Dynamic Crop Models Research Articles

Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa.

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.

Development of a crop growth model for the energy analysis of controlled agriculture environment spaces

In controlled environment agriculture spaces, the conditions fluctuate between photoperiod and dark period, with crops growing continuously. As crops grow, their impact on the energy demand and energy use, often estimated using a building performance simulation tool, becomes more prominent. In this paper, a dynamic crop model integrated into a building performance simulation tool is proposed to estimate the yield and heat gain/loss from crops by combining a growth model and an energy balance model of the crops. The developed growth model is an adjusted version of a greenhouse lettuce growth model modified for high-density controlled environment agriculture applications by calibrating the sensitive parameters for several indoor environment conditions (temperature, lighting, etc.) using an experimental growth dataset. The yield, the energy demand and the energy use were assessed for a case study modelled in TRNSYS. The results obtained using the greenhouse and developed growth models were compared to those generated with the experimental growth dataset. Depending on the indoor environment conditions, the difference in specific energy use estimated using the experimental growth dataset and the developed model varied between 0.1% and 3.5%, indicating that the model led to an acceptable level of accuracy. The dynamic crop model estimates yield and heat gain/loss from crops for various indoor environment conditions, which are essential for carrying out energy, financial, and environmental analyses.

Variance-based global sensitivity analysis and uncertainty quantification of the AquaCrop model parameters for basil (Ocimum basilicum L.) under different nitrogen fertilizer rates

Dynamic crop models are powerful tools for evaluating crop responses to alternative management options. These models consist of various input parameters that need calibration before being implemented in a new environment. Sensitivity analysis (SA) has proven to be an efficient method for identifying dominant model parameters. This study aimed to determine influential parameters of the AquaCrop model for basil and also quantify output uncertainty under different nitrogen fertilizer rates. The Sobol’ SA method was employed to compute both first-order and total-order sensitivity indices. Results indicated that normalized water productivity (WP*) and crop coefficient (KcTr,x) were the most influential parameters across all treatments. Furthermore, as fertility stress increased, the interaction effects among parameters decreased. The temporal analysis of model output (biomass) revealed that parameter sensitivities were highly time-dependent. In addition, the uncertainty of predicted biomass was determined using combined violin and box plots. Results demonstrated that the optimal nitrogen fertilizer application rate extended the distribution of model output. In summary, these findings offer valuable insights for model simplification and effective decision-making.

20% of uncertainty in yield estimates could be caused by the radiation source.

Solar radiation is the energy for all biological, physical, and chemical processes of the earth's surface system, and affects the growth and development of crops at all stages. But the diverse data sources and fusion algorithms lead to large differences in the radiation values in various climate datasets. Accurate estimates of the radiation data is not an easy task, the uncertainty of which and the impact on crop yield simulation remains unknown. In this study, the total solar radiation amounts from four independent global radiation datasets were shown considerable heterogeneity across regions and cropping seasons. Forcing the dynamic crop models with the four radiation inputs produced similarly great uncertainties of simulated yield in most regions, with the greatest uncertainty up to 30% of average yield for wheat in Europe. The global-scale uncertainty of simulated yield is increasing during the past three decades and would reach up to 20% of its averages in the future, equivalent to 300 million tons when converting to the global crop production. The results of this study suggest that the previously projected crop yield changes with climate change have large uncertainties propagated from solar radiation data sources used for projections. These uncertainties may mislead the assessment of future food security.

Coupling localized Noah-MP-Crop model with the WRF model improved dynamic crop growth simulation across Northeast China

Northeast China is an important commodity grain base in China and plays an important role in maintaining national and local food security. The intensifying climate variability and strengthening of extreme weather events threaten crop growth and yields in this region. The dynamic crop model coupled into the land surface models showed advances in simulating crop phenology and crop growth response to climate change, however, which was mainly validated for the United States. By comparing with the satellite observed crop growth cycles, we firstly calibrated the parameters for the Weather Research and Forecasting (WRF) coupled Noah-MP-Crop model. Then, we evaluated the performance of the calibrated WRF-Noah-MP-Crop model with the default crop models and the dynamic vegetation models coupled with WRF by comparing our simulations with corresponding satellite measurements and meteorological observations. Our results indicated the augmentation of the Noah-MP-Crop model in WRF significantly improved the crop phenology simulation at the beginning and ending time of the growing season compared with the dynamic vegetation model using unmanaged crops. The satellite-calibrated crop parameters substantially improved the simulation of crop growth, plant physiology, and biomass accumulation for both corn and soybean. Coupling the localized dynamic crop model into the WRF led to considerable decreases in the simulated mean-absolute-errors (MAEs) and biases of the leaf area index, evapotranspiration, and gross primary production compared with the MODIS observed values. Compared with the statistical yield from each province, the modified crop model underestimated the corn yield from 11.1% to 48.6%, whereas overestimated the soybean yield from 16.5% to 162.6%. The coupled WRF-Noah-MP-Crop model with flexible resolutions holds unmatched advantages in estimating and projecting crop growth and yield under future climate change scenarios, which would be critical in ensuring sustainable agricultural development and maintaining future food security.

Expansion and evaluation of two coupled root–shoot models in simulating CO2 and H2O fluxes and growth of maize

AbstractLeaf water pressure head (Ψleaf) and more specifically its critical thresholds (Ψthreshold) characterize stomatal control of transpiration, particularly for C4 plants, but this physiological process has rarely been integrated into dynamic crop models at the field scale. We further extended two coupled models with Feddes root water uptake (RWU) and Couvreur RWU models by adding the C4 photosynthesis model in order to be applicable to a maize (Zea mays L.) crop growing under field conditions. The Feddes RWU model relates RWU and plant stress directly to the root zone water hydraulic potential, whereas the Couvreur model explicitly represents hydraulic signals between soil and stomata. Model performance was evaluated with a comprehensive dataset including stomatal conductance, Ψleaf, sap flow, gross assimilation rate (GAR), soil water content (SWC), dry biomass, and leaf area index (LAI) from a two‐season maize experiment under contrasting environments and water regimes. For the Couvreur model, the RWU and dry biomass were more sensitive to the root hydraulic conductance parameters than to Ψthreshold and root growth parameters. The agreement index (I) for the Couvreur model after calibration was 0.91, 0.80, 0.81, and 0.69 for biomass, LAI, RWU, and GAR, respectively. The Feddes model performed similarly for the same metrics. The Feddes model simulated accurately the plant water stress in the first 45 d of the growing season, whereas the Couvreur model inaccurately predicted water stress, which resulted in lower agreement with observations (i.e., RMSEs of biomass simulated by Feddes and Couvreur model averaged from four validated plots were 0.171 and 0.214 kg m–2, respectively). The Feddes model showed the potential to be used for maize under water stress whereas the Couvreur model needs to be further evaluated and improved with an adequate estimation of root hydraulic conductance. A dynamic parameterization of normalized root system conductance and/or more accurate assimilate allocation to the roots, especially under drought stress, should be considered in order to apply this model in future studies to maize.

Review on Plant Physiology and Crop Modeling for the Response of Rice Crop to Climate Change

Climate change impacts the sustainability of agriculture. Physiological processes are very essential for improving crop modeling. The objective of this paper was to assess the integrated effects of crop modeling and crop physiology on the response of rice crop to climate change. Some models have weakness and this weakness rectified by better understanding of physiological features which is correlated to the growth and development of the plant. Process-based dynamic crop models are able to estimate a range of crop response to the environment and to assess the biophysical effects of future climate scenarios on growth and yield. Crop modelling has the potential to enable society to assess the efficacy of G × E technologies to mitigate and adapt crop production systems to climate change. An advanced integration of crop modeling and crop physiology will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales. High night temperature increases carbon loss from night respiration and reduces the activity of source–sink enzymes resulting in lower biomass, poor grain filling, and reduced grain weight of rice. ORYZA and CERES-Rice models are the most familiar physiological based rice models currently used for rice crop modeling studies. Interaction between plant physiology and modeling is essential to improving the existing models, for creating new models, and for improving predictions on crop responses to climate changes and variability. Keywords: Crop modeling, Physiology, climate change, crop response, Rice DOI: 10.7176/JNSR/13-4-01 Publication date: February 28 th 2022

Weather records from recent years performed better than analogue years when merging with real-time weather measurements for dynamic within-season predictions of rainfed maize yield

Within-season crop yield prediction with a dynamic crop model can provide valuable references for field management practices and regional food security. However, weather ensembles containing the unknown future weather conditions occurring after prediction dates are essential for such predictions using crop models. Two strategies were established for selecting analogue weather years as the target growing season based on a five-year maize experiment conducted at eight sites in the Loess Plateau of China. The first strategy tried weather data from different lengths of years ahead the planting year. The second strategy used the k-nearest neighbor (k-NN) algorithm to select analogue weather according to different combinations of weather variables with daily or accumulative values. The results showed that satisfactory predictions could be obtained after maize tasseling (about 50 d prior to maturity). The mean absolute relative error (ARE) and coefficient of variation (CV) of the daily yield predictions after tasseling were 6.6% and 5.7%, respectively, in 2010 at the Yulin site. In the leading-year strategy, the most reliable predictions were obtained by the weather data from the 10 years ahead of planting, with an overall average ARE of 11.7%. In the k-NN strategy, the most reliable predictions were obtained by using the analogue weather selected with only accumulative precipitation, with an overall average ARE of 11.5%. Additionally, both of the two optimal strategies improved the original predictions in most cases. However, the k-NN strategy was more likely to generate worse predictions in the early part of the growing season. Generally, it was more convenient to use the weather data of 10 leading years before the planting year to represent the unknown weather data after the prediction dates. This strategy provided reliable prediction accuracy without complex programming and requirement for long-term weather records.

Simulating the impact of crop–livestock interaction on crop performance using DSSAT

AbstractAn integrated crop–livestock system (ICL) can help with enhancing soil quality and crop productivity, ultimately increasing farm income. Field experiments that evaluated the impact of cover crops (CCs) and grazing on crop productivity have shown mixed results primarily because of a relatively short duration of the crop–livestock system. Dynamic crop models can help to simulate the long‐term impact of soils and crop management on crop yield. The objectives of the current study were to develop a simple simulation methodology for crop–livestock interaction using the Decision Support System for Agrotechnology Transfer (DSSAT) and to evaluate the performance of CC grazing on maize (Zea mays L.) production. Here, differences in pre‐ and postgrazing dry matter were used to determine the daily biomass consumption by livestock, and the in vitro dry matter digestibility (IVDMD) of the CCs was used to determine the amount of manure that is being returned to the soil during the grazing period. The data generated from the field experiments were used to calibrate and evaluate the cropping system model (CSM)‐CERES‐Maize of DSSAT. The index of agreement (d) for yield was 0.99 and 0.95, whereas relative root mean squared error (RRMSE) was 4.72 and 0.3% of the observed yield for calibration and evaluation, respectively. The trends in the field data were well represented by the simulated data. The current methodology provides evidence that crop modeling can be used to study ICLs, especially the crop–livestock interactions, by scientists and policy makers to help develop best management practices to increase yield and improve soil health.

Using ENSO conditions to optimize rice yield for Nepal’s Terai

The direct application of forecasts from seasonal prediction systems (SPSs) in agriculture is limited by their skill, and SPSs are more skilled at El Niño-Southern Oscillation (ENSO) prediction than precipitation prediction. An alternative to the direct application of forecasts from SPSs could be to link the forecast of ENSO conditions with dynamic crop models to evaluate alternate crop management options prior to the start of the actual planting. Although potential benefits of this approach have been tested in many areas of the world, so far limited evidence exists regarding its application in Nepal’s Terai region. The overall goal of this study was to determine the potential relationship between ENSO and summer monsoon precipitation over Nepal’s Terai and ascertain SPSs’ skill in predicting ENSO. This analysis included disentangling the relative contribution of precipitation to interannual variability in rice yield from other factors using a cropping system model, namely, the Crop Environment Resource Synthesis-Rice (CSM-CERES-Rice). The crop model was also employed to explore options for increasing rice yield and minimizing risk by adjusting crop management. This study found that precipitation was the main variable affecting interannual variability in rice yield, that SPSs are good at predicting ENSO, and that the ENSO signal can be used to predict seasonal precipitation anomalies in the study area in all years except ENSO neutral years. Prior knowledge of seasonal precipitation anomalies can then be used to optimize rice yield using a crop model, and ultimately to assist farmers with decision making.

Limiting transpiration rate in high evaporative demand conditions to improve Australian wheat productivity

Abstract Limited-transpiration rate at high evaporative demand (‘LTR’ trait) has potential to improve drought adaptation, crop water productivity and food security. The quantification of the implications of LTR for water consumption, biomass accumulation and yield formation requires the use of dynamic crop modelling to simulate physiological and environmental processes and interactions in target environments. Here, a new transpiration module was developed for the Agricultural Production Systems sIMulator (APSIM NextGen) and used to simulate atmospheric and edaphic water stress on wheat crops. This module was parameterized with (i) data from a lysimeter experiment assessing genotypic variability in the LTR trait for four genotypes contrasting in transpiration efficiency, and with (ii) a more pronounced response to high evaporative demand. The potential of the LTR trait for improving crop productivity was investigated across the Australian wheatbelt over 1989–2018. The LTR trait was simulated to allow an increase in national yield by up to 2.6 %, mostly due to shift in water use pattern, alleviation of water deficit during grain filling period and a higher harvest index. Greatest productivity gains were found in the north-east (4.9 %, on average) where heavy soils allow the conserved water with the LTR trait to be available later at more critical stages. The effect of the LTR trait on yield was enhanced under the future climate scenario, particularly in the north-east. Limiting transpiration at high evaporative demands appears to be a promising trait for selection by breeders, especially in drought-prone environments where crops heavily rely on stored soil moisture.

Incorporating a dynamic gene-based process module into a crop simulation model

AbstractDynamic crop simulation models are tools that predict plant phenotype grown in specific environments for genotypes using genotype-specific parameters (GSPs), often referred to as ‘genetic coefficients’. These GSPs are estimated using phenotypic observations and may not represent ‘true’ genetic information. Instead, estimating GSPs requires experiments to measure phenotypic responses when new cultivars are released. The goal of this study was to evaluate a new approach that incorporates a dynamic gene-based module for simulating time-to-flowering for common bean (Phaseolus vulgaris L.) into an existing dynamic crop model. A multi-environment study that included 187 recombinant inbred lines (RILs) from a bi-parental bean family was conducted in 2011 and 2012 to measure the effects of quantitative trait loci (QTLs), environment (E) and QTL × E interactions across five sites. A dynamic mixed linear model was modified in this study to create a dynamic module that was then integrated into the Cropping System Model (CSM)-CROPGRO-Drybean model. This new hybrid crop model, with the gene-based flowering module replacing the original flowering component, requires allelic make-up of each genotype that is simulated and daily E data. The hybrid model was compared to the original CSM model using the same E data and previously estimated GSPs to simulate time-to-flower. The integrated gene-based module simulated days of first flower agreed closely with observed values (root mean square error of 2.73 days and model efficiency of 0.90) across the five locations and 187 genotypes. The hybrid model with its gene-based module also described most of the G, E and G × E effects on time-to-flower and was able to predict final yield and other outputs simulated by the original CSM. These results provide the first evidence that dynamic crop simulation models can be transformed into gene-based models by replacing an existing process module with a gene-based module for simulating the same process.

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

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.

Olive tree irrigation as a climate change adaptation measure in Alentejo, Portugal

Climate change projections for Southern Europe reveal warming and drying trends for the upcoming decades, bringing important challenges to Portuguese olive orchards in particular. We analyzed irrigation as an adaptation measure to ensure the future sustainability of olive tree yields in Alentejo, the main olive producing area in Portugal. A dynamic crop model was used to simulate olive tree yields over the baseline (1981–2005) and two future scenarios (RCP4.5 and RCP8.5, 2021–2080), using a 4 member-ensemble of state-of-the-art climate model chains. Climate change projections point to an increase in mean temperature (of up to 2 °C by 2080) and potential evapotranspiration (40−50 mm), while a decrease in precipitation (-80 to −90 mm) and actual evapotranspiration (-50 to −70 mm), under both future scenarios. Future yield decreases 15–20% (for RCP4.5 and RCP8.5) and accumulated losses can reach -8 t/ha to −10 t/ha by 2080. This decrease is due to enhanced heat and water stress under future climate conditions. As an adaptation measure, irrigation was simulated, but only applied at a certain water stress level. The results indicate higher yields due to this adaptation strategy, in range with the present values (±1%), thus alleviating the projected yield decreases in the future. The amount of water required for irrigation ranges from 60 to 85 mm, depending on the RCP, which corresponds to 0.6–1 times the projected decrease in precipitation. However, this value can reach up to 2 times for one climate model chain. We conclude that while irrigation is a feasible adaptation measure against the threats of climate change in Alentejo olive orchards, this strategy may be threatened by the scarcity of water resources. Outlining appropriate, timely and cost-effective adaptation measures is critical for the sustainability of both the environment and the Alentejo olive sector.

Remote Sensing Data Assimilation in Dynamic Crop Models Using Particle Swarm Optimization

A growing world population, increasing prosperity in emerging countries, and shifts in energy and food demands necessitate a continuous increase in global agricultural production. Simultaneously, risks of extreme weather events and a slowing productivity growth in recent years has caused concerns about meeting the demands in the future. Crop monitoring and timely yield predictions are an important tool to mitigate risk and ensure food security. A common approach is to combine the temporal simulation of dynamic crop models with a geospatial component by assimilating remote sensing data. To ensure reliable assimilation, handling of uncertainties in both models and the assimilated input data is crucial. Here, we present a new approach for data assimilation using particle swarm optimization (PSO) in combination with statistical distance metrics that allow for flexible handling of model and input uncertainties. We explored the potential of the newly proposed method in a case study by assimilating canopy cover (CC) information, obtained from Sentinel-2 data, into the AquaCrop-OS model to improve winter wheat yield estimation on the pixel- and field-level and compared the performance with two other methods (simple updating and extended Kalman filter). Our results indicate that the performance of the new method is superior to simple updating and similar or better than the extended Kalman filter updating. Furthermore, it was particularly successful in reducing bias in yield estimation.

Climate change projections for olive yields in the Mediterranean Basin

AbstractThe olive tree is one of the most important crops in the Mediterranean basin. Given the strong climatic influence on olive trees, it becomes imperative to assess climate change impacts on this crop. Herein, these impacts were innovatively assessed, based on an ensemble of state‐of‐the‐art climate models, future scenarios and dynamic crop models. The recent‐past (1989–2005) and future (2041–2070, RCP4.5 and RCP8.5) olive growing season length (GSL), yield, growing season temperature (GST) and precipitation (GSP), potential (ETP) and actual (ETA) evapotranspiration, water demand (WD) and water productivity (WP) were assessed over Southern Europe. Crop models were fed with an ensemble of EURO‐CORDEX regional climate model data, along with soil and terrain data. For the recent‐past, important differences between western and eastern olive growing areas are found. GSL presents a strong latitudinal gradient, with higher/lower values at lower/higher latitudes. Yields are lower in inner south Iberia and higher in Italy and Greece, which is corroborated by historical data. Southern Iberia shows higher GST and lower GSP, which contributes to a higher ETP, lower ETA and, consequently, stronger WD. Regarding WP, the recent‐past values show similar ranges across Europe. Future projections point to a general increase in GSL along with an increase in GST up to 3°C. GSP is projected to decrease in Western Europe, leading to enhanced WD and consequently a yield decrease (down to −45%). Over eastern European, GSP is projected to slightly increase, leading to lower WD and to a small yield increase (up to +15%). WP will remain mostly unchanged. We conclude that climate change may negatively impact the viability of olive orchards in southern Iberia and some parts of Italy. Thus, adequate and timely planning of suitable adaptation measures are needed to ensure the sustainability of the olive sector.

New modelling technique for improving crop model performance - Application to the GLAM model

Crop models simulate growth and development and they are often used for climate change applications. However, they have a variable skill in the simulation of crop responses to extreme climatic events. Here, we present a new dynamic crop modelling method for simulating the impact of abiotic stresses. The Simultaneous Equation Modelling for Annual Crops (SEMAC) uses simultaneous solution of the model equations to ensure internal model consistency within daily time steps; something that is not always guaranteed in the usual sequential method. The SEMAC approach is implemented in GLAM, resulting in a new model version (GLAM-Parti). The new model shows a clear improvement in skill under water stress conditions and it successfully simulates the acceleration of leaf senescence in response to drought. We conclude that SEMAC is a promising crop modelling technique that might be applied to a range of models.

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.

Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Soil-crop models are used to simulate ecological processes from the field to the regional scale. Main inputs are soil and climate data in order to simulate model response variables such as crop yield. We investigate the effect of changing the resolution of input data on simulated crop yields at a regional scale using up to ten dynamic crop models. For these models we compared the effects of spatial input data aggregation for wheat and maize yield of two regions with contrasting climate conditions (1) Tuscany (Italy, Mediterranean climate) and (2) North Rhine Westphalia (NRW, Germany, temperate climate). Soil and climate data of 1 km resolution were aggregated to resolutions of 10, 25, 50, and 100 km by selecting the dominant soil class (and corresponding soil properties) and by arithmetic averaging, respectively. Differences in yield simulated at coarser resolutions from the yields simulated at 1 km resolution were calculated to quantify the effect of the aggregation of the input data (soil and climate data) on simulation results. The mean yield difference (bias) at the regional level was positive due to the upscaling of productive dominant soil(s) to coarser resolution. In both regions and for both crops, aggregation effects (i.e. errors in simulation of crop yields at coarser spatial resolution) due to the combined aggregation of soil and climate input data increased with decreasing resolution, whereby the aggregation error for Tuscany was larger than for North Rhine Westphalia (NRW). The average absolute percentage yield differences between grid cell yields at the coarsest resolution (100 km) compared to the finest resolution (1 km) were by about 20–30% for Tuscany and less than 15 and 20% for NRW for winter wheat and silage maize, respectively. In the Mediterranean area, the prediction errors of the simulated yields could reach up to 60% when looking at individual crop model simulations. Additionally, aggregating soil data caused larger aggregation errors in both regions than aggregating climate data. Those results suggest that a higher spatial resolution of climate and especially of soil input data are necessary in Mediterranean areas than in temperate humid regions of central Europe in order to predict reliable regional yield estimations with crop models. For generalization of these outcomes, further investigations in other sub-humid or semi-arid regions will be necessary.

Using daily data from seasonal forecasts in dynamic crop models for yield prediction: A case study for rice in Nepal’s Terai

Skillful seasonal climate predictions paired with a dynamic crop model can assist agricultural management and help farmers minimize risk. For crop yield predictions, the skill in generating realistic distributions of weather for the crop growing season matters more than the skill of forecasting the mean seasonal climate itself. In this regard, the ensemble of daily fields of the Seasonal Prediction Systems (SPSs) output could be a potential alternative to other methods that are available to generate daily weather from the monthly or seasonal mean forecasts. However, the SPSs are not expected to forecast individual weather events at a given grid point (deterministic forecast), but if the statistics of the predicted weather are correct, an ensemble of yield predictions using individual realizations of the ensemble seasonal forecast may produce a more skillful yield forecast. So far, the potential of this new approach has not been tested. The goal of this study was to determine the potential applicability of using daily data from SPSs to predict rice yield through a case study of Nepal’s Terai. The study used 28 years (1983–2010) daily hindcasts of the coupled forecast system model version 2 (CFSv2) SPS into a Cropping System Model (CSM)-CERES-Rice. The hindcasts of the CFSv2, initialized at different lead times, were used in various ways to simulate rice yield, which were then compared to the reference yield and to the simulated yield using climatology alone to examine the predictive skill at different lead times. The results from this study indicate that unlike the typical ensemble averaging approach commonly used in seasonal climate forecasting, averaging the simulated yield using individual member does not guarantee better yield prediction. Further analyses should be made, including alternative downscaling methods as well as a similar analysis for an area where quality meteorological and agricultural data are available and where the seasonal forecasts exhibit better skill.