Decision Support System For Agrotechnology Transfer Research Articles

Predicting Irrigated and Rainfed Rice Yield Under Projected Climate Change Scenarios in the Eastern Region of India

Numerous estimates for the coming decades project changes in precipitation resulting in more frequent droughts and floods, rise in atmospheric CO2 and temperature, extensive runoff leading to leaching of soil nutrients, and decrease in freshwater availability. Among these changes, elevated CO2 can affect crop yields in many ways. It is imperative to understand the consequences of elevated CO2 on the productivity of important agricultural crop species in order to devise adaptation and mitigation strategies to combat impending climate change. In this study, we have modeled rice phenology, growth phase, and yield with the “Decision Support System for Agrotechnology Transfer (DSSAT) CERES rice model” and arrived at predicted values of yield under different CO2 concentrations at four different locations in Eastern India out of which three locations were irrigated and one location was rainfed. The ECHAM climate scenario, Model for Interdisciplinary Research on Climate (MIROC)3.0 climate scenario, and ensemble models showed different levels of yield increase with a clear reduction in yield under rainfed rice as compared to irrigated rice. A distinct regional and cultivar difference in response of rice yield to elevated CO2 was seen in this study. Results obtained by simulation modeling at different climate change scenarios support the hypothesis that rice plant responses to elevated CO2 are through stimulation of photosynthesis. Realization of higher yields is linked with source sink dynamics and partitioning of assimilates wherein sink capacity plays an important role under elevated CO2 conditions.

Impact of climate change and carbon dioxide fertilization effect on irrigation water demand and yield of soybean in Serbia

SUMMARYThe Decision Support System for Agrotechnology Transfer (DSSAT) v. 4·2 crop model was used to estimate climate change impacts on soybean yield in Serbia in simulations for 2030 and 2050 integration periods using three global climate change models (GCMs): the European Centre Hamburg Model (ECHAM), The Hadley Centre Coupled Model (HadCM) and the National Center for Atmospheric Research Parallel Climate Model (NCAR-PCM) under two scenarios from the IPCC Special Report on Emissions Scenarios (IPCC 2001): A1B SRES and A2 SRES. Input data included weather data from a 1971–2000 baseline period from ten weather stations assimilated from the Republic Hydrometeorological Service of Serbia. Output results from the three GCMs under the two scenarios for 2030 and 2050 were statistically downscaled with the ‘Met & Roll’ weather generator for predicted climate conditions. Mechanical and chemical soil properties were collected in the vicinity of weather stations and analysed by the Agency for Environmental Safety in Belgrade. Genetic coefficients, for the soybean maturity group II variety, were slightly modified using the DSSAT-SOYGRO model ones. The results showed a considerable benefit of carbon dioxide fertilization on soybean yield and yield increases at all locations. The greatest estimated yield increases obtained using outputs the HadCM model for 2030 both scenarios; in 2050, however, the A2 scenario resulted in smaller increase in yield at some locations. The highest increase in yield was in the central and eastern parts of Serbia. Analyses of the climate change impacts on irrigation demand showed a great increase in the irrigation demand amount per growing season. The average irrigation demand reached the highest values in the southern and eastern parts of Serbia. Water productivity reached highest values in eastern and central locations, while the minimum is expected in the most southern and northern location. According to all results it can be concluded that soybean will benefit greatly under climate change conditions and that soybean cropping, currently most concentrated in the Vojvodina region in northern Serbia, expanding in the central part and one location in eastern Serbia.

Modeling site-specific fertilizer recommendations for maize production in the Sudan savannah agro-ecology of Ghana

Maize (Zea mays L.) production in the Sudan savannah agro-ecological zone of Ghana is hindered by erratic rainfall and low soil fertility. This study was conducted to refine profitable fertilizer recommendations for maize production on a selected benchmark soil in Sudan savannah agro-ecological zone using Decision Support System for Agro-technology transfer (DSSAT). Maize variety Obaatanpa was used for the experiment and the rates of N P K nutrients evaluated were 0-0-0, 0-90-90, 40-90-90, 80-90-90, 120-0-90, 120-45-90, 120-90-90, 120-90-0, 120-90-45 and 160-90-90 kg ha-1. Predictive ability of the model was tested and validated and the simulation of maize yields with nutrient rates. Results showed that treatment 160-90-90 was efficient for maize production in 2010 growing season and 120-0-90 was efficient for maize production in 2011 growing season. However, the use of treatment 120-90-90 was more preferred and sustainable option for maize production due to the available types and combinations of fertilizer in the Ghanaian market, affordability and prevalence of P-deficient soils. Model sensitiveness to N fertilizer rates should be reworked in order to make model predictions for treatments with low N rates more accurate. Key words: maize production, agro-ecological and fertilizer.

English

The aim of this research is to examine the impact of climate change in maize farmers’ livelihood in Zambezi region, Namibia and benefit of adaptation. Trade-off analysis–multidimensional (TOA-MD) model was presented as a method for evaluation with a combination of simulated baseline production and future simulated yield using Decision Support Systems for Agro-technology Transfer (DSSAT) in maize production system, under five different climate scenarios of Global Circulation Models (GCMs). Even though the magnitude and the impact of different GCMs differs, the projections shows to have a negative economic impact with the highest going up to 76% and lowest to be around 46% loss without any adaption strategies in the Zambezi region. Adaptation strategies and some policy options were tested. The analysis suggests that the introduction of an irrigation system may be sufficient to offset the negative effects of climate change. Since various assumptions and uncertainties are associated with using the proposed approach and results should be interpreted with caution. Despite these limitations, the methodology presented in this study shows the potential to yield new insights into the way that realistic adaptation strategies could improve the livelihoods of smallholder farmers. To safeguard the limited productive assets of rural Namibian’s, the study suggested policy aim to target pro-poor disaster management and other adoption mechanism is very important. Apart from protecting productive resources of the rural population, policy should target the diversification of the rural economic environment and strengthen rural-urban linkages. Key words: Climate change, trade-off analysis–multidimensional (TOA-MD), maize, Namibia, Zambezi.

Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. and Yang, J. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49–61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regi...

Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. and Yang, J. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49–61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERES–Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha−1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.

Parameter and uncertainty estimation for maize, peanut and cotton using the SALUS crop model

The generic and simple version of SALUS (System Approach to Land Use Sustainability) crop model was recently integrated in the DSSAT (Decision Support System for Agrotechnology Transfer) cropping system model to provide an alternative approach to more complex crop models without the need for a detailed parameterization.A previous uncertainty and sensitivity analysis of the model (SALUS-Simple) established that accurate estimation of 15 of the 20 crop parameters required for predicting crop performance under water limitation was necessary to achieve reliable simulations. The present study used a Markov Chain Monte Carlo-based Bayesian stepwise approach for estimating crop parameters in SALUS-Simple using limited, end-of-season data (limited data case) and detailed in-season data (detailed data case). Independent testing were performed using data distributed with DSSAT version 4.5.Results of the detailed data case indicated that the estimated parameters resulted in smaller deviations between simulated and measured variables and in posterior parameter distributions with smaller variances. Independent testing showed that maize growth simulations (based on both data cases) were in good agreement with observations while peanut and cotton growth was simulated with mixed success. SALUS-Simple predictions using parameters estimated in the limited data case were concordant with observations for end-of-season biomass and yield, but simulations of in-season growth were degraded relative to the use of parameters estimated in the detailed data case.We conclude that the use of a sequential approach reduced compensation errors and, the use of a range of data types combined with a higher ratio between the number of data points and the number of estimated parameters significantly reduced uncertainties associated with the estimated parameters. Furthermore, model predictions based on mean parameter values can be regarded as reliable estimators of the expected values of the distributions of model predictions when an average prediction rather than a distribution is needed. Results from this study highlighted the principle that parameters estimated based on end-of-season data do not guarantee accurate prediction of in-season growth even if a Bayesian approach is used. The ability of the SALUS-Simple model to be parameterized or adapted for simulating canopy-level potential production of annual plants based on limited data is promising. Further testing of the model will help establish its response to different soils, climates and crops.

Evaluation of the CROPGRO model for white cabbage production under temperate European climate conditions

The CROPGRO cabbage model, included in the Decision Support System for Agrotechnology Transfer (DSSAT) software, was initially calibrated under tropical Hawaiian climate conditions. Until recently, the application of this model has not been very widespread. Hence, the main objective of this study was to evaluate the CROPGRO cabbage model for white cabbage (Brassica oleracea convar. capitata var. alba L.) production under temperate European climate conditions. Data sets of field experiments from southwest Germany were used for model calibration including observed data of number of leaves, leaf area index, specific leaf area, leaf and stem weight, head weight and total aboveground biomass. For model evaluation, cabbage data sets over three years from southeast and over two years from east Germany were used. The genotype files (cultivar and ecotype), which include the main parameters of crop phenology and plant growth, were adapted to the standard European cabbage cultivar ‘Kalorama’. Observed dry matter cabbage head yields over different years and different locations ranged between 6574kgha−1 and 11926kgha−1; head yields over all datasets were predicted by the model with an accuracy of R2=0.98. Sensitivity analysis conducted under different nitrogen fertilizer amounts (−50% N to +50% N) and two fertilizer application strategies (basal and split) generated logical, useful results from an agronomic point of view. The sensitivity analysis revealed that the efficiency of nitrogen fertilization highly depends on the genetically determined potential yield of a specific cabbage cultivar under given environmental conditions. Overall, the CROPGRO cabbage model proved to be suitable for predicting white cabbage yields under temperate European climate conditions and under different management strategies.

Climate change effects on crop yields in Serbia and related shifts of Köppen climate zones under the SRES‐A1B and SRES‐A2

ABSTRACTWe considered shifts in the Köppen climate zones and the corresponding impact on the crop yields in Serbia by comparing (1) the results of downscaling with the ECMWF Hamburg Atmospheric Model 5 (ECHAM5) and regional Eta Belgrade University (EBU)‐Princeton Ocean Model (POM) model for the A1B and A2 scenarios over 2001–2030 and 2071–2100 and (2) the present climate simulations for the period 1961−1990. We analyzed the EBU‐POM regional climate model complexity by calculating the corresponding metrics. The yields of winter wheat, maize and soybeans were evaluated with the Decision Support System for Agrotechnology Transfer (DSSAT) model.In the future, the Köppen climate zones of Serbia will shift in coverage percentage and altitude from the present climate simulations toward warmer and drier climate zones. The calculated climate indices feature changes in the following parameters: increases in the mean annual temperature, growing season temperature, number of growing degree days (higher than 5 °C) and the frequency of tropical days; and decreases in the mean annual precipitation, growing season precipitation and frequency of frost days. Yields of crops (winter wheat, maize and soybeans) will increase on average under both scenarios, with the exception of maize in non‐irrigated conditions and under the A2 scenario.

Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Research based development of best management options for aerobic rice–maize cropping systems must be developed to improve water and nitrogen use efficiency. The main objective of this study was to identify water saving rice production technology for rice grown in sandy loam soils in semi-arid conditions using the calibrated CERES-Rice and Maize models of the Decision Support System for Agro Technology Transfer (DSSAT). A two-year experiment with two different crop establishment methods viz., aerobic rice and flooded rice with four nitrogen rates followed by maize under zero tilled conditions was used to calibrate and evaluate DSSAT CERES-Rice and CERES-Maize models. The calibrated models were used to develop best management options for an aerobic rice–maize sequence which can produce similar yields with water savings relative to that of traditional flooded rice–maize system. The results showed that application of 180kgNha−1 in four splits and automatic irrigation with 40mm, when soil available water (ASW) in top 30cm fell below to 60% was the best management combination for aerobic rice, saving 41% of water while producing 96% of the yield attainable under flooded conditions. Similarly for maize, application of 120kgNha−1 and irrigation with 30mm of water at 40% ASW in the top 30cm soil was the most dominant management option. Further, application of 180kgNha−1 with rice followed by 120kgNha−1 in maize provided stable yield for both aerobic and flooded rice systems over time as simulated by the model. The results illustrate that DSSAT model is a useful tool for evaluating alternative management options aimed at maintaining yields and saving water in rice–maize systems in semi-arid regions.

Solar radiation management impacts on agriculture in China: A case study in the Geoengineering Model Intercomparison Project (GeoMIP)

AbstractGeoengineering via solar radiation management could affect agricultural productivity due to changes in temperature, precipitation, and solar radiation. To study rice and maize production changes in China, we used results from 10 climate models participating in the Geoengineering Model Intercomparison Project (GeoMIP) G2 scenario to force the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. G2 prescribes an insolation reduction to balance a 1% a−1 increase in CO2 concentration (1pctCO2) for 50 years. We first evaluated the DSSAT model using 30 years (1978–2007) of daily observed weather records and agriculture practices for 25 major agriculture provinces in China and compared the results to observations of yield. We then created three sets of climate forcing for 42 locations in China for DSSAT from each climate model experiment: (1) 1pctCO2, (2) G2, and (3) G2 with constant CO2 concentration (409 ppm) and compared the resulting agricultural responses. In the DSSAT simulations: (1) Without changing management practices, the combined effect of simulated climate changes due to geoengineering and CO2 fertilization during the last 15 years of solar reduction would change rice production in China by −3.0 ± 4.0 megaton (Mt) (2.4 ± 4.0%) as compared with 1pctCO2 and increase Chinese maize production by 18.1 ± 6.0 Mt (13.9 ± 5.9%). (2) The termination of geoengineering shows negligible impacts on rice production but a 19.6 Mt (11.9%) reduction of maize production as compared to the last 15 years of geoengineering. (3) The CO2 fertilization effect compensates for the deleterious impacts of changes in temperature, precipitation, and solar radiation due to geoengineering on rice production, increasing rice production by 8.6 Mt. The elevated CO2 concentration enhances maize production in G2, contributing 7.7 Mt (42.4%) to the total increase. Using the DSSAT crop model, virtually all of the climate models agree on the sign of the responses, even though the spread across models is large. This suggests that solar radiation management would have little impact on rice production in China but could increase maize production.

Evaluating Starter N Application to Soybean With CROPGRO-Soybean Model in the Southern Guinea Savanna Agro-Ecology of Nigeria

Nitrogen (N) is essential for the growth and development of crops. It is a major soil nutrient for soybean. Deficiency of nitrogen in soil before soybean root nodule formation constitutes the most limiting factor after soil moisture for its production. This study was undertaken to determine starter N application rate for optimum soybean yield for two soybean varieties; assess Cropgro-soybean model and project soybean yield under different N use. Two field experiments were set up in a 5 × 2 split-plot arrangement in a randomized complete block design in 2009 and 2010. The N doses of 0 (control), 5, 15, 25 and 35 kg N/ha were applied one week after planting using urea to form the main plot and soybean varieties (TGx1485-1D and TGx1448-2E) constituted the sub-plot with three replications. Designated samples were used to evaluate 100 seed weight, plant dry biomass and harvest index. Data obtained were subjected to analysis of variance and means separated with least significant difference at p ? 0.05. Crop phenological data and yield at harvest were used for Decision Support System for Agrotechnology Transfer (DSSAT) Cropgro-Soybean model calibration and yield projection under different N use. Root mean square error (RMSE) and percentage error (PE) were used to analyze model outputs. The soil water balance output of the model was reviewed for explaining the components of water output in the environment. Field studies showed significant differences (p ? 0.05) in varietal response to seed weight and biomass production where variety TGx1448-2E produced a significantly higher 100 seed weight and higher plant dry biomass. Genotypic superiority of TGx1448-2E over TGx1485-1D is suggested. There was high accuracy in prediction of soybean phenology (i.e. PE < 10%) for both varieties, although high PE (> 15%) were obtained for yields of TGx1448-2E. The model accurately predicted soybean phenology to within 0-1 day of the field observed values. The soil water balance indicated 39-45 % loss of the total seasonal rainfall to runoff and 3-11 % through deep drainage. The application of N starter-dose was not effective in enhancing yield of soybean in the study-sites owing to the occurrences of intensive rainfall events that led to runoff and the loss of applied N during the cropping season.

DSSAT Nitrogen Cycle Simulation of Cover Crop–Maize Rotations under Irrigated Mediterranean Conditions

The accurate simulation of N cycle in winter cover crop–maize rotations can help to study management practices aimed at reducing N leaching. A lysimeter and field experiment were used to calibrate and test the model under irrigated Mediterranean conditions. Thereafter, the model was used to evaluate the impact of cover crop–maize rotations on N leaching for a range of soil types and irrigation managements during a 14 yr rotation in La Violada watershed, Spain. The Decision Support System for Agrotechnology Transfer (DSSAT) v4.5.0.51 soil temperature subroutine overpredicted soil temperature, but this was improved when using a new EPIC‐based soil temperature subroutine that will be available in DSSAT v4.6. The model was accurate simulating total maize N content in maize after fallow and after different cover crops (root mean square error [RMSE] = 25 kg N ha−1), adequately simulated N leaching reductions with cover crops, and was less efficient simulating soil inorganic N. Overall, the DSSAT‐CENTURY model proved appropriate to simulate N cycle. DSSAT‐CERES‐Maize did not adequately simulate the observed yield reduction under N limitation in our high yielding conditions. Simulations in La Violada watershed indicate that N leaching in maize would still be high (44–98 kg N ha−1 yr−1) under an optimized water and N management. Cover crop–maize rotations could reduce N leaching by 31% while supplying similar N availability to maize than fallow–maize rotations. The model was useful to study several management scenarios that would be difficult to investigate under field conditions.

Contribution of Drought to Potential Crop Yield Reduction in a Wheat-Maize Rotation Region in the North China Plain

With consecutive occurrences of drought disasters in China in recent years, it is important to estimate their potential impacts on regional crop production. In this study, we detect the impacts of drought on wheat and maize yield and their changes at a 0.5°×0.5° grid scale in the wheat-maize rotation planting area in the North China Plain under the A1B climate change scenario using the Decision Support System for Agrotechnology Transfer (DSSAT) model and the outputs of the regional climate modeling system – Providing Regional Climates for Impacts Studies (PRECIS). Self-calibrating palmer drought severity index was used as drought recognition indicator. Two time slices used for the study were the baseline (1961–1990) and 40 years of 2011–2050. The results indicate that the potential planting region for double crop system of wheat-maize would expend northward. The statistic conclusions of crop simulations varied considerably between wheat and maize. In disaster-affected seasons, wheat yield would increase in the future compared with baseline yields, whereas in opposite for maize yield. Potential crop yield reductions caused by drought would be lower for wheat and higher for maize, with a similar trend found for the ratio of potential crop yield reductions for both crops. It appears that the negative impact of drought on maize was larger than that on wheat under climate change A1B scenario.

Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada

Liu, S., Yang, J. Y., Drury, C. F., Liu, H. L. and Reynolds, W. D. 2014. Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario, Canada. Can. J. Soil Sci. 94: 435–452. A performance assessment of the Decision Support Systems for Agrotechnology Transfer (DSSAT) model (v4.5) including the CERES-Maize and CENTURY modules was conducted for continuous maize production under annual synthetic fertilization (CC-F) and no fertilization (CC-NF) using field data from a long-term (53-yr) cropping experiment in Ontario, Canada. The assessment was based on the accuracy with which DSSAT could simulate measured grain yield, above-ground biomass, leaf area index (LAI), soil inorganic nitrogen concentration, and soil water content. Model calibration for maize cultivar was achieved using grain yield measurements from CC-F between 2007 and 2012, and model evaluation was achieved using soil and crop measurements from both CC-F and CC-NF for the same 6-yr period. Good model–data agreement for CC-F grain yields was achieved for calibration (index of agreement, d=0.99), while moderate agreement for CC-NF grain yields was achieved for evaluation (d=0.79). Model–data agreement for above-ground biomass was good (d=0.83–1.00), but the model consistently underestimated for CC-F and overestimated for CC-NF. DSSAT achieved good model–data agreement for LAI in CC-F (d=0.82–0.99), but moderate to poor agreement in CC-NF (d=0.46–0.64). The CENTURY module of DSSAT simulated soil inorganic nitrogen concentrations with moderate to good model–data agreement in CC-F (d=0.74–0.88), but poor agreement in CC-NF (d=0.40–0.50). The model–data agreement for soil water content was moderate in 2007 and 2008 for both treatments (d=0.60–0.76), but poor in 2009 (d=0.46–0.53). It was concluded that the DSSAT cropping system model provided generally good to moderate simulations of continuous maize production (yield, biomass, LAI) for a long-term cropping experiment in Ontario, Canada, but generally moderate to poor simulations of soil inorganic nitrogen concentration and soil water content.

Assessing nitrogen fertilizer rates and split applications using the DSSAT model for rice irrigated with urban wastewater

When reclaimed wastewater containing more nutrients than conventional irrigation water is used for irrigation, alternative fertilization practices are required for agricultural productivity and environmental sustainability. The objective of this study was to assess the effect of the nitrogen (N) fertilizer rates and split N fertilizer application on rice yields grown in paddy fields irrigated with reclaimed wastewater using the Decision Support System for Agrotechnology Transfer (DSSAT) v4.5 model. The experimental plots employed a randomized complete block design with three treatments and four replications. The three treatments included ground water irrigation plots (GW), untreated wastewater irrigation plots (WW), and reclaimed wastewater irrigation plots (RWW). The input data for the DSSAT model, including weather data, irrigation amount, irrigation water quality, soil data, cropping practice data, and rice yields, were monitored and collected between 2006 and 2009. The DSSAT model was calibrated and validated with observed yield data using the root mean square error (RMSE), normalized RMSE (nRMSE), and the index of agreement (d) for the statistical indices. The values of RMSE, nRMSE, and d were 269–645kgha−1, 4.1–11.7%, and 0.94–0.95, respectively, for the calibration period. The calibrated model showed good agreement with the observed rice yields, and the values of RMSE, nRMSE, and d were 155–538kgha−1, 2.6–10.4%, and 0.95–0.98, respectively, for the validation period. The simulation results showed that the optimal N fertilizer rate for paddy field rice irrigated with reclaimed wastewater was considered to be 20–50% less than the standard fertilizer rate (SFR). Adjusting the split N fertilizer application rate was demonstrated as being enough to satisfy the target rice yield and resulted in an additional 10–20kgha−1 reduction in the amount of N fertilizer used.

Soil water balance: Comparing two simulation models of different levels of complexity with lysimeter observations

The simulation of the water balance in cropping systems is a useful tool to study how water can be used efficiently. However, this requires that models simulate water balance accurately. Beyond the typical comparison of model outputs with field observations, in this study we present the inter-comparison of models of different complexity with the same field dataset as a powerful method to assess model performance. The compared models were DSSAT (Decision Support System for Agrotechnology Transfer) and WAVE (Water and Agrochemicals in soil, crop and Vadose Environment), both describing one dimensional water transport. The soil water balance in DSSAT uses a simpler “tipping bucket” approach, while the more mechanistic WAVE integrates Richard's equation. The soil parameters were calibrated by using the Simulated Annealing (SA) global optimizing method. A continuous weighing lysimeter in a bare fallow provided the observed values of drainage and evapotranspiration (ET) while soil water content (SW) was supplied by capacitance sensors. An automated weather station recorded the weather data. After optimizing soil parameters with SA, both models performed well simulating the soil water balance components for the calibrated period. The use of cumulative values for ET and drainage in the optimization was more effective than using their daily values. For the validation period, the models predicted well soil evaporation over time but there were differences between models in the soil water and drainage simulations. In particular, WAVE predicted drainage well while DSSAT presented larger errors in the cumulative values. That could be due to the mechanistic nature of WAVE against the more functional nature of DSSAT. Further studies should be conducted to improve the quality of DSSAT drainage simulations. The good results from WAVE indicate that, after soil calibration, it could be used as a reasonable substitute for other models for periods when no drainage field measurements are available.

The variation of the water deficit during the winter wheat growing season and its impact on crop yield in the North China Plain

The North China Plain (NCP) is one of the main agricultural areas in China. However, it is also widely known for its water shortages, especially during the winter wheat growing season. Recently, climate change has significantly affected the water environment for crop growth. Analyzing the changes in the water deficit, which is only affected by climate factor, will help to improve water management in the NCP. In this study, the Decision Support System for Agrotechnology Transfer (DSSAT) was used to investigate the variations in the water deficit during the winter wheat growing season from 1961 to 2010 in 12 selected stations in the NCP. To represent the changes in the water deficit without any artificial affection, the rainfed simulation was used. Over the past 50 years, the average temperature during the winter wheat growing season increased approximately 1.42 °C. The anthesis date moved forward approximately 7-10 days and to late April, which increased the water demand in April. Precipitation in March and May showed a positive trend, but there was a negative trend in April. The water deficit in late April and early May became more serious than before, with an increasing trend of more than 0.1 mm/year. In addition, because the heading stage, which is very important to crop yield of winter wheat, moved forward, the impact of water deficit in late April was more serious to crop yield.

Validate the DSSAT Model on Winter Wheat under Conservation Tillage Treatments in the West Henan, China

Crop growth simulation models can be useful in evaluating the impacts of different tillage and residue management operations on the changes in land productivity and soil-water balance components. They offer a potentially valuable set of tools for examining questions related to performance of conservation agriculture. This can be both to improve our understanding or conceptualization of processes and to improve quantitative predictions for use by agronomists, growers, policy makers or others. We applied the new Decision Support System for Agro-technology Transfer (DSSAT) version 4.5, an improved crop growth simulation model, to three conservation agriculture treatments and one conventional tillage treatment data from a field-scale study in west Henan region of China to predict winter-wheat yield, leaf area index and soil-water balance. The sites average annual precipitation is 632mm and it had a winter wheat-fallow-winter wheat rotation. There winter wheat planting in October and harvesting in next year June. The model was calibrated using 2005-2006 winter-wheat crop data from field experiments of the four treatments. The treatments were: (1) decreased tillage (DT): mulching of 10-15cm height straw and one ploughing operation to 25cm depth on July 1st; (2) zero tillage (ZT): zero tillage with 35-40cm height straw mulching; (3) subsoiling (SS): 35-40cm height straw mulching and subsoil to 40cm depth on July 1st; (4) conventional tillage (CT): 10-15cm height straw mulching and two ploughing operations 20cm deep on July 1st and October 1st. The DSSAT satisfactorily simulated the four treatments variations in winter-wheat yield, leaf area index and soil-water balance. There was better agreement between observed and predicted yields (the error absolute values were less than 3.95% and the error mean absolute values were less than 2.78%). The mean value of root mean square errors (RMSE) for simulated leaf area index (LAI) and soil water storage were 0.41cm2·cm-2 and 0.08cm3·cm-3 for DT, ZT, SS and CT, treatment respectively. The predicted water use efficiency for the four treatments were 15.85, 15.40, 16.58 and 15.81kg·mm-1·ha-1, respectively. These values were close to the values calculated from field measured data (16.82, 14.44, 16.86 and 15.66kg·mm-1·ha-1, respectively). Although the analysis results show us that the DSSAT V4.5 is well suited for simulating winter-wheat growth in the West Henan region of China, these results are preliminary and based on only one year of experimental data and four treatments and further long-term analyses need to be carried out for improving the understanding of the conservation agriculture cropping systems in the west Henan region of China.

Maize Cultivar Specific Parameters for Decision Support System for Agrotechnology Transfer (DSSAT) Application in Tanzania

In order to develop basis for tactical or strategic decision making towards agricultural productivity improvement in Tanzania, a new approach in which crop models could be used is required. Crop specific parameters for maize cultivars in Tanzania have not been determined before and consequently; crop modeling approaches to address biophysical resource management challenges has not been effective. The objective of this study was to evaluate DSSAT (v4.5) Cropping System Model (CSM) using four adapted maize cultivars namely Stuka, Staha, TMV1 and Pioneer HB3253 for quantifying model parameters. The results indicate that maize cultivars did not differ significantly in terms of the number of days to anthesis, maturity, or grain weight except final aboveground biomass. Also, there was no difference between variables with respect to growing seasons. The cultivar specific parameters obtained were within the acceptable range of those for a hypothetical maize medium season cultivar (990002) included in the DSSAT 45 CSM. Model evaluation results indicate that using the estimated cultivar coefficients, the model simulated well the effects of varying nitrogen management as indicated by the agreement index (d-statistic) closer to unity. Therefore, it is concluded that model calibration and evaluation was satisfactory within the limits of test conditions, and that the model fitted with cultivar specific parameters can be used in simulation studies for research, farm management or decision making.