Performance and sensitivity of the DSSAT crop growth model in simulating maize yield under conservation agriculture

With the practice of conservation agriculture (CA) soil water and nutrient dynamics are modified by the presence of a mulch of crop residues and by reduced or no-tillage. These alterations may have impacts on crop yields. The crop growth model DSSAT (Decision Support Systems for Agrotechnology Transfer) has recently been modified and used to simulate these impacts on crop growth and yield. In this study, we applied DSSAT to a long-term experiment with maize (Zea mays L.) grown under contrasting tillage and residue management practices in Monze, Southern Province of Zambia. The aim was (1) to assess the capability of DSSAT in simulating crop responses to mulching and no-tillage, and (2) to understand the sensitivity of DSSAT model output to input parameters, with special attention to the determinants of the model response to the practice of CA. The model was first parameterized and calibrated for the tillage treatment (CP) of the experiment, and then run for the CA treatment by removing tillage and applying a mulch of crop residues in the model. In order to reproduce observed maize yields under the CP versus CA treatment, optimal root development in the model was restricted to the upper 22 cm soil layer in the CP treatment, while roots could optimally develop to 100 cm depth under CA. The normalized RMSE values between observed and simulated maize phenology and total above ground biomass and grain yield indicated that the CA treatment was equally well simulated as the CP treatment, for which the model was calibrated. A global sensitivity analysis using co-inertia analysis was performed to describe the DSSAT model response to 32 model input parameters and crop management factors. Phenological cultivar parameters were the most influential model parameters. This analysis also demonstrated that in DSSAT mulching primarily affects the surface soil organic carbon content and secondly the total soil moisture content, since it is negatively correlated with simulated soil water evaporation and run-off. The correlations between the input parameters or crop management factors and the output variables were stable over a wide range of seasonal rainfall conditions. A local sensitivity analysis of simulated maize yield to three key parameters for the simulation of the CA practice revealed that DSSAT responds to mulching particularly when rooting depth is restricted, i.e., when water is a critical limiting crop growth factor. The results of this study demonstrate that DSSAT can be used to simulate crop responses to CA, in particular through simulated mulching effects on the soil water balance, but other, often site-specific, factors that are not modeled by DSSAT, such as plough pan formation under CP or improved soil structure under CA, may need to be considered in the model parameterization to reproduce the observed crop yield effects of CA versus CP.     

Combining input uncertainty and residual error in crop model predictions: A case study on vineyards

As crop modelling has matured and been proposed as a tool for many practical applications, there is increased need to evaluate the uncertainty in model predictions. A particular case of interest that has not been treated before is that where one takes into account both uncertainty in the model explanatory variables and model residual error (the uncertainty in model predictions even when the explanatory variables are perfectly known). The specific case we consider is that of a model for predicting water stress of a vineyard. For many of the model explanatory variables, the vine grower (or the farmer advisor) has a choice between approximate values which are easily obtainable and more precise values that are more difficult (and more expensive) to obtain. We specifically discuss the explanatory variable “initial water stress” which is directly based on the initial soil water content and can be estimated or measured (precise but expensive). The vine grower is interested in the decrease in uncertainty that would result from measuring initial water stress, but it is the decrease in total uncertainty, including model residual error, that is of importance.We propose using accurate measurements of water stress over time in multiple vineyards, to estimate model residual error. The uncertainty in initial water stress can be estimated if one has approximate and precise values of initial water stress in several vineyards. We then combine the two sources of error by simulation thanks to an independence hypothesis; the model is run multiple times with a distribution of values for initial water stress, and on each day a distribution of model residual errors is added to the result.The results show that the resulting uncertainty is quite different in different fields. In some cases, uncertainty in initial water stress becomes negligible a short time after the start of simulations, in other cases that uncertainty remains important, compared to model residual error, throughout the growing season. In all cases, residual error is a substantial percentage of overall error and thus should be taken into account.     

Implications of climate model biases and downscaling on crop model simulated climate change impacts

In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate sources of uncertainty. This paper considers the specific aspect of input weather data quality from a Regional Climate Model (RCM) leading to differences in estimates made by three crop models. The availability of hindcast RCM estimates enables comparison of crop model outputs derived from observed and modelled weather data. Errors in estimating the past climate implies biases in future projections, and thus affect modelled crop responses. We investigate the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed (12 UK sites), original (50 × 50 km) and bias corrected downscaled (site-specific) hindcast (1960–1990) weather data from the HadRM3 RCM. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop model estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite evaluated (observed-hindcast) differences. This is likely due to compensating errors in the input weather data and non-linearity in the crop models processes, making interpretation of results problematic. Understanding how biases in climate data manifest themselves in individual crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections and crop model ensembles. The results have implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles.     

In-field development of a conceptual crop functioning and management model: A case study on cotton in southern Mali

West African cotton production has increased rapidly in recent years. Cotton is being cropped under new ecological conditions by new cotton-producing farmers, but the cropping techniques recommended by developers have essentially remained the same. Methodologies are needed to generate a broad scope of recommendations on cropping techniques to deal with the increasing diversity concerning farmers and cropping conditions.

A conceptual model of a cotton field was developed that approaches a crop field as a biophysical system under the influence of a “technical system” (i.e. the combination of farmers’ practices implemented in the field). The system outputs were restricted to yield and the main yield components. A theoretical model was first designed on the basis of published data and expert knowledge on cotton physiology, local soil–climate conditions and farmers’ practices. It was based on five specific hypotheses on links between technical and biophysical systems. The hypotheses were tested in a local farmers’ network. Thirty “cropping situations” (soil–crop–technique combinations) were selected in farmers’ fields around Katogo village (Mali), a village that had been previously selected for a cotton crop management prototyping program. Homogeneous groups of situations were drawn up on the basis of the dynamics of crop aerial biomass accumulation. They were compared for their management and environment features. The initial conceptual model was then simplified, while taking the measured variability in its components and the sensitivity of the outputs to these components into account. This conceptual model is being evaluated in other villages, where we have partnerships with farmers, in order to develop a version adapted to a broad range of situations.     

Run-time calibration of simulation models by integrating remote sensing estimates of leaf area index and canopy nitrogen

Dynamic simulations models may enable for farmers the evaluation of crop and soil management strategies, or may trigger crop and soil management strategies if they are used as warning systems, e.g. for drought risks and for nutrients shortage. Predictions by simulation models may differ from field observations for a variety of reasons, and such deviations can be revealed instantly by traditional or by new field monitoring techniques. The objective of this study was to improve simulation results by integrating remote sensing observations during the growing season in the simulation (i.e. run-time calibration). The Rotask 1.0 simulation model was used as it simulates daily interactions between climate (radiation temperature, vapour pressure, wind speed, precipitation), soils (water holding capacities, soil organic matter dynamics, evaporation) and crops (light interception, dry matter production, nitrogen uptake, transpiration). Various run-time calibration scenarios for replacing simulated values by remotely observed values were tested. For a number of times in the growing season, simulated values of leaf area index (LAI) and canopy nitrogen contents were replaced with values estimated from remote sensing. Field experiments were carried out in the Netherlands in 1997 (validation) and 1998 (calibration) with potato variety Bintje. Destructive field samplings were performed to follow LAI and canopy nitrogen development in the growing season. Remote sensing observations at canopy level were taken by CropScan™ equipment, covering the electromagnetic spectrum between 460–810 nm in eight spectral bands. LAI and canopy nitrogen were monitored at various moments throughout the growing season by relating them with vegetation indices (VI) that were calculated from the combination of specific remote sensing bands. The results of this study show that run-time calibration of mechanistic simulation models may enhance simulation accuracy, depending on the method how additional information is integrated. It is advised to synchronize dry matter balances and internal nitrogen balances in accordance with adjustments to observed calibration variables (in this case LAI and canopy nitrogen content). It is shown an integrated approach follows the actual crop–soil system more closely, which is helpful for specific crop management and precision agriculture in general. Run-time calibration with variables that can be estimated from remote sensing observations gives more accurate simulation results of variables that can not be observed directly, e.g. the evolution of soil inorganic nitrogen contents. High frequencies of remote sensing observations and interpolation in between them, allow reconstructing the evolution of LAI and canopy nitrogen contents to be integrated in the simulation, thereby increasing simulation accuracy of other model variables.     

Use of crop models to understand genotype by environment interactions for drought in real-world and simulated plant breeding trials

Crop simulation models of plant processes capture the biological interactions between the sensing of signals at an organ level (e.g. drought affecting roots), the response of the plant at a biochemical level (e.g. change in development rate) and the result at the organ (or crop) level (e.g. reduced growth). In dissecting the complex control of phenotypes like yield, simulation models have several roles. Models have been used to generate an index of the climatic environment (e.g. of drought stress) for breeding programme trials. In wheat and sorghum grown in northern Australia, this has shown that mid-season drought generates large genotype by environment interaction. By defining gene action to calculate the value of input trait parameters to crop models, simulated multi-environment trials estimate the yield of ‘synthetic’ sorghum cultivars grown in historical or artificial climates with current or potential management regimes. In this way, the biological interactions among traits constrain the crop yields to only those that are biologically possible in the given set of environments. This allows the construction of datasets that are more ‘realistic’ representations of gene by trait by environment interaction than is possible using only the statistical attributes (e.g. means, variances and correlations) of real-world trait datasets. This approach has an additional advantage in that ‘biological and experimental noise’ can be manipulated separately. These ‘testbeds’ for statistical techniques can be extended to the interpretation of a crossing and selection programme where the processes of chromosomal recombination are simulated using a quantitative genetics model and applied to the trait parameters. Statisticians are challenged to develop improved methods for the resulting simulated phenotype datasets, with the objective of revealing the (known) underlying genetic and environment structure that was input to the simulations. These improved methods can then be applied to existing plant breeding programmes.     

Accounting for both parameter and model structure uncertainty in crop model predictions of phenology: A case study on rice

We consider predictions of the impact of climate warming on rice development times in Sri Lanka. The major emphasis is on the uncertainty of the predictions, and in particular on the estimation of mean squared error of prediction. Three contributions to mean squared error are considered. The first is parameter uncertainty that results from model calibration. To take proper account of the complex data structure, generalized least squares is used to estimate the parameters and the variance-covariance matrix of the parameter estimators. The second contribution is model structure uncertainty, which we estimate using two different models. An ANOVA analysis is used to separate the contributions of parameter and model uncertainty to mean squared error. The third contribution is model error, which is estimated using hindcasts. Mean squared error of prediction of time from emergence to maturity, for baseline +2 °C, is estimated as 108 days², with model error contributing 86 days², followed by model structure uncertainty which contributes 15 days² and parameter uncertainty which contributes 7 days². We also show how prediction uncertainty is reduced if prediction concerns development time averaged over years, or the difference in development time between baseline and warmer temperatures.     

基于高斯函数的春玉米叶片功能期模型构建与应用

叶片功能期是影响光合生产能力的关键因素,冠层叶片功能期的量化评估对玉米植株生长和产量形成具有重要意义。本研究于2015—2017年在中国农业科学院作物科学研究所吉林公主岭试验站进行,定株观测先玉335和郑单958两个品种各个叶位叶片展开时间和衰老时间,基于2015年和2016年试验数据,以高斯函数(y =a+b×e-(x-c)^2/2d^2)模拟玉米各叶位叶片功能期的动态变化,并用2017年数据验证,在此基础上进一步明确了模型特征参数的生理学意义,简化了叶片功能期模型构建的方法。研究条件下利用高斯函数构建的玉米叶片功能期模型年际间稳定性好、品种间区分度大。进一步解析利用一阶导(功能期最大值)、二阶导(功能期变化速率最大的点)、三阶导(功能期开始快速增大的点)等于零取整后的叶位并配合最顶部叶位(n)和基部第1叶这5个转折叶位叶片功能期构建的模型拟合度良好,极大地简化了该模型参数拟合的数据需求,并探讨了利用该模型函数对玉米叶片功能分组的可能性。本研究为玉米生产能力的量化分析提供了思路和方法,对各类玉米生长模型的完善和其他相关研究也有借鉴意义。     

川中丘陵紫色土区作物及灌草植被生长模拟研究

作物及植被对川中紫色土区土壤侵蚀有着重要影响。本文通过使用土壤侵蚀过程模型WEPP的作物生长模块对陈家湾小流域作物及植被生长进行模拟,获取高度、盖度以及叶面积指数,与实测值进行对比。通过分析得出：WEPP模型作物生长模块对作物高度模拟较高,能够预测植被盖度、叶面积变化趋势但有不同程度的高估现象。     

基于作物生长模型的夏玉米灌溉需求分析

用校正后的作物模拟模型(PS123模型)和河北曲周县1966～1999年的逐日气象资料为依据,分析了当地夏玉米的需水量和优化灌溉方案.结果表明,该地区玉米生长季灌溉需水量的年际变异高达80%以上.用模拟的蒸散量计算的1999玉米水分利用效率一般在17～25kg*hm-2*mm-1之间,雨养条件下的水分利用效率较高,可达29kg*hm-2*mm-1.在引入灌     

Factorial regression analysis of genotype by environment interaction in ryegrass populations, using both isozyme and climatic data as covariates

A sample of 58 natural ryegrass populations (Lolium perenne L.) from the French and Spanish oceanic coast was evaluated for three seasonal growth traits (i.e. spring, autumn and winter vigour traits) in 5 evaluation sites (three in France and two in Spain). This sample showed a high level of genotype (population) × environment (evaluation sites) interactions for the three agronomic traits. A factor regression analysis, using both isozyme frequencies of populations and climatic factors of evaluation sites as covariates, was carried out on a sub-sample of 30 populations in order to explain these interactions. This method succeeded in explaining most population × location interactions by the product of two covariates. For instance, for spring vigour trait, 72.8% of the interaction term could be explained by the use of two covariates: the PGI2-20 allelic frequency of populations and the minimum temperature of the coldest month of evaluation sites. This study shows the advantage of such a method for plant breeders who need to identify promising ryegrass populations for their breeding objectives. A number of genetic and evolutionary hypotheses are also discussed. This revised version was published online in July 2006 with corrections to the Cover Date.