Cultivar-specific Parameters Research Articles

Algorithm for estimating cultivar-specific parameters in crop models for newer crop cultivars

Model parameters in mechanistic process-based crop models are broadly classified into general parameters (environmental, management, and parameters related to soil processes), species-specific parameters, and cultivar-dependent parameters. While general and species-specific parameters typically remain unchanged, identifying cultivar-dependent parameters is crucial during calibration. With the continuous development of new cultivars, updating cultivar-specific parameters in crop models is necessary. However, gathering data on the behavior of these newer cultivars and calibrating the model to accommodate them is challenging. Currently, there is no standard approach for determining cultivar-specific parameters in crop models due to the difficulty of identifying variations in the growth and development process in newer cultivars, as well as the variations in the model structure, correlations between sub-model components and between different parameters in the crop models. The present study develops a standard methodology for determining cultivar-dependent parameters based on experiments and incorporating them into process-based crop growth models. The developed methodology is demonstrated using the cotton crop and the GOSSYM, a mechanistic process-based cotton crop simulation model. Experiments are conducted on 40 major cotton cultivars grown in the USA to quantify their growth and development under the same environmental and management conditions. Cultivar-dependent parameters are derived and integrated into the GOSSYM model based on the experimental data. By establishing a standardized methodology and demonstrating its application in the context of cotton and the GOSSYM model, this study provides practical guidance for incorporating cultivar-dependent parameters into crop simulation models. Researchers and agronomists can effectively utilize this methodology to integrate new cultivars into their crop models.

Effects of different observed datasets on the calibration of crop model parameters with GLUE: A case study using the CROPGRO-Soybean phenological model.

Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. Different combinations of four observed phenological stages, including initial flowering, initial pod, initial grain, and initial maturity stages for five soybean cultivars from Exp. 1 and Exp. 3 described in Table 2 are respectively used to calibrate the CSPs. The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in Table 2. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R2), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. The evaluation results suggest that RMSE and MAE decrease, and R2 and NSE increase with the increase in the number of observed phenological stages used for parameter calibration. However, there is no significant reduction in the RMSEs (MAEs, NSEs) using two, three, and four observed stages. Relatively reliable optimized CSPs for CSMP are obtained by using at least two observed phenological stages balancing calibration effect and computational cost. These findings provide new insight into parameter estimation of crop models.

Emulator-based optimization of APSIM-Sugar using the results of sensitivity analysis performed with the software GEM-SA

In most countries where sugarcane is grown, new sugarcane varieties are frequently introduced to the market; however, the existing crop models are not frequently updated. Therefore, experiments are needed to parameterize and optimize crop and cultivar-specific parameters of crop models. In this study, we used GEM-SA -generated output files of a study conducted to perform emulator-based sensitivity analysis of the APSIM-Sugar model using GEM-SA software. We compared the accuracy of simulations performed with optimized parameters using four methods: (1) optimization of 26 parameters of the APSIM-sugar model, (2) optimization of the six most sensitive parameters of the APSIM-sugar model, (3) emulator-based optimization of 26 parameters of the APSIM-sugar model, and (4) emulator-based optimization of the six most sensitive parameters. We also evaluated the computational expensiveness of these optimization methods. The results showed that the emulator-based optimization methods provided fast results compared to optimization using APSIM simulations. Moreover, the emulator-based optimization of the six selected most sensitive parameters (which took only a few minutes) provided almost similar simulation results to those obtained with all optimized parameters using APSIM optimization, which took days. Considering the accuracy and computational complexity, we propose to perform the optimization of the most sensitive parameters using an emulator-based approach. Since the sensitivity analysis results were used in this study, this optimization process could be directly coupled with the emulator-based sensitivity analysis explained by Gunarathna et al. (2019c).

Combined use of APSIM and logistic regression models to predict the quality characteristics of maize grain

Most physiology-based crop simulation models do not simulate grain quality dynamics other than protein content. In this study, a simple algorithm was adapted for predicting starch, oil, and protein content of two maize cultivars using four years of experimental data performed in northern Iran. Quality modelling was performed in two steps: (a) the APSIM-Maize model was used to dynamically simulate the phenology and growth of the whole crop and its grain protein and (b) a Three-Parameter Logistic model (3PLM) was adjusted to compute the starch and oil contents of grains. APSIM cultivar-specific parameters related to phenology and growth were selected and manually adjusted to reach satisfactory normalized root means square error (nRMSE) between simulation outputs and field collected data. Grain dry weight dynamics and starch and oil content of maize cultivars were used to calculate the temporal changes of content during grain filling. Then, the parameters of starch and oil accumulation 3PLM were adjusted to fit the experimental data. Results showed that APSIM-Maize performed well in simulating the phenological events (flowering: R2 = 0.88, nRMSE = 2.89; maturity: R2 = 0.92, nRMSE = 3.10), grain yield (R2 = 0.94, nRMSE = 9.86) and protein content (R2 = 0.50, nRMSE = 9.40). In addition, adjusted 3PLM models accurately predicted final starch and oil contents of maize cultivars with nRMSE less than 10% and R2 more than 0.90. These results suggest that the combination of APSIM with a simple three-parameter logistic model could be useful for predicting the protein, starch and oil contents of maize grains.

Global Optimization of Cultivar Trait Parameters in the Simulation of Sugarcane Phenology Using Gaussian Process Emulation

The global optimization of parameters in process-based crop models is often considered computationally expensive. Gaussian process (GP) emulation is a widely used method for reducing the computational burden of the optimization process. Total above-ground biomass and cane dry weight of three Thai sugarcane cultivars (KK3, LK92-11 and 02-2-058) collected under rainfed and irrigated conditions were used to optimize cultivar-specific parameters in the Agricultural Production Systems sIMulator (APSIM)-Sugarcane crop model through a GP emulation. GP emulators were trained and validated to approximate APSIM-Sugarcane model and then used for optimizing the cultivar-specific parameters through the differential evolution algorithm. Resulting optimized parameters allowed to obtain simulations that quite well approximated the observed biomass and CDW (validation results between simulated and observed yields: R2 0.93–0.98; normalized root mean squared error: 5–22%; Willmott’s agreement index: 0.87–0.99). The best parametrization was obtained under the lowest water stressed conditions. Based on these results, we suggest that GP emulation can be efficiently implemented for the parameterization of computationally expensive simulators.

Estimating cultivar-specific salt tolerance model parameters from multi-annual field tests for identification of salt tolerant potato cultivars

Having salt-tolerant potatoes is of paramount interest to farmers in salt affected areas, but reliable cultivar-specific parameters on salt tolerance are lacking. To address this issue existing field data on tuber yield on sandy soil at six levels of saline irrigation (0.5, 4, 8, 12, 16, 20 dS m−1) of 13 varieties for which data in two or more consecutive years were available, were analysed year-by-year with the method developed earlier. The method provides estimates of the zero-observed-effect yield (Y0), and two typical salt tolerance parameters, - i.e., a characteristic salinity level and a decline parameter –, as well as information about the uncertainties and correlations between these estimates. The results indicate that all varieties have a similar lethal soil salinity (20–24 dS m−1). However, both yield Y0 as well as salt tolerance parameters differ among cultivars, but for a single variety the estimates vary year by year, and have large uncertainties, underlining the difficulty to obtain robust parameters from single year experiments. The annual variety also hampers the discrimination between varieties. To remedy this, the data from multiple years were united in a single analysis by introducing another – unknown, annually varying - factor that is limiting the yield in the trials. Two ways to describe co-current limitations often used in models were tested. In contrast to the minimum rule, the multiplicative rule is found to provide an acceptable description of the observed yields over all years. This results in a single set of salt tolerance parameters with a narrower uncertainty bound than from single year estimation. It shows that with due account of uncertainties, field tests can be used to identify relatively salt tolerant cultivars, while accounting for between-year yield differences. Most potato cultivars have an ECe90 of about 4–5 dS m−1, but for some it is roughly double, while maintaining good yield, suggesting that these varieties are good candidates for salt adapted agriculture.

Determination of Upland Rice Cultivar Coefficient Specific Parameters for DSSAT (Version 4.7)-CERES-Rice Crop Simulation Model and Evaluation of the Crop Model under Different Temperature Treatments conditions

To develop basis for strategic or arranged decision making towards crop yield improvement in Thailand, a new method in which crop models could be used is essential. Therefore, the objective of this study was to measure cultivar specific parameters by using DSSAT (v4.7) Cropping Simulation Model (CSM) with five upland rice genotypes namely Dawk Pa-yawm, Mai Tahk, Bow Leb Nahng, Dawk Kha 50 and Dawk Kahm. Experiment was laid out in a Completely Randomized Design (CRD) with split plot design. Results showed that five upland rice genotypes had significantly affected each other by different temperature treatments (28°C, 30°C, 32°C) with grain yield, tops weight, harvest index, flowering, and maturity date. At the same time, all the phenological traits had highly significant variation with the genotypes. The cultivar specific parameters obtained by using a temperature tolerant cultivar (Basmati 385) with five upland genotypes involved in the DSSAT4.7-CSM. Model evaluation results indicated that utilizing the estimated cultivar coefficient parameters, model simulated well with varying temperature treatments as indicated by the agreement index (d-statistic) closer to unity. Hence, it was estimated that model calibration and evaluation was realistic in the limits of test cropping seasons and that CSM fitted with cultivar specific parameters can be used in simulation studies for investigation, farm managing or decision making. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.

Using UAV-Based SOPC Derived LAI and SAFY Model for Biomass and Yield Estimation of Winter Wheat

Knowledge of sub-field yield potential is critical for guiding precision farming. The recently developed simulated observation of point cloud (SOPC) method can generate high spatial resolution winter wheat effective leaf area index (SOPC-LAIe) maps from the unmanned aerial vehicle (UAV)-based point cloud data without ground-based measurements. In this study, the SOPC-LAIe maps, for the first time, were applied to the simple algorithm for yield estimation (SAFY) to generate the sub-field biomass and yield maps. First, the dry aboveground biomass (DAM) measurements were used to determine the crop cultivar-specific parameters and simulated green leaf area index (LAI) in the SAFY model. Then, the SOPC-LAIe maps were converted to green LAI using a normalization approach. Finally, the multiple SOPC-LAIe maps were applied to the SAFY model to generate the final DAM and yield maps. The root mean square error (RMSE) between the estimated and measured yield is 88 g/m2, and the relative root mean squire error (RRMSE) is 15.2%. The pixel-based DAM and yield map generated in this study revealed clearly the within-field yield variation. This framework using the UAV-based SOPC-LAIe maps and SAFY model could be a simple and low-cost alternative for final yield estimation at the sub-field scale.

Optimizing Genetic Parameters of CSM-CERES Wheat and CSM-CERES Maize for Durum Wheat, Common Wheat, and Maize in Italy

The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001–2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha−1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions.

Advancing crop modelling capabilities through cultivar-specific parameters sets for the Italian rice germplasm

Rice is one of the excellences of Italian agriculture, so that modern research techniques are mobilised to support farmers and local stakeholders in the main harvested areas in Northern Italy. Crop modelling is among the most prominent tools, as proved by the model-based services developed for yield forecasting and blast disease risk alert. Here we propose a methodology to obtain cultivar-specific parameters sets to improve the current crop modelling capabilities, using field observations collected on the main Italian rice cultivars. The definition of cultivar-specific parameters sets was based on the calibration of the crop model WOFOST_GT using long-term (2004–2015) experimental data collected in two sites (167 field trials). Our results proved a satisfactory accuracy in reproducing field observations of heading (average RRMSE = 5.9% ± 1.3%) and maturity date (22.6% ± 4.7%), canopy height (10% ± 1.4%), and yield (11.1% ± 4.1%) across cultivars and years. The analysis of the cultivar-specific parameters sets allowed highlighting the inter-relationships between model parameters, which revealed similarities with standard classifications of grain shape, phenological development and stem length. Our study is meant to deepen our modelling knowledge of the Italian rice varietal landscape, thus fostering future applications at different spatial and temporal resolutions.

Sensitivity Analysis of Plant- and Cultivar-Specific Parameters of APSIM-Sugar Model: Variation between Climates and Management Conditions

With increasing demand for food and energy, there is a great need for improving sugarcane productivity. New cultivars and management strategies can be assessed using process-based crop models. Information on cultivars needs to be updated frequently, but it is still limited in most crop models. Therefore, it is important to identify possible candidates for varietal parameterization and calibration. Because sensitivity analysis is computationally expensive, we used a less expensive emulator-based approach to conduct a global sensitivity analysis using the apsimr package and GEM-SA software. We studied the sensitivity of four yield outputs of the APSIM-Sugar model to 13 parameters in rainfed and irrigated conditions in Japan and Sri Lanka. Unlike previous studies, our aim was to give comprehensive insights into the variation in sensitivity due to variation in climate. The results confirmed distinct variation of parameter influence between climates and between management conditions. We identify possible candidates for parameterization and calibration of new cultivars for APSIM-Sugar under different environments, and show the effect of variation in climate on variation in parameter influence under different management conditions. It was confirmed that both radiation use efficiency and transpiration efficiency were sensitive and have to be examined to use new cultivars, though these are not listed as cultivar parameters.

Model parameters of four important vegetable crops for improved water use and yield estimation

High-value vegetable crops are typically grown under irrigation to reduce production risk. For water resource planning it is essential to be able to accurately estimate water use of irrigated crops under a wide range of climatic conditions. Crop water use models provide a means to make water use and yield estimates, but need crop- and even cultivar-specific parameters. There is generally a lack of crop-specific model parameters for some important commercially grown vegetable crops, especially parameters determined over both summer and winter seasons. The experimental site used in this study was on the Steenkoppies Aquifer, a catchment under stress and an important vegetable production area in South Africa. Crop-specific growth parameters and water use for 4 selected high-value vegetable crops (beetroot, cabbage, carrots and broccoli) were measured over multiple seasons (two summers and one winter). These were used to parameterise the Soil Water Balance (SWB) generic crop growth model for both summer and winter seasons. In seasons where the same cultivar was planted, a single set of model parameters could be used to successfully simulate crop growth and water use. Results show that the amount of irrigation water required is dependent on season and rainfall, with broccoli having the lowest (1.8–2.7 kg m−3) and beetroot the highest (12.2–23.4 kg m−3) water productivity (WPFM), defined as fresh mass of marketable product per unit water consumed. The root crops had a greater harvest index (HIDM) than cabbage and broccoli. The parameters obtained expand the current database of SWB crop growth parameters for vegetables and can be used in a wide range of mechanistic simulation models to improve water management at field and catchment levels.

Improving the prediction of potato productivity: APSIM-Potato model parameterization and evaluation in Tasmania, Australia

Crop growth models are required to be extensively evaluated against actual data from field grown plants in order to have confidence in their prediction of crop productivity under various management options or a future changed climate. We evaluated the ability of the APSIM-potato model to predict production, phenology, and Nuptake of potato (Solanum tuberosum L.) under Tasmanian conditions. On-farm monitoring plots were established in north-west Tasmania within four different well-managed potato fields grown during the 2012/13 cropping season. Detailed soil and crop data sets measured in the on-farm plots planted with two potato cultivars, 'Russet Burbank' and 'Moonlight' were used to parameterise and evaluate the model. The model ealistically reproduced the observed tuber yield with high precision (a mean N-RMSE of 15.4% and modelling efficiency of 1.0 for both cultivars). Measured mean tuber yield was 17 t ha-1 for 'Russet Burbank' with a simulated yield of 20 t ha-1. For 'Moonlight' simulated tuber yield was 16.0 t ha-1 compared to measured yield of 15.1 t ha-1. The simulation results provide insight on the model performance under Tasmanian conditions. The results suggest that the model has potential to be used for purposes such as simulating productivity under various management options and climate change impact studies. Additional experiments are however required to improve cultivar specific input parameters such as phenology, leaf area and leaf duration and other functions that needs further refinement to improve model ability to simulate plant organs beside the tuber

RETRACTED: Simulating peanut (Arachis hypogaea L.) growth and yield with the use of the Simple Simulation Model (SSM)

Abstract The use of crop simulation models for interpreting experiments and analyzing production systems in different management and environmental conditions is common in the literature. In this study, parameterization and evaluation of the Simple Simulation Model (SSM) for the prediction of peanut ( Arachis hypogaea L.) growth and yield was conducted for the first time. Data from different field experiments from Astaneh Ashrafieh of northern Iran were used for coefficient estimation and model evaluation for the Virginia-type peanut variety North Carolina 2 (cv. NC2). After estimation of genetic parameters, the model was tested using independent data. The SSM simulated peanut growth and yield with reasonable accuracy, using data of more than 10 field experiments from different environmental conditions (11 experiments in the parameterization stage and 15 experiments in the evaluation stage). Based on data of independent experiments that were not used for parameterization, the model predicted an acceptable percentage of the observed results concerning days to harvest maturity (r = 0.46, CV = 5%), accumulated dry matter (r = 0.66, CV = 15%), grain yield (r = 0.55, CV = 21%), and pod yield (r = 0.45, CV = 18%). Local sensitivity analysis with 23 parameters indicated that two parameters related to leaf development and a parameter related to yield formation were the most sensitive cultivar-specific parameters; thus, estimation of the parameters need to be done with care for new cultivars. The SSM provided an adequate level of peanut growth simulation and based both on its transparency and easiness-to-use can be used as a valid tool for simulating growth of peanut Virginia-type varieties.

Early sowing can improve irrigation water use efficiency and yield of common bean

ABSTRACT One of the great challenges in the use of irrigation, from an economic and environmental point of view, is to optimize the amount of water used in the production system. This study aimed at determining the viability of early sowing in the fall/winter harvest season, as well as its effects on the irrigation water use efficiency and yield of common bean. A crop model named CSM-CROPGRO-Dry bean, which simulates growth, development and yield of common bean as a function of environmental conditions, crop management and cultivar-specific parameters, was used to simulate the effects of 12 sowing dates on the irrigation water use efficiency and yield of the Pérola and BRS Radiante common bean cultivars. The simulated dates were as it follows: March 1st, 10 and 20 and April 1st, 10 and 20 (considered early); and May 1st, 10 and 20 and June 1st, 10 and 20 (considered traditional). The early sowing results in greater water economy than the traditional sowing season. Sowing in March provides a higher grain yield for the BRS Radiante cultivar and is similar to the traditional sowing of the Pérola cultivar. Concerning grain yield and irrigation water use efficiency, it is feasible to sow both cultivars in March.

Annual patterns of xylem embolism in high-yield apple cultivars.

Temperate angiosperm species show pronounced annual patterns in xylem embolism. In this study, we investigated whether high-yield cultivars of Malus domestica Borkh. growing under optimised soil water conditions follow similar patterns to wild-type plants, and evaluated crucial factors for the formation of winter embolism and the subsequent restoration of the hydraulic system in spring. In five different cultivars growing at three different sites, various hydraulic and microclimatic parameters were monitored over three successive years. In all cultivars on all sites and in all years, the percentage loss of hydraulic conductivity (PLC) increased in autumn with freeze-thaw events and accumulated over winter. Maximum values were reached in late winter and differed significantly among cultivars. In spring, the hydraulic system was restored and PLC remained negligible during summer. Embolism formation in autumn was significantly correlated with the occurrence of freeze-thaw events, whereas further conductivity losses over winter were related to winter desiccation and influenced by climatic and cultivar-specific parameters. Restoration of the hydraulic system in spring was strongly linked to a decrease in the starch content of wood and buds, and soil temperature. Despite high soil water availability, hydraulic recovery took several weeks and was not completed before bud break. Spring is thus a critical phase for temperate angiosperms, especially for high-yield cultivars with risky hydraulic strategies.

A simple approach to predict growth stages in winter wheat (Triticum aestivum L.) combining prediction of a crop model and marker based prediction of the deviation to a reference cultivar: A case study in France

Predicting wheat growth stages using ecophysiological models is of particular interest as it allows anticipating important agricultural managements. Numerous ecophysiological models exist but they need cultivar-specific parameterization, which is often costly and time consuming. The work presented here proposes a simple approach to predict wheat growth stages using the allelic composition of wheat cultivars. It relies on using the prediction of a modified version of the ARCWHEAT model for a well parameterized reference cultivar (Soissons) and the marker-based predicted deviation in days to the reference cultivar. First, the deviations to the reference cultivar Soissons for the beginning of stem elongation (δZ30) and heading date (δZ55) were calculated for a large panel of cultivars. Analysis of variance showed prominent genetic effects for δZ30 and δZ55 and possible genotype×environment interactions (G×E) for δZ30. Genotypic means δZ30 and δZ55 were used in association genetics revealing 90 and 83 genetic markers associated to these traits, respectively. Multiple linear regression models predicting δZ30 using 11 genetic markers (R2=76%) or δZ55 using 17 markers (R2=85%) were obtained by a stepwise procedure. Marker PPD-D1 had the largest impact in both models. Finally, marker-based deviations added to the prediction for the reference cultivar Soissons allowed predicting Z30 or Z55 for a large independent validation dataset. The root mean square error of prediction for Z30 and Z55 using the approach proposed in this paper (6.8 and 4.7 days, respectively) was comparable to the one obtained using the conventional approach with cultivar-specific parameters values (6.5 and 4.1, respectively). The models proposed in this paper appeared sufficient in order to predict growth stages of cultivars which cannot be parameterized such as new cultivars coming out on the market. Moreover, genetic markers involved in the multiple linear regression models predicting δZ30 and δZ55 may provide interesting candidates to unravel new genes determining earliness in winter wheat.

Modeling Differential Growth in Switchgrass Cultivars Across the Central and Southern Great Plains

Switchgrass (Panicum virgatum L.) has been recognized as a potential biofuel crop, because it is adapted to a wide range of environmental and climatic conditions. Zones of adaptation for many switchgrass cultivars are well documented and attributed to local adaptation to the temperature and photoperiod at the location of origin. The objective of this study is to develop cultivar-specific growth parameters for the Agricultural Land Management and Numerical Assessment Criteria (ALMANAC) model based on location of origin and use these parameters to predict the biomass production of two lowland cultivars (Alamo and Kanlow) and two upland cultivars (Blackwell and Cave-in-Rock) in the central and southern Great Plains (TX, AR, LA, OK, KS, and MO). The plant parameters adjusted for each cultivar’s origin include average growing season temperature (22–27 °C), photoperiod at growth onset (11.46–13.12 h), maximum number of heat units (1,500–2,300), maximum leaf area index (6–12), and light extinction coefficient (0.33–0.50). The absolute difference between the average simulated and measured yields across all seven field locations for each cultivar is less than 0.5 Mg ha−1. Performance of the cultivar-specific parameters varies by location, but the parameters do a reasonable job of estimating the average yield (less than 15 % difference) of each cultivar for a majority of field locations. In addition, regional simulations of the four cultivars each show realistic spatial variation in yield across the central and southern Great Plains. The parameters derived in this project for the ALMANAC model provide a tool for optimizing choice of switchgrass cultivar on different soils, in different climates, and with different management across large geographic regions.

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.

Using farm accountancy data to calibrate a crop model for climate impact studies

Process-based crop models are widely used in decision support systems or to assess impacts of climate change on agriculture at different spatial scales. They include crop and/or cultivar-specific parameters that need to be calibrated. However, the availability of reference data is often limited. An alternative is to use yield records from widely available Farm Accountancy Data Network (FADN). The goal of this study was therefore to propose and test a crop model calibration procedure that makes use of FADN data. To account for the lack of management information in the FADN databases, the concept of ‘Meteorologically Possible Yield’ (MPY) was adopted. This concept is particularly relevant in the context of climate impact studies as MPYs are by definition only driven by climate variability. As an example, the procedure was applied to calibrate the generic crop model CropSyst for a region located in north-eastern Switzerland. Validation using data from a long-term field trial with detailed information on fertilizer applications showed that the proposed procedure provides robust simulation results and is therefore suitable for climate impact studies in regions where detailed experimental data are scarce. In a case study application, the transferability of the local calibration to a site with drier conditions was tested and simulation results for this new site were compared to results obtained using local recalibration. Results showed that predicted yields can differ substantially and the differences can be strongly amplified when impacts of climate change are considered. This highlights the need for adjusting model calibrations to local site conditions and for considering parameter uncertainties in climate impact studies.