggests that RH is the most influential parameter for ET followed by temperature, SLR and WS. Based on RDIst it was observed that rainfall is negatively associated with ET and their cumulative effect on water availability can be efficiently estimated using drought index.

Adapting the CROPGRO Model to Simulate Alfalfa Growth and Yield

Alfalfa is the main forage legume in crop livestock systems worldwide. There is still a scarcity of perennial crop models for alfalfa simulation. Regrowth and herbage yield depend on reserves, seasonal temperature and daylength. A systematic procedure was followed to develop species and cultivar parameters. CROPGRO‐PFM‐alfalfa is available in the latest DSSAT model version (4.7). Despite alfalfa’s global importance, there is a dearth of crop simulation models available for predicting alfalfa growth and yield with its associated composition. The objectives of this research were to adapt the CSM‐CROPGRO Perennial Forage Model for simulating alfalfa growth and yield and to describe model adaptation for this species. Data from six experimental plots grown under sprinkler irrigation in the Ebro valley (Northeast Spain) were used for model adaptation. Starting with parameters for Bracharia brizantha, the model adaptation was based on values and relationships reported from the literature for cardinal temperatures and dry matter partitioning. A Bayesian optimizer was used to optimize temperature effects on photosynthesis and daylength effects on partitioning and an inverse modeling technique was employed for nitrogen fixation rate and nodule growth. The calibration of alfalfa tissue composition was initiated from soybean composition analogy but was improved with values from alfalfa literature. There was considerable iteration in optimizing parameters for the processes outlined above where comparisons were made to measured data. After adaptation, the Root Mean Square Error and d‐statistic of harvested herbage averaged across 58 harvests (yield range: 990–4617 kg ha−1) were 760 kg ha−1 and 0.75, respectively. In addition, good agreement was observed for Leaf Area Index (LAI) (LAI range: 0.1–6.7) with d‐statistic of 0.71. Simulated belowground mass was within the range of literature values. The results of this study showed that CROPGRO‐PFM‐Alfalfa can be used to simulate alfalfa growth and development. Further testing with more extensive datasets is needed to improve model robustness.

An integrated modeling framework for crop and biofuel systems using the DSSAT and GREET models

As global demand for food, energy, and water resources continues to increase, decision-makers in these sectors must find sustainable ways to produce and provide for the growing population. While many models have been created to aid in decision-making in these systems, there is a lack of robust integrated models that enable an understanding of the interconnections of these systems. This study develops a modeling framework that explores the connections of the corn and ethanol systems, two major food and energy resources. A crop modeling tool (DSSAT) and a biofuel life cycle assessment tool (GREET) are connected using a service-oriented architecture programming approach. A Python program is developed to connect these two models and run scenario analyses to assess environmental impacts of the integrated system. This paper explores the impact of decisions such as fertilizer use and plant population on environmental effects of greenhouse gases, energy use, and water in the integrated system.

Grain sorghum production functions under different irrigation capacities

Water is the major factor limiting crop production in semi-arid regions of the southern and central US High Plains. The Ogallala Aquifer is the major source of irrigation water in the region. The water levels in the aquifer have been declining due to water withdrawals for irrigation exceeding mean annual recharge. As a result, some of the wells in the region are not able to meet the crop water requirements due to lower irrigation capacity (IC). Grain sorghum, one of the major crops grown in western Kansas is considered drought tolerant. There is limited literature on the effect of IC on grain sorghum yield under erratic rainfall patterns of the semi-arid High Plains. Study objectives were to: (1) evaluate the Decision Support System for Agrotechnology Transfer – Cropping System Model (DSSAT-CSM) for assessing grain sorghum yield and water productivity; (2) develop grain sorghum water production functions under five different IC, two cultivars and three irrigation scheduling strategies; and (3) evaluate response of grain sorghum under various IC in western Kansas based on long term historical weather; 4) to determine the minimum irrigation capacity required for irrigating grain sorghum in western Kansas. A calibrated and validated late maturing cultivar in prior study was used, while measured yield and phenology data of an early maturing cultivar was used to evaluate an existing cultivar in DSSAT model. Results showed that the cultivar in DSSAT were representative of the cultivars in the field with Normalized Root Mean Square of Error (NRMSE) 18%, index of agreement (I) of 0.97 and percent of deviation (d) −19 to 29%. The corresponding statistical goodness of fit values for days to flowering was 12%, 0.99 and 0–17%, respectively. Overall, the simulated yield and phenology values agreed with the measured indicating the satisfactory simulation performance of the model. Highest yield, crop water productivity and irrigation water use efficiencies were obtained from IC of 2.5–3.6 mm/day under the irrigation scheduling strategies from panicle initiation to grain filling. However, long term simulation showed that the minimum IC for growing grain sorghum varied depending on the minimum yield goal to be met based on long term perspective. In some wet years, lower IC up to 1.7 mm/day or even in some of the wettest years no irrigation was suitable. Our findings indicate that it might not be necessary to irrigate grain sorghum from planting to maturity but if producers are forced to do so by climatic conditions, an IC of 1.7–2.5 mm/day was found to produce a yield of at least 5 t/ha similar to that of 5 mm/day IC in 75% of the long-term growing seasons. If producers have to irrigate grain sorghum from panicle initiation to maturity, the 2.5 mm/day IC was found to be adequate to meet a minimum yield goal of 5 t/ha in 3 out of 4 years based on soils and long-term average climate around western Kansas. IC of 3.6 mm/day was found to be adequate if producers wish to irrigate only from panicle initiation to grain filling to meet a minimum yield goal of 6 t/ha in 75–90% of the long-term seasons.

Crop Upgrading Strategies and Modelling for Rainfed Cereals in a Semi-Arid Climate—A Review

Spatiotemporal rainfall variability and low soil fertility are the primary crop production challenges facing poor farmers in semi-arid environments. However, there are few solutions for addressing these challenges. The literature provides several crop upgrading strategies (UPS) for improving crop yields, and biophysical models are used to simulate these strategies. However, the suitability of UPS is limited by systemization of their areas of application and the need to cope with the challenges faced by poor farmers. In this study, we reviewed 187 papers from peer-reviewed journals, conferences and reports that discuss UPS suitable for cereals and biophysical models used to assist in the selection of UPS in semi-arid areas. We found that four UPS were the most suitable, namely tied ridges, microdose fertilization, varying sowing dates, and field scattering. The DSSAT, APSIM and AquaCrop models adequately simulate these UPS. This work provides a systemization of crop UPS and models in semi-arid areas that can be applied by scientists and planners.

Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data

Crop models allow simulating the development and yield of the crops, to represent and to evaluate the influence of multiple factors. The DSSAT cropping system model is one of the most widely used and contains CROPGRO module for soybean. This crop has a great importance for many southern countries of Latin America and for Argentina. Solar radiation and rainfall are necessary variables as inputs for crop models; however these data are not as readily available. The satellital products from Clouds and Earth's Radiant Energy System (CERES) and Tropic Rainfall Measurement Mission (TRMM) provide continuous spatial and temporal information of solar radiation and precipitation, respectively. This study evaluates and quantifies the uncertainty in estimating soybean yield using a DSSAT model, when recorded weather data are replaced with CERES and TRMM ones. Different percentages of data replacements, soybean maturity groups and planting dates are considered, for 2006–2016 period in Oliveros (Argentina). Results show that CERES and TRMM products can be used for soybean yield estimation with DSSAT considering that: percentage of data replacement, campaign, planting date and maturity group, determine the amounts and trends of yield errors. Replacements with CERES data up to 30% result in %RMSE lower than 10% in 87% of the cases; while the replacement with TRMM data presents the best statisticals in campaigns with high yields. Simulations based entirely on CERES solar radiation give better results than those with TRMM. In general, similar percentages of replacement show better performance in the estimation of soybean yield for solar radiation than the replacement of precipitation values.

Recommendations of Fertilizer Formulas for the Maize Production in Northern Benin

Maize cultivation under soil conditions in Benin requires high quantity of nutrients. There is therefore a need to develop adequate fertilizer recommendations in order to achieve the level of productivity that could meet the needs of the increasing population in the rural area. The present study aims to update the mineral fertilizer formulas recommended for maize production in northern Benin. An experimental program was carried out in the year 2012 on tree main soil types: ferric Luvisols, gleyic Luvisols and eutric Gleysols in two agroecolological zones of Northern Benin. The experimental design was a randomized completed bloc with four replicates, installed in farmers’ fields with the specific objective to validate five N, P, K based fertilizer formulas. The maize variety EVDT-97 STRW was used. Biophysical and economic analyses completed using the seasonal stool of the DSSAT model allowed to identify a series of efficient options. The results of variance analyses relating to the effect of different fertilizer formulas on maize grain yields showed that the rate simulated by the DSSAT model (115-30-75) produced the highest grain yields regardless of the soil types and agro-ecological zones. The ratio of observed-to-simulated values are close to 1 and the mean standard prediction error (NRMSE) between the observed and the simulated yields was comprised between 11% and 20% for gleyic Luvisols but between 21% and 30% for the other soil types. The results of the biophysical and economic analysis showed that N80P30K35 was the most efficient fertilizer formula for sustainable maize production in Northern Benin.

Calibration and Validation of DSSAT Model v4.6 for Kharif Rice in Agro-Climatic Zone (IIIB) of Bihar

Calibration and Validation of DSSAT Model v4.6 for Kharif Rice in Agro-Climatic Zone (IIIB) of Bihar

Fertilizer recommendations for maize production in the South Sudan and Sudano-Guinean zones of Benin

The present study aims to determine fertilizer (N–P–K) recommendations for maize (Zea mays L.) on Acrisols (south Benin) and Ferric and Plintic Luvisols (centre Benin). Two years (2011 and 2012) experiment was conducted at Dogbo and Allada districts (southern) and Dassa (centre Benin). Six on-farm experiments were carried out to validate fertilizer rates simulated by the DSSAT model. The experimental design in each field was a completely randomized bloc with four replications and ten N–P–K rates: 0–0–0 (control), 44–15–17.5 (standard fertilizer recommendation for maize), 80–30–40, 80–15–40, 80–30–25, 80–30–0, 69–30–40, 92–30–40, 69–15–25 and 46–15–25 kg ha−1. Treatments 44–15–17.5 and 46–15–25 showed the lowest grain and stover yields. The observed maize grain yields were highly correlated with the estimated grain yields (R2 values varied between 80 and 91% for growing season 2011 and between 68 and 94% for growing season of 2012). The NRSME values varied between 12.54 and 22.56% (for growing season of 2011) and between 13.09 and 24.13% (for growing season of 2012). The economic analysis for the past 32 years (1980–2012) including the current experiment showed that N–P–K rates 80–30–25 (at Dogbo), 80–15–40 (at Allada) and 80–30–0 (at Dassa) were the best fertilizer recommendations as they presented the highest grain yields and the best return to investment per hectare. Nevertheless, 80–30–25 is advised for Dassa considering that sustainable maize production will require regular inputs of potassium. The 2 years of field experiments were not sufficient to derive biophysically optimal fertilizer recommendation rates for each site.

Adapting DSSAT Model for Simulation of Cotton Yield for Nitrogen Levels and Planting Dates

Experiment was conducted to test and validate DSSAT model for dynamic simulation of growth, development, and seed‐cotton yield. Simulated values of days to anthesis, maturity, dry matter, and yield by model were reliable with recorded data. Seasonal analysis showed 23.0, 4.2, and 14.9% yield reduction at Faisalabad, Sahiwal, and Multan, respectively under future changing climate. Strategy analysis showed that May sown of cultivar FH‐142 at Faisalabad and Sahiwal and MNH‐886 at Multan with 200 kg N ha−1 can be a viable option to get maximum yield. The DSSAT module for Cotton Crop Modeling has been widely evaluated as a tool to predict the effect of climate change on productivity. A 2‐yr multifactorial experiment was conducted at three locations of Pakistan (Faisalabad, Sahiwal, and Multan) to test and validate this model for dynamic simulation of growth, development, and seed‐cotton (Gossypium hirsutum L.) yield of cultivars (3) at varying N increments (three levels) sown at two different timings (1 May and 1 June). The model was first calibrated with field data collected during 2014 based on the best performing treatment (May sown and 200 kg N ha−1). Data of year 2015 was then used for further validation. Modeled values of various phenological attributes (e.g., days to anthesis and maturity) by model were reliable with recorded data, having root mean square error (RMSE) less than 2 d during both years. The RMSE values for total dry matter and seed‐cotton yield were reasonably good (278–573 kg ha−1 and 237–422 kg ha−1, respectively). Applying 1980 to 2015 climate histories for the three regions, we found Faisalabad to be vulnerable up to 23.0% reduction of yield followed by Multan (14.9%), whereas the Sahiwal region is modeled as much more resilient, with less than 5% predicted reductions in yield. Finally, we found that strategic cultivar choice and timing of planting can alleviate many of the adverse impacts of changing climates on cotton yield. We conclude that the DSSAT model can be effective as a tool to make strategic cotton planting choices under changing climates.

Using the DSSAT model to simulate wheat yield and soil organic carbon under a wheat-maize cropping system in the North China Plain

AbstractCrop modelling can facilitate researchers' ability to understand and interpret experimental results, and to diagnose yield gaps. In this paper, the Decision Support Systems for Agrotechnology Transfer 4.6 (DSSAT) model together with the CENTURT soil model were employed to investigate the effect of low nitrogen (N) input on wheat (Triticum aestivum L.) yield, grain N concentration and soil organic carbon (SOC) in a long-term experiment (19 years) under a wheat-maize (Zea mays L.) rotation at Changping, Beijing, China. There were two treatments including N0 (no N application) and N150 (150 kg N ha−1) before wheat and maize planting, with phosphorus (P) and potassium (K) basal fertilizers applied as 75 kg P2O5 ha−1 and 37.5 kg K2O ha−1, respectively. The DSSAT-CENTURY model was able to satisfactorily simulate measured wheat grain yield and grain N concentration at N0, but could not simulate these parameters at N150, or SOC in either N treatment. Model simulation and field measurement showed that N application (N150) increased wheat yield compared to no N application (N0). The results indicated that inorganic fertilizer application at the rates used did not maintain crop yield and SOC levels. It is suggested that if the DSSAT is calibrated carefully, it can be a useful tool for assessing and predicting wheat yield, grain N concentration, and SOC trends under wheat-maize cropping systems.

Relationship between spring barley productivity and growing management in Lithuania’s lowland

ABSTRACTPurpose: The current study was aimed to analyse the occurrence of water and nitrogen stress in spring barley and estimate their effects on the crop performance under low-input and conventional management.Materials and methods: Field experiments were conducted during 2007–2009 at the Institute of Agriculture, Lithuanian Research Centre for Agriculture and Forestry on a sandy-loam soil. The management systems were: (a) conventional, with the application of fertilizers and pesticides adjusted to target 5 t ha−1 grain yield; and (b) low-input, without fertilizers and pesticides. Biomass and nitrogen concentration, leaf area index, soil moisture, drainage water runoff and ground water table were measured periodically during the growing season.Results: In all three experimental years, the annual precipitation was close or above the climate normal, but a large part of the rainfall (up to 310 mm) was lost through drainage contributing to the occurrence of temporary moisture deficit in late spring or summer. Water stress resulted in a lower spring barley biomass accumulation rate and lower biomass yield in the years characterized by sub-optimal rainfall distribution. Direct measurements of water retention in the soil and DSSAT model simulations gave relatively good indication of water stress occurrence. Under the low-input management, nitrogen nutrition level was a major constraint for spring barley biomass and grain yield formation.Conclusions: Under Central Lithuania’s conditions, spring barley frequently experiences temporary water stress, because a relatively high proportion of annual precipitation is lost during the non-growing period. This crop can benefit from anticipated increased precipitation and carbon dioxide levels if adequately provided with nitrogen.

CROP SIMULATION MODEL FOR MALWA REGION PUNJAB

The Crop Simulation Models are used to estimate the crop production of a region by using various parameters like temperature, soil, weather and crop management etc. There are various models for the purpose of estimating crop production of various countries. As DSSAT model, that is developed and used by USA, has a modular design and it is a daily time step model that facilitates the evaluation and application of the crop models for different purposes. But our model is specifically designed for Malwa Region Punjab. It considers various parameters of soil and crop as input and then analyzes the result of estimation of crop yield by checking available weather and soil conditions of that particular area and use a suitable algorithm to estimate the future production of the crop.

English

English

Application of DSSAT Model to Simulate Corn Yield under Long-Term Tillage and Residue Practices

Long-term (1991-2013) field experiments were conducted to study the effects of different tillage and residue management practices on grain corn production in eastern Canada. Three different tillage practices, namely conventional tillage (CT), reduced tillage (RT), and no tillage (NT), along with two residue management practices, namely with residue (R) and without residue (NR), were considered. Field measurements of grain yield, biomass, soil organic carbon (SOC), soil moisture, and mineral nitrogen were used to evaluate the performance of the DSSAT model and to understand the impacts of long-term tillage and residue management practices. The observed corn yield, biomass, LAI, and nitrate-nitrogen (NO3-N) were not found to be significantly different under the various tillage and residue practices. The observed soil moisture showed a significant difference in the upper 10 cm soil layer. The SOC pool at 0-20 cm depth was reduced by 9.5% for CT and increased by 17.3% and 7.6% for RT and NT treatments, respectively. The DSSAT model was able to simulate corn and biomass yield, LAI, soil moisture, and SOC with RMSE ranging from 2% to 31%, indicating reasonable model performance. The model did not provide accurate results for NO3-N and soil moisture simulations, with RMSE ranging from 34% to 78%. Further improvements in the model are needed to better simulate soil moisture and N dynamics under different tillage and residue practices.