Mapping crop diseases using survey data: The case of bacterial wilt in bananas in the East African highlands

Globally, crop diseases result in significant losses in crop yields. To properly target interventions to control crop diseases, it is important to map diseases at a high resolution. However, many surveys of crop diseases pose challenges to mapping because available observations are only proxies of the actual disease, observations often are not normally distributed and because typically convenience sampling is applied, leading to spatially clustered observations and large areas without observations. This paper addresses these challenges by applying a geostatistical methodology for disease incidence mapping. The methodology is illustrated for the case of bacterial wilt of banana (BWB) caused by Xanthomonas campestris pv. musacearum in the East African highlands. In a survey using convenience sampling, 1350 banana producing farmers were asked to estimate the percentage yield loss due to bacterial wilt. To deal with the non-normal distribution of the data, the percentages were classified into two binary variables, indicating whether or not the disease occurred and whether or not the yield loss was severe. To improve the spatial prediction of disease incidence in areas with low sampling density, the target variables were correlated in a logistic regression to a range of environmental variables, for which maps were available. Subsequently, the residuals of the regression analysis were interpolated using simple kriging. Finally, the interpolated residuals were added to the regression predictions. This so-called indicator regression kriging approach resulted in continuous maps of disease incidence. Cross-validation showed that the method yields unbiased predictions and correctly assesses the prediction accuracy. The geostatistical mapping is also more accurate than conventional mapping, which uses the mean of observations within districts as the predicted value for all locations within the district, although the accuracy improvement is not very large. The maps were also spatially aggregated to district level to support regional decision-making. The analysis showed that the disease is widespread on banana farms throughout the study area and can locally reach severe levels.     

‘Fingerprints’ of four crop models as affected by soil input data aggregation

The spatial variability of soil properties is an important driver of yield variability at both field and regional scale. Thus, when using crop growth simulation models, the choice of spatial resolution of soil input data might be key in order to accurately reproduce observed yield variability. In this study we used four crop models (SIMPLACE<LINTUL-SLIM>, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo-morphologically) contrasting regions of the federal state of North-Rhine-Westphalia (Germany) for the period from 1995 to 2008. Three spatial resolutions of soil input data were taken into consideration, corresponding to the following map scales: 1:50 000, 1:300 000 and 1:1 000 000. The four crop models were run for water-limited production conditions and model results were evaluated in the form of frequency distributions, depicted by bean-plots.In both regions, soil data aggregation had very small influence on the shape and range of frequency distributions of simulated yield and simulated total growing season evapotranspiration for all models. Further analysis revealed that the small influence of spatial resolution of soil input data might be related to: (a) the high precipitation amount in the region which partly masked differences in soil characteristics for water holding capacity, (b) the loss of variability in hydraulic soil properties due to the methods applied to calculate water retention properties of the used soil profiles, and (c) the method of soil data aggregation.No characteristic “fingerprint” between sites, years and resolutions could be found for any of the models. Our results support earlier recommendation to evaluate model results on the basis of frequency distributions since these offer quick and better insight into the distribution of simulation results as compared to summary statistics only. Finally, our results support conclusions from other studies about the usefulness of considering a multi-model approach to quantify the uncertainty in simulated yields introduced by the crop growth simulation approach when exploring the effects of scaling for regional yield impact assessments.     

Evaluation of regional climate scenario data for impact assessment of climate change on rice productivity in Korea

Spatial evaluation of the uncertainty associated with climate data would allow reliable interpretation of simulation results for regional crop yield using gridded climate data as input to a crop growth model. The objective of this study was to examine the spatial uncertainty of regional climate model data through determining optimal seeding date with the ORYZA2000 model for assessment of climate change impact on rice productivity in Korea. The optimal seeding date was determined at each grid point using regional climate model outputs under the RCP 8.5 scenarios. In major rice production areas such as inland plain regions, where temperatures of regional climate data were relatively accurate, the optimal seeding date determined using those gridded data were reasonable. However, areas with complex terrains including areas near bodies of water, e.g. coastal areas, riverbasins, lakes, and mountainous areas, had a relatively large uncertainty of the optimal seeding date determined using the regional climate data. These results indicated that the uncertainty of regional climate data at a high spatial resolution of 12.5 km should be taken into account in the regional impact assessment based on crop growth simulations in Korea. In addition, further studies would be merited to assess the impact of climate change on rice yield at an ultra-high spatial resolution of 1 km in Korea. Crop yields were projected to decrease after the 2020s when crop yield simulations from inland plain areas were considered, which suggested that adaptation strategies should be established and implemented in the near future.     

Spatial and temporal uncertainty of crop yield aggregations

The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty.The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics.Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28).For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.     

Model suitability to assess regional potato yield patterns in northern Ecuador

A wide range of scenario studies aiming at rural development require regional patterns of crop yield. This study aims to evaluate three different modeling approaches for their suitability to assess regional potato yield patterns. The three model approaches include (1) an empirical model; (2) a process-based crop growth simulation model; and (3) a metamodel derived from the crop growth simulation model. Scenario studies have specific requirements for these modeling approaches including (1) their ease to use, (2) a realistic sensitivity, (3) the relevance in terms of generating the desired system property, and (4) their credibility in producing recognizable plausible outputs for stakeholders. The modeling approaches were applied to assess patterns of potato yields in a major production area in northern Ecuador. All three modeling approaches require significant expert knowledge for their development and calibration. However, after this initial phase, the empirical model and the metamodel are very easy to use and transparent. However, their application domain is limited to the case study area. The application of the crop growth simulation model remains complex and the model functions as a black box. The results show that regional patterns of potato yield are determined by a limited number of variables. The sensitivity of all three modeling approaches to climatic factors and water holding capacity suggest that the potato production in the area is constrained by water availability and temperature. All models generate similar yield patterns. However, the empirical model derives quality adjusted potato yields that correlate highly to the observed yields, whereas the crop growth simulation model and the derived metamodel produce potential, water and nutrient limited yields. Scenario studies may require yield patterns at different levels of resolution. All results could be aggregated to different resolutions but in general the patterns remained very similar. All three modeling approaches were capable to reproduce the observed regional pattern of potato yield and are therefore considered to be credible. In analyzing the effect of spatial aggregation on the performance of the modeling approaches, the results show that aggregation improves the overall correspondence between model output and interpolated, observed yields. It can be concluded that the various modeling approaches have their unique value. They are therefore complementary to each other for the interpretation of the observed patterns. The patterns themselves do not vary much and as such the most convenient modeling approach can be selected (based on available expertise and data).     

Characteristic ‘fingerprints’ of crop model responses to weather input data at different spatial resolutions

Crop growth simulation models are increasingly used for regionally assessing the effects of climate change and variability on crop yields. These models require spatially and temporally detailed, location-specific, environmental (weather and soil) and management data as inputs, which are often difficult to obtain consistently for larger regions. Aggregating the resolution of input data for crop model applications may increase the uncertainty of simulations to an extent that is not well understood. The present study aims to systematically analyse the effect of changes in the spatial resolution of weather input data on yields simulated by four crop models (LINTUL-SLIM, DSSAT-CSM, EPIC and WOFOST) which were utilized to test possible interactions between weather input data resolution and specific modelling approaches representing different degrees of complexity. The models were applied to simulate grain yield of spring barley in Finland for 12 years between 1994 and 2005 considering five spatial resolutions of daily weather data: weather station (point) and grid-based interpolated data at resolutions of 10 km × 10 km; 20 km × 20 km; 50 km × 50 km and 100 km × 100 km. Our results show that the differences between models were larger than the effect of the chosen spatial resolution of weather data for the considered years and region. When displaying model results graphically, each model exhibits a characteristic ‘fingerprint’ of simulated yield frequency distributions. These characteristic distributions in response to the inter-annual weather variability were independent of the spatial resolution of weather input data. Using one model (LINTUL-SLIM), we analysed how the aggregation strategy, i.e. aggregating model input versus model output data, influences the simulated yield frequency distribution. Results show that aggregating weather data has a smaller effect on the yield distribution than aggregating simulated yields which causes a deformation of the model fingerprint. We conclude that changes in the spatial resolution of weather input data introduce less uncertainty to the simulations than the use of different crop models but that more evaluation is required for other regions with a higher spatial heterogeneity in weather conditions, and for other input data related to soil and crop management to substantiate our findings. Our results provide further evidence to support other studies stressing the importance of using not just one, but different crop models in climate assessment studies.     

Comparison of regional climate scenario data by a spatial resolution for the impact assessment of the uncertainty associated with meteorological inputs data on crop yield simulations in Korea

Uncertainty of crop yield simulation would be affected by weather input data prepared from different sources of climate datasets. Although regional climate data at a high spatial resolution would be useful for the impact assessment of climate change on crop production, little effort has been made to characterize the uncertainty associated with such climate data in terms of crop yield simulations. The objectives of this study were to compare climate scenario data products obtained from a series of downscaling processes and to identify an overall pattern of uncertainty in these climate data in terms of crop yield simulation. Regional climate scenario data from 2011 to 2014 had a spatiotemporal pattern of uncertainty, which differed by meteorological variables and spatial resolution. Overall, the uncertainty of daily minimum temperature was greater than that of maximum temperature. Daily minimum temperature also had relatively greater uncertainty in an early season of crop production, which could result in the cumulative impact on the uncertainty of crop yield simulations. For the uncertainty of climate data at different spatial resolution, climate data at higher spatial resolution, e.g. 1 km, tended to have lower uncertainty than data at resolution of 12.5 km did. Still, the uncertainty of regional climate data was relatively similar between data at resolution of 12.5 km and 1 km in major rice production areas in Korea except in areas near Seosan. This merits further studies to examine actual differences in projected crop yields using regional climate scenario data in the future and to assess the impact of uncertainty associated with regional climate data on crop yield simulation.     

内蒙古东部农牧林交错区作物气候生产潜力评价

摘要：综合考虑光、温、水等影响作物生长发育的基本要素,采用数学模型,计算了内蒙古东部农牧林交错区作物气候生产潜力,从而对该区的作物生产能力进行初步评估和分析,为进一步发展区域作物生产、提高作物产量提供相关依据。     

基于环境因子的春玉米产量结构模型分析研究

环境因子对作物产量的影响是现代农业气象研究的重要内容之一,建立春玉米产量结构模型可为春玉米的科学生产提供依据。本研究分析贵港春玉米不同生育阶段的环境因子与产量结构的相关性,并建立全因子、显著因子的多元线性回归模型和BP神经网络模型。结果表明,对春玉米产量结构影响最大的生育期为拔节—抽雄期,10~40 cm的土壤水分体积含水率与产量结构最为密切;四种产量结构预测模型优度(R²)比较,全因子模型(AF)优于显著因子模型(SF),多元线性回归(MLR)模型优于BP神经网络(BPNN)模型。试报检验模型发现MLR模型的泛化能力不及BPNN模型,其中BPNN_AF模型对理论产量、果穗粗的预测最为精准。BPNN全因子模型(BPNN_AF)可作为春玉米产量结构预测的最优模型,能较好捕捉作物产量结构与环境因子之间的非线性影响规律,预测结果较为合理准确。     

高光谱遥感估测大豆冠层生长和籽粒产量的探讨

现代作物育种需要监测大量育种材料的生长并估测产量潜势, 高光谱遥感技术为此提供了简单、快捷、非损伤性测定的可能途径。选取30份大豆育成品种进行连续2年的产量比较试验, 在盛花期(R2)、盛荚期(R4)和鼓粒始期(R5)测定地上部生物量(ADM)和叶面积指数(LAI), 并利用ASD高光谱地物仪同步收集大豆冠层反射光谱信息。供试品种间ADM、LAI和产量差异显著或极显著。不同生育期可见光和近红外区域的光谱反射率与大豆ADM、LAI及产量均有显著相关, 尤其在R4和R5期相关性最高。在构建大量光谱参数的基础上, 遴选出对ADM、LAI及产量预测精度较好的回归模型。其中, R5期的P_Area560光谱参数与LAI和R4期的V_Area1450光谱参数与ADM构建的两个生长性状的监测模型效果最好, 决定系数(R²)分别为0.582和0.692。未发现单一生育期光谱参数对大豆估产的有效模型, 但综合R2期NPH1280、R4期V_Area1190以及R5期NPH560构建的产量估测模型, 决定系数(R²)达到0.68, 效果较好。本研究     

The interactions between genotype,management and environment in regional crop modelling

Biophysical models to simulate crop yield are increasingly applied in regional climate impact assessments. When performing large-area simulations, there is often a paucity of data to spatially represent changes in genotype (G) and management (M) across different environments (E). The importance of this uncertainty source in simulation results is currently unclear. In this study, we used a variance-based sensitivity analysis to quantify the relative contribution of maize hybrid (i.e. G) and sowing date (i.e. M) to the variability in biomass yield (Y_T, total above-ground biomass) and harvest index (HI, fraction of grain in total yield) of irrigated silage maize, across the extent of arable lands in New Zealand (i.e. E). Using a locally calibrated crop model (APSIM-maize), 25 G x M scenarios were simulated at a 5 arc minute resolution (∼5 km grid cell) using 30 years of historical weather data. Our results indicate that the impact of limited knowledge on G and M parameters depends on E and differs between model outputs. Specifically, the sensitivity of Y_T and HI to genotype and sowing date combinations showed different patterns across locations. The absolute impact of G and M factors was consistently greater in the colder southern regions of New Zealand. However, the relative share of total variability explained by each factor, the sensitivity index (S_i), showed distinct spatial patterns for the two output variables. The Y_T was more sensitive than HI in the warmer northern regions where absolute variability was the smallest. These patterns were characterised by a systematic response of S_i to environmental drivers. For example, the sensitivity of Y_T and HI to hybrid maturity consistently increased with temperature. For the irrigated conditions assumed in our study, inter-annual weather conditions explained a higher share of total variability in the southern colder regions. Our results suggest that the development of methods and datasets to more accurately represent spatio-temporal G and M variability can reduce uncertainty in regional modelling assessments at different degrees, depending on prevailing environmental conditions and the output variable of interest.     

基于升尺度方法的华北冬小麦区域生长模型初步研究 Ⅰ.潜在生产水平

作物生长模型是在田间尺度上开发的,而区域尺度上的作物生长信息更受决策部门的关注。作物模拟从单点研究发展到区域应用需要解决升尺度连接（Scaling-up）等一系列技术问题。本文利用以经纬度为权重的IDW空间插值法对气象数据和与温度有关的作物参数进行空间插值;根据华北冬小麦的品种地带性分布特点进行了冬小麦品种参数     

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.     

Dynamic Prediction of Cotton Regional Yield Based on the COSIM Model - A Case Study of Akesu City,Xinjiang

[Objective] Dynamic prediction of crop yield using a crop growth simulation model is the focus of increasing research attention. [Method] Based on meteorological, cotton yield, and cotton phenology data recorded at Akesu in Xinjiang from 1991 to 2014, this study aimed to improve the accuracy of crop yield prediction by the COSIM model. The average sowing date for each study year, as well as multiple sowing dates during the suitable sowing period, was imported into the COSIM model, and the two yield prediction methods were compared and analyzed. [Result] The accuracy of both yield prediction methods was higher than 90.0%, indicating that the two methods showed good applicability at Akesu. However, the method using multiple sowing dates during the suitable sowing period showed higher prediction accuracy when cotton yield was dynamically predicted in each month and the actual sowing date was uncertain. [Conclusion] The two prediction methods based on the crop growth simulation model are suitable for prediction of cotton yield at Akesu. In addition, according to the characteristics of different forecast years, the appropriate forecasting method can be used to improve the accuracy of prediction. The results also provide a reference for dynamic prediction of cotton yield in other cotton-producing areas.     

Improving cereal yield forecasts in Europe – The impact of weather extremes

The impact of extreme events (such as prolonged droughts, heat waves, cold shocks and frost) is poorly represented by most of the existing yield forecasting systems. Two new model-based approaches that account for the impact of extreme weather events on crop production are presented as a way to improve yield forecasts, both based on the Crop Growth Monitoring System (CGMS) of the European Commission. A first approach includes simple relations – consistent with the degree of complexity of the most generic crop simulators – to explicitly model the impact of these events on leaf development and yield formation. A second approach is a hybrid system which adds selected agro-climatic indicators (accounting for drought and cold/heat stress) to the previous one. The new proposed methods, together with the CGMS-standard approach and a system exclusively based on selected agro-climatic indicators, were evaluated in a comparative fashion for their forecasting reliability. The four systems were assessed for the main micro- and macro-thermal cereal crops grown in highly productive European countries. The workflow included the statistical post-processing of model outputs aggregated at national level with historical series (1995–2013) of official yields, followed by a cross-validation for forecasting events triggered at flowering, maturity and at an intermediate stage. With the system based on agro-climatic indicators, satisfactory performances were limited to microthermal crops grown in Mediterranean environments (i.e. crop production systems mainly driven by rainfall distribution). Compared to CGMS-standard system, the newly proposed approaches increased the forecasting reliability in 94% of the combinations crop × country × forecasting moment. In particular, the explicit simulation of the impact of extreme events explained a large part of the inter-annual variability (up to +44% for spring barley in Poland), while the addition of agro-climatic indicators to the workflow mostly added accuracy to an already satisfactory forecasting system.     

空间统计分析在作物育种品系选择中的效果

为了研究空间统计分析法在作物育种田间试验品系选择中的效果,采用剩余误差空间相关线性混合模型对一个具有56个品系的小麦育种随机区组设计田间试验产量资料进行了空间统计分析。运用地理统计学中的半变异函数法确定剩余误差空间协方差的函数。结果表明,试验的剩余误差存在着典型的空间相关性,利用剩余误差空间协方差结构的信息可降低品系效应估计的误差和提高品系效应差异F检验与t检验的效率。此外,空间分析法对品系效应估计受试验条件不均匀的影响小,可导致较经典方差分析法不同的品系排序和优系选择结果。     

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.     

作物产量“三合结构”定量表达及高产分析

针对目前作物产量水平长期徘徊难以突破，产量分析理论缺乏量化指标体系，可操作指导作用小等问题，依据“三合结构”模式二级结构层各因素的关系，建立了“三合结构”定量表达式，并通过田间试验与模型模拟相结合的方法，对春玉米、夏玉米、水稻和冬小麦高产实例进行定量化分析，明确了限制产量进一步提高的关键因素，提出了高产突破的可能方向。结果表明，提高叶片平均净同化率（MNAR），改善群体的物质生产能力，是水稻产量进一步提升的关键；适当提高平均叶面积指数（MLAI）或经济系数（HI）可能会进一步增加冬小麦产量；春玉米籽粒产量主要伴随着MLAI和单位面积穗数（EN）的增加而提高，其实质是平均作物生长率（MCGR）的提高增加了单位面积上总粒数（TGN）。进一步研究确定了“三合结构”定量表达式参数间的函数关系式，通过公式代换可推导出某一参数与目标参数的函数关系。作物产量“三合结构”定量表达式的建立为作物群体定量化研究提供了新的思路和方法，有助于全面掌握群体参数变化与产量形成的定量关系，为指导作物生产进行有效的技术调控提供依据。     

气候变异对内蒙古武川县麦类作物产量的影响

【研究目的】内蒙古武川县位于阴山北麓农牧交错带温凉旱区,是春小麦、莜麦等喜凉作物的适宜产地,但由于气候的年际波动,产量低而不稳。通过分析产量与气候的关系,可采取措施减轻潜在的气候风险。【方法】笔者根据产量统计资料、生育期观测资料和历年气象数据,用相关分析法分析各时期平均温度、日照时数、降水量三个气候因子对春小麦、莜麦产量的影响,建立多因子产量评估模式。【结果】结果表明,武川小麦和莜麦的丰歉与生长季各月的平均气温及其总和呈负相关,与生长季各月降水量及生长季降水之和呈正相关,与日照时数关系不显著。通过找出影响产量丰歉的气象指标因子进行多元回归分析,得出产量的评价模型,可用于武川县麦类作物产量丰歉年评估。【结论】研究认为,气候暖干化将增加武川麦类作物生产的气候风险     

Assessment of uncertainty and sensitivity analyses for ORYZA model under different ranges of parameter variation

We explore the effects of different ranges of parameter variation (RPV) on sensitivity and uncertainty analyses for ORYZA_V3 model. In this study, a latin hypercube sampling (LHS) technique is used to generate parameter sample sets, and a regression-based method is employed for the sensitivity analysis on 16 crop parameters. Then, a top-down concordance coefficient (TDCC) is calculated to assess the stability of parameter sensitivity rankings across diverse RPV. Furthermore, coefficients of variation (CV) and 90% confidence intervals (90CI) of daily model outputs are analyzed by considering uncertainty in observations. We find that the increasing RPV multiplies the CV of daily model outputs, whereas the RPV has no effect on the CV’s change rule over time. The 90CI of model outputs include most of the observations when the RPV is more than ±30% perturbation. The standardized regression coefficient (SRC) of some parameters are obviously minified when the RPV is ±5% or ±50% perturbation. The results highlights the importance of RPV selection in the sensitivity and uncertainty analysis of crop model, and ±30% perturbation was suggested when the RPV cannot be specifically obtained.