Evaluation of the CERES-Rice version 3.0 model for the climate conditions of the state of Kerala, India

The CERES-Rice version 3.0 crop growth simulation model was calibrated and evaluated for the agroclimatic conditions of the state of Kerala in India. Genetic coefficients were developed for the rice crop variety Jaya and used for the model evaluation studies. In four experiments using different transplanting dates during the virippu season (June to September) under rainfed conditions (i.e. no irrigation), the flowering date was predicted within an error of four days and date of crop maturity within an error of two days. The model was found to predict the phenological events of the crop fairly well. The grain yield predicted by the model was within an error of 3 for all the transplanting dates, but the straw yield prediction was within an error of 27. The high accuracy of the grain yield prediction showed the ability of the model to simulate the growth of the crop in the agroclimatic conditions of Kerala. It can be concluded from this study that the model can be used for making various strategic and tactical decisions related to agricultural planning in the state

An operational approach to high resolution agro-ecological zoning in West-Africa

Research ArticleThe objective of this work is to develop a simple methodology for high resolution crop suitability analysis under current and future climate, easily applicable and useful in Least Developed Countries. The approach addresses both regional planning in the context of climate change projections and pre-emptive short-term rural extension interventions based on same-year agricultural season forecasts, while implemented with off-the-shelf resources. The developed tools are applied operationally in a case-study developed in three regions of Guinea-Bissau and the obtained results, as well as the advantages and limitations of methods applied, are discussed. In this paper we show how a simple approach can easily generate information on climate vulnerability and how it can be operationally used in rural extension servicesinfo:eu-repo/semantics/publishedVersio

Effects of climate change on rice production in the tropical humid climate of Kerala, India

The CERES-Rice v3. crop simulation model, calibrated and validated for its suitability to simulate rice production in the tropical humid climate Kerala State of India, is used for analysing the effect of climate change on rice productivity in the state. The plausible climate change scenario for the Indian subcontinent as expected by the middle of the next century, taking into account the projected emissions of greenhouse gases and sulphate aerosols, in a coupled atmosphere-ocean model experiment performed at Deutsches Klimarechenzentrum, Germany, is adopted for the study. The adopted scenario represented an increase in monsoon seasonal mean surface temperature of the order of about 1.5 °C, and an increase in rainfall of the order of 2 mm per day, over the state of Kerala in the decade 2040-2049 with respect to the 1980s. The IPCC Business-as-usual scenario projection of plant usable concentration of CO2 about 460 PPM by the middle of the next century are also used in the crop model simulation. On an average over the state with the climate change scenario studied, the rice maturity period is projected to shorten by 8 and yield increase by 12. When temperature elevations only are taken into consideration, the crop simulations show a decrease of 8 in crop maturity period and 6 in yield. This shows that the increase in yield due to fertilisation effect of elevated CO2 and increased rainfall over the state as projected in the climate change scenario nearly makes up for the negative impact on rice yield due to temperature rise. The sensitivity experiments of the rice model to CO2 concentration changes indicated that over the state, an increase in CO2 concentration leads to yield increase due to its fertilisation effect and also enhance the water use efficiency of the paddy. The temperature sensitivity experiments have shown that for a positive change in temperature up to 5 °C, there is a continuous decline in the yield. For every one degree increment the decline in yield is about 6. Also, in another experiment it is observed that the physiological effect of ambient CO2 at 425 ppm concentration compensated for the yield losses due to increase in temperature up to 2 °C. Rainfall sensitivity experiments have shown that increase in rice yield due to increase in rainfall above the observed values is near exponential. But decrease in rainfall results in yield loss at a constant rate of about 8 per 2 mm/day, up to about 16 mm/day

Modeling nitrogen and water management effects in a wheat-maize double-cropping system

Excessive N and water use in agriculture causes environmental degradation and can potentially jeopardize the sustainability of the system. A field study was conducted from 2000 to 2002 to study the effects of four N treatments (0, 100, 200, and 300 kg N ha-1 per crop) on a wheat (Triticum aestivum L.) and maize (Zea mays L.) double cropping system under 70 ± 15% field capacity in the North China Plain (NCP). The root zone water quality model (RZWQM), with the crop estimation through resource and environment synthesis (CERES) plant growth modules incorporated, was evaluated for its simulation of crop production, soil water, and N leaching in the double cropping system. Soil water content, biomass, and grain yield were better simulated with normalized root mean square errors (NRMSE, RMSE divided by mean observed value) from 0.11 to 0.15 than soil NO3-N and plant N uptake that had NRMSE from 0.19 to 0.43 across these treatments. The long-term simulation with historical weather data showed that, at 200 kg N ha-1 per crop application rate, auto-irrigation triggered at 50% of the field capacity and recharged to 60% field capacity in the 0-to 50-cm soil profile were adequate for obtaining acceptable yield levels in this intensified double cropping system. Results also showed potential savings of more than 30% of the current N application rates per crop from 300 to 200 kg N ha-1, which could reduce about 60% of the N leaching without compromising crop yields. Copyright © 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved

Modeling a wheat-maize double cropping system in China using two plant growth modules in RZWQM

Agricultural system models are potential tools for evaluating soil-water-nutrient management in intensive cropping systems. In this study, we calibrated and validated the Root Zone Water Quality Model (RZWQM) with both a generic plant growth module (RZWQM-G) and the CERES plant growth module (RZWQM-C) for simulating winter wheat (Triticum aestivum L.) and maize (Zea mays L.) double cropping systems in the Northern China Plain (NCP), China. Data were obtained from an experiment conducted at Yucheng Integrated Agricultural Experimental Station (36°57′N, 116°36′E, 28 m asl) in the North China Plain (NCP) from 1997 to 2001 (eight crop seasons) with field measurements of evapotranspiration, soil water, soil temperature, leaf area index (LAI), biomass and grain yield. Using the same soil water and nutrient modules, both plant modules were calibrated using the data from one crop sequence during 1998-1999 when detailed measurements of LAI and biomass growth were available. The calibrated models were then used to simulate maize and wheat production in other years. Overall simulation runs from 1997 to 2001 showed that the RZWQM-C model simulated grain yields with a RMSE of 0.94 Mg ha -1 in contrast to a RMSE of 1.23 Mg ha-1 with RZWQM-G. The RMSE for biomass simulation was 2.07 Mg ha-1 with RZWQM-G and 2.26 Mg ha-1 with RZWQM-C model. The RMSE values of simulated evapotranspiration, soil water, soil temperature and LAI were 1.4 mm, 0.046 m3 m-3, 1.75°C and 1.0 for RZWQM-G and 1.4 mm, 0.047 m3 m-3, 1.84°C and 1.1 for RZWQM-C, respectively. The study revealed that both plant models were able to simulate the intensive cropping systems once they were calibrated for the local weather and soil conditions. Sensitivity analysis also showed that a reduction of 25% of current water and N applications reduced N leaching by 24-77% with crop yield reduction of 1-9% only. © 2005 Elsevier Ltd. All rights reserved

Effects of cover crop on the nitrate leaching in subsurface drainage predicted by RZWQM-DSSAT hybrid model

Planting winter cover crops such as winter rye (Secale cereale L.) after corn and soybean harvest is one of the more promising practices to reduce nitrate loss to streams from tile drainage systems without negatively affecting production. Because availability of replicated tile ]drained field data is limited and because use of cover crops to reduce nitrate loss has only been tested over a few years with limited environmental and management conditions, estimating the impacts of cover crops under the range of expected conditions is difficult. If properly tested against observed data, models can objectively estimate the relative effects of different weather conditions and agronomic practices (e.g., various N fertilizer application rates in conjunction with winter cover crops). In this study, an optimized winter wheat cover crop growth component was integrated into the calibrated RZWQM ]DSSAT hybrid model, and then we compared the observed and simulated effects of a winter cover crop on nitrate leaching losses in subsurface drainage water for a corn ]soybean rotation with N fertilizer application rates over 225 kg N ha-1 in corn years

Winter cover crop effects on nitrate leaching in subsurface drainage as simulated by RZWQM-DSSAT

Planting winter cover crops such as winter rye (Secale cereale L.) after corn and soybean harvest is one of the more promising practices to reduce nitrate loss to streams from tile drainage systems without negatively affecting production. Because availability of replicated tile-drained field data is limited and because use of cover crops to reduce nitrate loss has only been tested over a few years with limited environmental and management conditions, estimating the impacts of cover crops under the range of expected conditions is difficult. If properly tested against observed data, models can objectively estimate the relative effects of different weather conditions and agronomic practices (e.g., various N fertilizer application rates in conjunction with winter cover crops). In this study, an optimized winter wheat cover crop growth component was integrated into the calibrated RZWQM-DSSAT hybrid model, and then we compared the observed and simulated effects of a winter cover crop on nitrate leaching losses in subsurface drainage water for a corn-soybean rotation with N fertilizer application rates over 225 kg N ha -1 in corn years. Annual observed and simulated flow-weighted average nitrate concentration (FWANC) in drainage from 2002 to 2005 for the cover crop treatments (CC) were 8.7 and 9.3 mg L -1 compared to 21.3 and 18.2 mg L -1 for no cover crop (CON). The resulting observed and simulated FWANC reductions due to CC were 59% and 49%. Simulations with the optimized model at various N fertilizer rates resulted in average annual drainage N loss differences between CC and CON increasing exponentially from 12 to 34 kg N ha -1 for rates of 11 to 261 kg N ha -1, but the percent difference remained relatively constant (65% to 70%). The results suggest that RZWQM-DSSAT is a promising tool to estimate the relative effects of a winter crop under different conditions on nitrate loss in tile drains, and that a winter cover crop can effectively reduce nitrate losses over a range of N fertilizer levels