Showing papers on "Crop simulation model published in 2020"

Designing crops for adaptation to the drought and high‐temperature risks anticipated in future climates

Abstract: Climate risks pervade agriculture and generate major consequences on crop production. We do not know what the next season will be like, let alone the season 30 years hence. Yet farmers need to decide on genotype and management (G×M) combinations in advance of the season and in the face of this environment risk. Beyond that, breeders must target traits for future genotypes up to 10 years ahead of their release. Here we present the case for next generation design of G×M×E for crop adaptation in future climates. We focus on adaptation to drought and high-temperature shock in sorghum [Sorghum bicolor (L.) Moench] in Australia, but the concepts are generic. The considerable knowledge of climate, both past and future, gives us insight into climate variability and trends. We know that CO and temperature are increasing, and this influences drought and high-temperature risks for crops. We also have considerable knowledge of crop growth and development responses to CO, drought, and high temperature that have been integrated into advanced crop simulation models. Here we explore by simulation the design of crops best suited to current and future environments. A yield–risk framework is used to identify adapted G×M combinations. The results in this case study indicate the urgent need for high-temperature tolerance to effects on seed set. Further, existing approaches to G×M for effective use of water through the crop cycle will not be adequate to maintain productivity once global warming of ∼2°C is reached. Improvement in transpiration efficiency offered the avenue with best potential for advancing adaptation relevant to future climates.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa.

Abstract: Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer Climate change may exacerbate current production constraints Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield Simulated responses to climate change factors were explored and compared between models Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%) Model ensembles gave greater accuracy than any model taken at random Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management

NASA/POWER and DailyGridded weather datasets-how good they are for estimating maize yields in Brazil?

Abstract: The low availability of high-quality meteorological data resulted in the development of synthetic meteorological data generated by satellite or data interpolation, which are available in grids with varying spatio-temporal resolution. Among these different data sources, NASA/POWER and DailyGridded databases have been applied for crop yield simulations. The objective of this study was to evaluate the performance of these two datasets, in different time scales (daily, 10-day, monthly, and annual), as input data for estimating potential (YP) and attainable (YA) maize yields, using the FAO Agroecological Zone crop simulation model (FAO-AEZ), properly calibrated and validated. For that, daily weather data from ten Brazilian locations were collected and compared to the data extracted from NASA/POWER and DailyGridded systems and later applied to estimate the potential and attainable maize yields. DailyGridded data showed a better performance than NASA/POWER for all weather variables and time scales, with confidence index (C) ranging from 0.52 to 0.99 for the former and from 0.09 and 0.99 for the latter. As a consequence of that, DailyGridded data was better than NASA/POWER to estimate maize yields with estimates close to those obtained with observed data, with a lower mean absolute errors (< 30 kg ha−1) and a higher confidence index (C = 0.99).

Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.

Abstract: Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.

Evaluating machine learning algorithms for predicting maize yield under conservation agriculture in Eastern and Southern Africa

Abstract: Crop simulation models are widely used as research tools to explore the impact of various technologies and compliment field experimentation. Machine learning (ML) approaches have emerged as promising artificial intelligence alternative and complimentary tools to the commonly used crop production models. The study was designed to answer the following questions: (a) Can machine learning techniques predict maize grain yields under conservation agriculture (CA)? (b) How close can ML algorithms predict maize grain yields under CA-based cropping systems in the highlands and lowlands of Eastern and Southern Africa (ESA)? Machine learning algorithms could predict maize grain yields from conventional and CA-based cropping systems under low and high potential conditions of the ESA region. Linear algorithms (LDA and LR) predicted maize yield more closely to the observed yields compared with nonlinear tools (NB, KNN, CART and SVM) under the conditions of the reported study. However, the KNN algorithm was comparable in its yield prediction to the linear tools tested in this study. Overall, the LDA algorithm was the best tool, and SVM was the worst algorithm in maize yield prediction. Evaluating the performance of different ML algorithms using different criteria is critical in order to get a more robust assessment of the tools before their application in the agriculture sector.

Modeling Adaptation Strategies against Climate Change Impacts in Integrated Rice-Wheat Agricultural Production System of Pakistan.

Abstract: There are numerous anticipated effects of climate change (CC) on agriculture in the developing and the developed world. Pakistan is among the top ten most prone nations to CC in the world. The objective of this analysis was to quantify the economic impacts of CC on the agricultural production system and to quantify the impacts of suggested adaptation strategies at the farm level. The study was conducted in the Punjab province's rice-wheat cropping system. For this purpose, climate modeling was carried out by using two representative concentration pathways (RCPs), i.e., RCPs 4.5 and 8.5, and five global circulation models (GCMs). The crop modeling was carried out by using the Agricultural Production Systems Simulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation models (CSMs), which were tested on the cross-sectional data of 217 farm households collected from the seven strata in the study area. The socio-economic impacts were calculated using the Multidimensional Impact Assessment Tradeoff Analysis Model (TOA-MD). The results revealed that CC's net economic impact using both RCPs and CSMs was negative. In both CSMs, the poverty status was higher in RCP 8.5 than in RCP 4.5. The adaptation package showed positive results in poverty reduction and improvement in the livelihood conditions of the agricultural households. The adoption rate for DSSAT was about 78%, and for APSIM, it was about 68%. The adaptation benefits observed in DSSAT were higher than in APSIM. The results showed that the suggested adaptations could have a significant impact on the resilience of the atmospheric changes. Therefore, without these adaptation measures, i.e., increase in sowing density, improved cultivars, increase in nitrogen use, and fertigation, there would be negative impacts of CC that would capitalize on livelihood and food security in the study area.

Sugarcane Yield Prediction Through Data Mining and Crop Simulation Models

Abstract: The understanding of the hierarchical importance of the factors which influence sugarcane yield can subsidize its modeling, thus contributing to the optimization of agricultural planning and crop yield estimates. The objectives of this study were to identify and ordinate the main variables that condition sugarcane yield, according to their relative importance, as well as to develop mathematical models for predicting sugarcane yield by using data mining (DM) techniques. For this, three DM techniques were applied in the analyses of databases of several sugar mills in the state of Sao Paulo, Brazil. Meteorological and crop management variables were analyzed through the following DM techniques: random forest; boosting; and support vector machine, and the resulting models were tested through the comparison with an independent data set. Finally, the predictive performances of these models were compared with the performance of a simple agrometeorological model, applied in the same data set. The results allowed to conclude that, within all the variables assessed, the number of cuts was the most important factor considered by all DM techniques. The comparison between the observed yields and those estimated by the DM models resulted in a root mean square error (RMSE) ranging between 19.70 and 20.03 t ha−1, which was much better than the performance of the Agroecological Zone Model, which presented RMSE ≈ 34 t ha−1.

New Approaches for the Assimilation of LAI Measurements into a Crop Model Ensemble to Improve Wheat Biomass Estimations

Abstract: The assimilation of LAI measurements, repeatedly taken at sub-field level, into dynamic crop simulation models could provide valuable information for precision farming applications. Commonly used updating methods such as the Ensemble Kalman Filter (EnKF) rely on an ensemble of model runs to update a limited set of state variables every time a new observation becomes available. This threatens the model’s integrity, as not the entire table of model states is updated. In this study, we present the Weighted Mean (WM) approach that relies on a model ensemble that runs from simulation start to simulation end without compromising the consistency and integrity of the state variables. We measured LAI on 14 winter wheat fields across France, Germany and the Netherlands and assimilated these observations into the LINTUL5 crop model using the EnKF and WM approaches, where the ensembles were created using one set of crop component (CC) ensemble generation variables and one set of soil and crop component (SCC) ensemble generation variables. The model predictions for total aboveground biomass and grain yield at harvest were evaluated against measurements collected in the fields. Our findings showed that (a) the performance of the WM approach was very similar to the EnKF approach when SCC variables were used for the ensemble generation, but outperformed the EnKF approach when only CC variables were considered, (b) the difference in site-specific performance largely depended on the choice of the set of ensemble generation variables, with SCC outperforming CC with regard to both biomass and grain yield, and (c) both EnKF and WM improved accuracy of biomass and yield estimates over standard model runs or the ensemble mean. We conclude that the WM data assimilation approach is equally efficient to the improvement of model accuracy, compared to the updating methods, but it has the advantage that it does not compromise the integrity and consistency of the state variables.

Spatial and Temporal Variability of Spring Barley Yield and Quality Quantified by Crop Simulation Model

Abstract: Nitrogen fertilization is the most critical agronomic input affecting barley production and farm profitability. The strict quality requirements for malting barley are challenging to achieve for farmers. In addition, soil variability and weather conditions can affect barley yield and quality. Thus, the objectives of this study are to (a) quantify the variability of soil properties, and (b) use spatial data in a crop simulation model, quantifying the impacts of climate−soil interactions on the barley crop yield and grain quality. Based on historical yield maps, a commercial field was divided into different yield stability zone levels. The Decision Support System for Agrotechnology Transfer model was used to evaluate soil and crop spatial data. The bulk density affected the soil water content and soil mineral N and hence the crop-growing conditions in each yield stability zone. Our observed and simulated results showed that 120 kg N ha−1 is the optimal rate to increase grain yield while still keeping within the grain N% requirements for malting quality. This study shows the great value of integrating crop modeling with on−farm experimental data for improving understanding of the factors which affect site−specific N fertilization of barley.

Yield Gap Analysis Using Remote Sensing and Modelling Approaches: Wheat in the Northwest of Iran

Abstract: The reduction of the yield gap is one of the strategies implemented for the improvement of food security. In this research, the yield gap of wheat in the west of Golestan province, Iran, was estimated using a two-step methodology. In the first step, the potential yield was evaluated using the SSM-iCrop2 model and in the following, the yield gap was determined by the difference between the actual yield and potential yield. In the second step, the NDVI-actual yield regression in parallel with boundary-line analysis was used to assess the attainable yield. The estimated attainable yield varied from 3.0 to 5.8 t ha−1. Accordingly, the attainable yield gap in the studied region was 2.6 t ha−1 on average, which could be obtained via improved management. Also, based on model outputs, the potential yield varied from 5.4 to 7.2 t ha−1 which suggests a high possibility to improve wheat yield in the west parts of Golestan province. The results of the study provided basic information to quantify the yield gap and yield optimization options. Our results revealed that remote sensing in combination with crop simulation models is a powerful tool in regional assessments and removes the limitations of working with point data.

How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

Abstract: The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.

Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania

Abstract: Short term food security issues require reliable crop forecasting data to identify the population at risk of food insecurity and quantify the anticipated food deficit. The assessment of the current early warning and crop forecasting system which was designed in mid 80’s identified a number of deficiencies that have serious impact on the timeliness and reliability of the data. We developed a new method to forecast maize yield across smallholder farmers’ fields in Tanzania (Morogoro, Kagera and Tanga districts) by integrating field-based survey with a process-based mechanistic crop simulation model. The method has shown to provide acceptable forecasts (r2 values of 0.94, 0.88 and 0.5 in Tanga, Morogoro and Kagera districts, respectively) 14–77 days prior to crop harvest across the three districts, in spite of wide range of maize growing conditions (final yields ranged from 0.2–5.9 t/ha). This study highlights the possibility of achieving accurate yield forecasts, and scaling up to regional levels for smallholder farming systems, where uncertainties in management conditions and field size are large.

Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements

Abstract: Novel technologies for estimating crop water needs include mainly remote sensing evapotranspiration estimates and decision support systems (DSS) for irrigation scheduling. This work provides several examples of these approaches, that have been adjusted and modified over the years to provide a better representation of the soil–plant–atmosphere continuum and overcome their limitations. Dynamic crop simulation models synthetize in a formal way the relevant knowledge on the causal relationships between agroecosystem components. Among these, plant–water–soil relationships, water stress and its effects on crop growth and development. Crop models can be categorized into (i) water-driven and (ii) radiation-driven, depending on the main variable governing crop growth. Water stress is calculated starting from (i) soil water content or (ii) transpiration deficit. The stress affects relevant features of plant growth and development in a similar way in most models: leaf expansion is the most sensitive process and is usually not considered when planning irrigation, even though prolonged water stress during canopy development can consistently reduce light interception by leaves; stomatal closure reduces transpiration, directly affecting dry matter accumulation and therefore being of paramount importance for irrigation scheduling; senescence rate can also be increased by severe water stress. The mechanistic concepts of crop models can be used to improve existing simpler methods currently integrated in irrigation management DSS, provide continuous simulations of crop and water dynamics over time and set predictions of future plant–water interactions. Crop models can also be used as a platform for integrating information from various sources (e.g., with data assimilation) into process-based simulations.

Mapping sub-field maize yields in Nebraska, USA by combining remote sensing imagery, crop simulation models, and machine learning

Abstract: Crop yield maps are valuable for many applications in precision agriculture, but are often inaccessible to growers and researchers wishing to better understand yield determinants and improve site-specific management strategies. A method for mapping sub-field crop yields from remote sensing imagery could increase the availability of crop yield maps. A variation of the scalable crop yield mapping approach (SCYM, Lobell et al. in Remote Sensing of Environment 164:324–333, 2015) was developed and tested for estimating sub-field maize (Zea mays L.) yields at 10–30 m without the use of site-specific input data. The method was validated using harvester yield monitor records for 21 site-years for irrigated and rainfed fields in eastern Nebraska, USA. Prediction error ranged greatly across site-years, with relative RMSE scores of 10.8 to 38.5%, and R2 values of 0.003 to 0.37. Significant proportional bias was detected in the predictions, but could be corrected with a small amount of ground truth data. Crop yield prediction accuracies without calibration were suitable for some precision applications such as mapping relative yields and delineating management zones, but model improvements or calibration datasets are needed for applications requiring absolute yield estimates.

Increasing Air Temperatures and Its Effects on Growth and Productivity of Tomato in South Florida

Abstract: Florida ranks first among US states in fresh-market tomato production with annual production exceeding one-third of the total annual production in the country. Although tomato is a signature crop in Florida, current and future ambient temperatures could impose a major production challenge, especially during the fall growing season. This problem is increasingly becoming an important concern among tomato growers in south Florida, but studies addressing these concerns have not been conducted until now. Therefore, this study was conducted to determine the impacts of the present ambient temperature conditions and planting dates on tomato productivity in south Florida. The study was conducted using crop simulation model CROPGRO-Tomato of DSSAT (Decision Support System for Agricultural Transfer) version 4.7. Five treatments were evaluated, and included AT (simulated treatment using 14 years of actual daily weather conditions at the study location) while other treatments were conducted based on a percentage (−20%, −10%, +10%, +20%) of AT to simulate cooler and warmer temperature regimes. The results suggested that under the current temperature conditions during the fall growing season in south Florida, average tomato yield was up to 29% lower compared to the cooler temperature regimes. Tomato yield further decreased by 52% to 85% at air temperatures above the current condition. Yield reduction under high temperature was primarily due to lower fruit production. Contrary to yield, both tomato biomass accumulation and leaf area index increased with increase in temperature. Results also indicated that due to changes in air temperature pattern, tomato yield increased as planting date increased from July to December. Therefore, planting date modification during the fall season from the current July–September to dates between November and December will reduce the impacts of heat stress and increase tomato productivity in south Florida.

Input Use Decisions with Greater Information on Crop Conditions: Implications for Insurance Moral Hazard and the Environment

Abstract: Emerging precision agriculture technologies allow farms to make input decisions with greater information on crop conditions. This greater information occurs by providing improved predictions of crop yields using remote sensing and crop simulation models and by allowing farms to apply inputs within the growing season when some crop conditions are already realized. We use a stylized model with uncertainty in yield and price to examine how greater information on crop conditions (i.e., a “forecast”) affects input use for insured and uninsured farms. We show that moral hazard decreases—farms apply more inputs—as the forecast accuracy improves when the forecast indicates good yields, and vice versa when the forecast indicates bad yields. In the long run, moral hazard decreases in response to an improvement in forecast accuracy. Even though moral hazard decreases in the long run, indemnity payments are likely to increase in the long run—driven by the increase in moral hazard when the forecast indicates bad crop conditions. We use the results of our model to discuss the potential impact of different technologies and types of inputs on the federal crop insurance program and the environment.

Sugar Beet Agronomic Performance Evolution in NW Spain in Future Scenarios of Climate Change

Abstract: Changes in environmental conditions resulting from Climate Change are expected to have a major impact on crops. In order to foresee adaptation measures and to minimize yield decline, it is necessary to estimate the effect of those changes on the evapotranspiration and on the associated irrigation needs of crops. In the study presented herein, future conditions extracted from RCP4.5 scenario of IPCC, particularized for Castilla-y-Leon (Spain), were used as inputs for FAO crop simulation model (AquaCrop) to estimate sugar beet agronomic performance in the medium-term (2050 and 2070). A regional analysis of future trends in terms of yield, biomass and CO2 sequestration was carried out. An annual ET0 increase of up to 200 mm was estimated in 2050 and 2070 scenarios, with ETc increases of up to 40 mm/month. At current irrigation levels, temperature rise would be accompanied by a 9% decrease in yield and a ca. 6% decrease in assimilated CO2 in the 2050 and 2070 scenarios. However, it is also shown that the implementation of adequate adaptation measures, in combination with a more efficient irrigation management, may result in up to 17% higher yields and in the storage of between 9% and 13% higher amounts of CO2.

Climate Change and Agriculture: A Review of Crop Models

Abstract: The clear evidence of climate change impact demands proactive role by scientists, agronomist and meteorologist for upscaling agricultural production, precision forecast and food safety, especially in the tropical region. The crop simulation model suggests probable growth, development and crop yield for soil-plant-atmosphere dynamics assessment. Decision Support System for Agro-Technology Transfer Model (DSSAT) is an application-based model that gives the best-suited recommendations to achieve sustainability in the agriculture by means of simulation of users’ minimum experimental data that includes weather data pertaining to site, crop growth period, and data concerning the soil, crop management practices, etc.

Model-based yield gap analysis and constraints of rainfed sorghum production in Southwest Ethiopia

Abstract: For ensuring food demand of the fast growing population in developing countries, quantification of crop yield gaps and exploring production constraints are very crucial. Sorghum is one of the most important climate change resilient crops in the rainfed farming systems of semi-arid tropics. However, there is little information about yield gaps and production constraints. This study aimed at analysing existing yield gaps and exploring major constraints of sorghum production in Southwest Ethiopia. A crop simulation model approach using AquaCrop and DSSAT was used to estimate potential yield and analyse the yield gaps. Model calibration and evaluation was performed using data from field experiments conducted in 2018 and 2019. Sorghum production constraints were assessed using a survey. The actual and water-limited yield of sorghum ranged from 0.58 to 2.51 and 3.6 to 6.47 t/ha, respectively for the period 2003–17. The regional yield gaps of sorghum for the targeted period were 3.02–3.95 t/ha with a mean value of 3.51 t/ha. Majority of respondent farmers considered seasonal rainfall risk (98%), poor soil fertility (86%), lack of improved varieties (78%) and inadequate weed management (56%) as major factors responsible for the existing yield gaps. The mean exploitable yield gap (2.5 t/ha) between water-limited and actual yield showed the level of existing opportunity for improvement in the actual productivity of sorghum. The gaps also call for introduction of proper interventions such as adoption of improved varieties, planting date adjustment, conservation tillage, fertilizer application and on time weed management.

Assessing HC27 Soil Database for Modeling Plant Production

Abstract: Soil information is a vital input for crop models applications in various large area studies including climate change impact and food security. One of the global soil databases that provide full information for crop models is HC27 of IFPRI. The quality of the database has not been assessed for crop modeling so far. A tested crop simulation model (SSM-iCrop2) was used for this purpose that needs soil water related properties (i.e., depth, albedo, curve number for runoff, drainage coefficient, and soil water limits at saturation, drained upper limit and lower limit) for the simulation of crop properties. Actual data of two soil profiles from three different climate zones (locations) were used as model inputs to simulate potential yield, evapotranspiration (under rainfed conditions) or net irrigation water requirement (under irrigated conditions) of some important plant species (alfalfa, sugar beet, sugar cane, wheat, olive, soybean, apricot and chickpea) under rainfed and irrigated conditions of Iran. Results showed that the application of HC27 soil information in the SSM-iCrop2 model resulted in model output that was not different from the model output with actual soil information with respect to mean, variance, and distribution. No statistically significant difference was found in the simulation of various combinations of soil profiles-plant species-locations. It was concluded that HC27 information can be used in simulation studies with SSM-iCrop2 or other similar simple models for the simulation of potential yield, net irrigation water, or evapotranspiration that are commonly required for food security and climate change studies.

Economic benefits of ENSO information in crop management decisions: case study of rice farming in West Java, Indonesia

Abstract: The El Nino Southern Oscillation (ENSO) strongly influences rainfall extremes in Indonesia with major impacts on droughts and floods and potential consequences for rice production. The Southern Oscillation Index (SOI) is an indicator used to detect the occurrence of ENSO events. A consistently negative (phase 1) and a rapidly falling SOI (phase 3) (indicating an El Nino cycle) were related to high probability of below-average rainfalls in the Ciparay and Bojongsoang areas of Bandung District. Therefore, the use of SOI phase information prior to the planting season would assist farmers in making optimum planting decisions. This study attempted to evaluate the economic benefits of using SOI phase information in March/April to make informed agricultural decisions for the second crop planting (April/May). The use of the SOI phases in conjunction with a crop simulation model would facilitate an objective evaluation of other cropping options. The results indicated that farmers who switched from rice to soybean or maize for the May planting season, following the March/April SOI phase I and III, earned higher incomes. The cumulative net income differences over the 63 years for soybean was about USD 1700 (27% higher at Ciparay) and USD 2350 (45% higher at Bojongsoang) and for maize was about USD 1524 (19% higher at Ciparay) and USD 1970 (35% higher at Bojongsoang).

Intercomparison and Performance of Maize Crop Models and Their Ensemble for Yield Simulations in Brazil

Abstract: Maize yield prediction is of extreme importance for both identifying those locations with high potential for this crop and determining the yield gaps of the crop where it is currently produced. The most feasible way to estimate crop yields is with the use of crop simulation models, since well calibrated and evaluated. Even though, these estimations have uncertainties once the crop models are not complete. Recent studies have shown that crop models´ uncertainties can be reduced when several models are used together, in an ensemble. Considering that, this study aimed to calibrate and evaluate three crop simulation models (AEZ-FAO; DSSAT-CERES-Maize and APSIM-Maize) to estimate maize potential and attainable yields and to assess the performance of different ensemble strategies to reduce their uncertainties for maize yield prediction. Weather, soil and maize yield data from 79 experimental sites in Brazil were used for calibrating and evaluating these models. After that, the models showed only a good performance, with mean absolute errors (MAE) between 727 and 1376 kg ha−1, R2 between 0.49 and 0.79, d index between 0.78 and 0.94, and C index from 0.54 to 0.84. When the ensemble was applied, using the combination of two models (DSSAT-CERES-Maize and APSIM-Maize), the results showed a better performance than each single model or even the average of them, with MAE = 799 kg ha−1, R2 = 0.79, d = 0.94 and C = 0.84, allowing us to conclude that the ensemble of simulated maize yields is a good strategy to reduce uncertainties on simulations.