A dynamic model of the influence of rotation and crop management on the disease development of eyespot. Proposal of cropping systems with low disease risk

A dynamic model of the effect of rotation and crop management on the frequency of plants infected by eyespot (anamorph Pseudocercosporella herpotrichoides, teleomorph Tapesia yallundae) in a field is proposed and its parameters are estimated on a series of experiments in France during two years. A first equation estimates disease frequency as a function of thermal time and of two parameters associated to the primary (from infectious crop residues) and secondary (from living diseased plants) infection cycles. Two other equations are proposed to estimate the values of the two infection cycle parameters as a function of macro-environment and cropping system; interactions between cultivation techniques were integrated using multiplicative equations. The primary infection cycle parameter depended on crop rotation, soil tillage, sowing date, tiller number per plant and available nitrogen. The secondary infection cycle parameter depended on tiller number per plant. The macro-environment effect on the two parameters is consistent with epidemiological models. The proposed model allows to choose those cropping systems that minimise disease risk for a given set of environmental and technical constraints.     

A crop model component simulating N partitioning during seed filling in pea

Seed N concentration is one of the main quality parameters in grain legume crops. Since few studies have aimed at modelling both seed and vegetative parts N concentrations, our objective was to model N partitioning between vegetative parts and filling seeds for pea (Pisum sativum L.) in field situations where both N nutrition and the plant genotype varied. A crop model component predicting the time courses of vegetative and seed N concentrations was built using knowledge concerning N partitioning during the seed filling period, which include a previously demonstrated relationship between the rate of individual seed N accumulation and the N availability within plants. A greenhouse experiment where assimilate availability was non-limiting was conducted with two genotypes. This experiment demonstrated the genotypic variability of one of the crop model component parameters, the maximum rate of individual seed N accumulation (SNR_max), allowing introduction of this parameter in the crop model component for the studied genotypes. Field experiments spanning 3 years and comprising various crop N nutrition and four genotypes were conducted to evaluate the crop model component. Observed seed and vegetative parts N concentrations ranged at harvest from 19.3 to 39.1 mg g⁻¹ and from 3.6 to 18.4 mg g⁻¹, respectively. N partitioning was well-simulated by the crop model component except when crops had deficient N nutrition. These results suggest that the parameter “NC_n-remob” (proportion of N in vegetative parts which is not available for remobilization to filling seeds), which is taken as constant in the crop model component, could depend upon the crop nutrition level. A sensitivity analysis highlights the need for a precise calibration of the parameters “NC_n-remob” and “SNR_max”. When the crop N nutrition level and further genotypic variability of these parameters are incorporated in the proposed crop model component, it will become a useful part of a pea crop model predicting yield and seed N concentration.     

Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars

Several sensor systems are available for ground-based remote sensing in crop management. Vegetation indices of multiple active and passive sensors have seldom been compared in determining plant health. This work describes a study comparing active and passive sensing systems in terms of their ability to recognize agronomic parameters. One bi-directional passive radiometer (BDR) and three active sensors, including the Crop Circle, GreenSeeker, and an active flash sensor (AFS), were tested for their ability to assess six destructively determined crop parameters. Over 2 years, seven wheat (Triticum aestivum L.) cultivars were grown with nitrogen supplies varying from 0 to 220 kg ha⁻¹. At three developmental stages, the crop reflectance was recorded and sensor-specific indices were calculated and related to N levels and the crop parameters, fresh weight, dry weight, dry matter content, as percent of dry weight to fresh weight, N content, aboveground N uptake, and the nitrogen nutrition index. The majority of the tested indices, based on different combinations of wavelengths in the visible and near infrared spectral ranges, showed high r²-values when correlated with the crop parameters. However, the accuracy of discriminating the influence of varying N levels on various crop parameters differed between sensors and showed an interaction with growing seasons and developmental stage. Visible- and red light-based indices, such as the NDVI, simple ratio (R₇₈₀/R₆₇₀), and related indices tended to saturate with increasing crop stand density due to a decreased sensitivity of the spectral signal. Among the destructively assessed biomass parameters, the best relationships were found for N-related parameters, with r²-values of up to 0.96. The near infrared-based index R₇₆₀/R₇₃₀ was the most powerful and temporarily stable index indicating the N status of wheat. This index was delivered by the BDR, Crop Circle, and AFS. Active spectral remote sensing is more flexible in terms of timeliness and illumination conditions, but to date, it is bound to a limited number of indices. At present, the broad spectral information from bi-directional passive sensors offers enhanced options for the future development of crop- or cultivar-specific algorithms.     

Non-destructive shoot biomass evaluation using a handheld NDVI sensor for field-grown staking Yam (Dioscorea rotundata Poir.)

ABSTRACT

Crop phenotyping is a key process used to accelerate breeding programs in the era of high-throughput genotyping. However, most rapid phenotyping methods developed to date have focused on major cereals or legumes, and their application to minor crops has been delayed. In this study, we developed a non-destructive method to predict shoot biomass by measuring spectral reflectance in staking yam (Dioscorea rotundata). The normalized difference vegetation index (NDVI) was evaluated using a handheld sensor that was vertically scanned from the top to the bottom of a plant alongside the stake. A linear regression model was constructed to predict shoot biomass through Bayesian analysis using NDVI as a parameter. The model well predicted the observed values of shoot biomass, irrespective of the growth stage and genotypes. Conversely, the model tended to underestimate the shoot biomass when the actual shoot biomass exceeded 150 g plant^?1; this was compensated for when the parameter green area, calculated from plant image, was included in the model. This method reduced the time, cost, effort, and field space needed for shoot biomass evaluation compared with that needed for the sampling method, enabling shoot biomass phenotyping for a large population of plants. A total of 210 cross-populated plants were evaluated, and a correlation analysis was performed between the predicted shoot biomass and tuber yield. In addition to the prediction of tuber yield, this method could also be applied for the evaluation of crop models and stress tolerance, as well as for genetic analyses.     

Simulating seed reserve mobilization and seedling growth of rice in DSRICE1

The simulation of seed reserve mobilization and seedling growth of rice in the model DSRICE1 was analyzed using published data. Early plant DM patterns are characterized by a decline in total (kernel+seedling) DM during heterotrophy, and then an increase into exponential growth after CO₂ assimilation (autotrophy) begins. Data for a tropical japonica variety were used for qualitative comparisons of observed and predicted kernel and total DM. The published version of DSRICE1 was sequentially modified to (1) subtract respiration costs from mobilized reserves, (2) use a constant mobilization rate, (3) use the same partitioning fractions (PF) for both reserves and assimilates, and (4) assess the effects of leaching losses. After the first modification, the model reproduced observed DM patterns, and setting a constant mobilization rate allowed complete use of reserves. Using consistent PF values for both assimilates and reserves was justified because it simplified the model and had only slight effects on predictions. Predicted mobilization efficiencies (ME; g seedling g⁻¹ seed) were greater than measured values, but were improved by accounting for leaching losses. In sensitivity analyses, six seed-related parameters had significant effects on seedling DM at 14 days after seeding (DAS), but none significantly affected mature DM or yields. In other tests, the effects of four traits on seedling growth were assessed over their likely empirical ranges. The start of CO₂ assimilation (DAS) had the greatest effect, followed by the start of mobilization and seed mass. Mobilization rate had the smallest effect over its likely range. Finally, simulated and measured early PF values were significantly different, particularly for root partitioning. Predicted DM to 18 DAS using three sets of PF values also differed, although more for total DM per plant than for some plant parts. Using the measured PF values will likely improve the model. It was also demonstrated that simulating reserve mobilization allows dynamic responses to variation in environmental and cultural inputs that may have critical effects on later growth. Thus, the calibration of parameters such as initial DM and leaf area required in other models may be avoided. Most important, using a more process-based approach in DSRICE1 facilitates (or forces) realistic linkages between seedling growth and environment by management interactions, which can improve analyses of crop and weed establishment and management strategies.     

Sensitivity analysis and auto-calibration of ORYZA2000 using simulation-optimization framework

There has been an increasing interest to employ crop growth simulation models for taking decision on irrigation water management. The effectiveness of such decisions mainly lies on the efficiency of the model in simulating the crop growth and the yield, which are influenced by the value of the parameters of the model. Therefore, calibration of such models is necessary before it can be employed for any application. This study proposes an auto-calibration procedure for ORYZA2000, a rice crop growth simulation model, for its application in South India. The data employed for calibration is taken from a field experiment conducted for 2 years in an experimental farm in South India. The ORYZA2000 model was integrated within Genetic Algorithm optimizer, which calls the simulator during each generation to evaluate the objective function. The auto-calibrated model was tested for its performance using a validation data set from the same experimental data. The results showed that the calibrated ORYZA2000 model is capable of simulating the full irrigation and water stress condition of rice crop effectively, and can be used to develop deficit irrigation management schedules.     

无人机载多光谱遥感监测冬油菜氮素营养研究

为探索无人机搭载的多光谱相机对冬油菜冠层氮素营养状况监测的可行性,设置9种施氮水平的油菜试验小区,获取八叶期、十叶期、十二叶期和蕾臺期的多光谱影像,同步采样分析获取地上部生物量、叶片氮浓度和氮素积累量等氮营养指标。以宽波段植被指数和氮营养指标的相关性为基础,通过敏感性分析确定最佳指数,建立预测模型并进行精度验证。结果显示,宽波段植被指数与氮营养指标有极显著的相关性,不同生育期差异明显。其中,红光标准值和蓝光标准值在蕾臺期均与各氮营养指标相关关系最好,且敏感性因子的值小而稳定。进一步研究表明,三种指标均可用红光标准值和蓝光标准值建立的二次模型进行估计,决定系数R2均大于0.85,模型精度较高,说明无人机多光谱遥感能有效辅助冬油菜氮素营养监测。     

The development of a coupled model (PCPF-SWMS) to simulate water flow and pollutant transport in Japanese paddy fields

A new coupled model (PCPF–SWMS) was developed for simulating fate and behavior of pollutant in paddy water and paddy soil. The model coupled the PCPF-1, a lumped model simulating pesticide concentrations in paddy water and 1 cm-surface sediment compartment, and the SWMS-2D, a finite element numerical model solving Richard's and advection-dispersion equations for solute transport in soil compartment. The coupling involved improvements on interactions of the water flow and the concentration the pollutant of at the soil interface between both compartments. The monitoring data collected from experimental plots in Tsukuba, Japan in 1998 and 1999 were used to parameterise and calibrate hydraulic functioning, hydrodynamic and hydrodispersive parameters of the paddy soil. The analysis on the hydraulic functioning of paddy soil revealed that the hard pan layer was the key factor controlling percolation rate and tracer transport. Matric potential and tracer monitoring highlighted the evolution of saturated hydraulic conductivity (K _S) of hard pan layer during the crop season. K _S slightly decreased after puddling by clay clogging and strongly increased after mid term drainage by drying cracks. The model was able to calculate residential time in every soil layers. Residential time of tracer in top saturated layers was evaluated to be less than 40 days. It took 60 days to reach the unsaturated layers below hardpan layer.     

西北春麦区Apsim-Wheat模型参数全局敏感性分析

为了提高西北春麦区Apsim-Wheat模型的模拟精度及适应性,以甘肃省定西市安定区2015-2018年田间试验数据为基础,采用基于参数筛选法(Morris)和基于方差分解法(Sobol)分析了Apsim-Wheat模型中作物品种参数、土壤参数及田间管理参数对小麦产量的敏感性,并分析比较了两种方法对模型的适应性。结果表明,用Morris法得到的对小麦产量敏感的参数分别为始花期积温(TFI)、出苗到拔节期积温(TEOJ)、春化敏感指数(VS)、田间持水量(DUL)、萎焉系数(LL15)、小麦萎焉系数(WheatLL)、播种日期(sowing date)、播种密度(sowing density)、播种深度(sowing depth);用Sobol法得到的对小麦产量敏感的土壤参数及田间管理参数与Morris法相同,而得到的敏感作物品种参数不同,分别为春化敏感系数(VS)、开花期积温(TF)、出苗到拔节期积温(TEOJ)、潜在灌浆速率(PGFR)、光周期敏感指数(PS)、灌浆期积温(TSGF);在Morris法和Sobol法下Apsim-Wheat模型中对小麦产量最敏感的参数均是作物品种参数,且多与品种积温、春化及光周期等参数有关,分别占总敏感性的73.85%和62.9%,土壤参数及田间管理参数对小麦产量的总敏感性分别为26.15%和37.1%,占比较小。综合来看,Morris法和Sobol法均可筛选出对小麦产量敏感的参数,且在调整土壤参数和田间管理参数时,两者具有可替代性;在调整作物品种参数时,可以依据地区实际情况,结合两种方法各自优势,快速筛选出模型敏感参数。     

Yield prediction using the SUBSTOR-potato model under Argentinian conditions

Summary The use of crop simulation models to predict yield, associated with decision support systems such as Decision Support System for Agrotechnology Transfer (DSSAT), are useful tools to test different management strategies. The potato growth model included in DSSAT is SUBSTOR-potato. To evaluate its performance in Argentina it was calibrated and validated using experimental results from different sites and years. Cultivar-specific coefficients were obtained during calibration. Validation based on several independent sets of field data, including cvs Huinkul, Kennebec, Mailén and Spunta showed good agreement (R²=0.915; n=24) between observed and simulated values in normal ranges of tuber yields. However, when the input parameter maturity date was not taken into account, tuber yields were overvalued due to an overestimation of LAI values during maturation. To solve this problem, a genetic coefficient for the duration of tuber filling needs to be included in the model.     

Modeling temperature- and radiation-driven leaf area expansion in the contrasting crops potato and wheat

The performance of a model for simulating increase in leaf area index (L) was evaluated for potato (Solanum tuberosum L.) and wheat (Triticum aestivum L.) cultivars across environments (years and sites). Rate of L expansion just after emergence was assumed to depend on temperature. After a predefined L, L_s, expansion was assumed to increase in proportion to leaf dry weight increase that depended on intercepted radiation, henceforward: radiation-limited expansion. The L_s value at which the model performed best was considered to be the most realistic L at which expansion shifts from temperature to radiation-limitation. An L_s value of zero leads to solely radiation-limited expansion, whereas a value larger than maximum L leads to solely temperature-limited expansion. The criteria used to evaluate the model were constancy of calibrated model parameters across environments, and predictive ability. For potato and wheat, parameters were most robust across environments, when L_s was neither zero nor at maximum L. Model parameters did not vary with genotype. The model’s predictions were best at an L_s of 1.0 for potato and 1.5 for wheat. Using these L_s values, the coefficient of determination between observed and predicted values was 91% for potato and 88% for wheat. Sensitivity analysis revealed that smaller L_s values led to larger changes in rate of leaf area expansion and crop dry weight than larger values did. Crop dry weight was hardly affected by an increase in L_s. Implications of the results for modeling are discussed.     

A modeling approach for assessing rice cropping cycle affected by flooding,salinity intrusion,and monsoon rains in the Mekong Delta,Vietnam

We developed a crop scheduling model for rice cultivation in the Vietnam Mekong Delta (VMD), focusing on the adaptive behavior of crop planning to various water resource constraints. In addition, we also examined the effects of environmental change on rice cultivation in the last decade. In the VMD, multiple rice cropping is practiced under a variety of adverse water conditions, including flooding, salinity intrusion, and irregular monsoon rains. These environmental changes influence the durations of growing seasons and the number of crops per year, resulting in changes in productivity. To validate the performance of the model, we compared model estimates for the heading date and changes in leaf area index at nine sites with estimates of these parameters derived from MODIS satellite time series data for the period 2002–2006. The root mean square errors of heading date between the modeled and satellite data in the upper, middle, and coastal regions of the delta were 17.6, 11.2, and 13.0 days, respectively. Based on the model, we examined case studies to assess the changes in cropping cycles and crop failures in the VMD due to extreme flooding in 2000 and salinity intrusion in 2004 by applying evaluation indices defined by available period for cultivation (APC) and safe margin for cropping (SMC) which is defined as the marginal time between APC and the period required for cultivation. Findings of case studies suggested that a small difference in the SMC of the cropping pattern is critical to the stability and productive capacity of the rice crop.     

Methodologies for simulating impacts of climate change on crop production

Ecophysiological models are widely used to forecast potential impacts of climate change on future agricultural productivity and to examine options for adaptation by local stakeholders and policy makers. However, protocols followed in such assessments vary to such an extent that they constrain cross-study syntheses and increase the potential for bias in projected impacts. We reviewed 221 peer-reviewed papers that used crop simulation models to examine diverse aspects of how climate change might affect agricultural systems. Six subject areas were examined: target crops and regions; the crop model(s) used and their characteristics; sources and application of data on [CO₂] and climate; impact parameters evaluated; assessment of variability or risk; and adaptation strategies. Wheat, maize, soybean and rice were considered in approximately 170 papers. The USA (55 papers) and Europe (64 papers) were the dominant regions studied. The most frequent approach used to simulate response to CO₂ involved adjusting daily radiation use efficiency (RUE) and transpiration, precluding consideration of the interacting effects of CO₂, stomatal conductance and canopy temperature, which are expected to exacerbate effects of global warming. The assumed baseline [CO₂] typically corresponded to conditions 10-30 years earlier than the date the paper was accepted, exaggerating the relative impacts of increased [CO₂]. Due in part to the diverse scenarios for increases in greenhouse gas emissions, assumed future [CO₂] also varied greatly, further complicating comparisons among studies. Papers considering adaptation predominantly examined changes in planting dates and cultivars; only 20 papers tested different tillage practices or crop rotations. Risk was quantified in over half the papers, mainly in relation to variability in yield or effects of water deficits, but the limited consideration of other factors affecting risk beside climate change per se suggests that impacts of climate change were overestimated relative to background variability. A coordinated crop, climate and soil data resource would allow researchers to focus on underlying science. More extensive model intercomparison, facilitated by modular software, should strengthen the biological realism of predictions and clarify the limits of our ability to forecast agricultural impacts of climate change on crop production and associated food security as well as to evaluate potential for adaptation.     

Water availability at sowing and nitrogen management of durum wheat: a seasonal analysis with the CERES-Wheat model

In water-limited environments soil water content at sowing is important in determining durum wheat germination, emergence and plant establishment. Soil water content interacts greatly with soil nitrogen content, affecting nitrogen uptake and crop productivity. Simulation models can be used to confirm the optimal strategy by testing several crop management scenarios.The CERES-Wheat model, previously calibrated and validated in southern Italy, has been used in a seasonal analysis to optimise nitrogen fertilisation of durum wheat at different levels of crop available water (CAW) at planting date in southern Italy. The simulation was carried out for a 48-year period with measured daily climatic data. The 99 simulated scenarios derived from the combinations of different CAW levels at sowing, nitrogen fertiliser rates and application times.The results obtained from the simulation indicated that the effect of CAW at sowing was relevant for durum wheat production at lowest and highest values, while the optimal sowing time to maximise yield and profit can be considered when CAW is 40–60%. In the case study optimal N fertiliser amount was estimated to be 100±20 kg ha⁻¹, from a productive, environmental and economic point of view. The nitrogen split application—half at sowing and half at stem extension stage—resulted in the best management practice.This application of the CERES-Wheat model confirmed the capability of the model to compare several crop management strategies in a typical durum wheat cropping area.     

Simulating Phenology,Growth and Yield of Transplanted Rice at Different Seedling Ages in Northern Iran Using ORYZA2000

Rice crop growth and yield in the north Iran are affected by crop duration and phenology.The purpose of this study was to calibrate and validate the ORYZA2000 model under potential production based on experimental data for simulating and quantifying the phenological development,crop duration and yield prediction of rice crop influenced by different seedling ages.In order to calibrate and validate the crop parameters of ORYZA2000 model,a two-year field experiment was conducted under potential growth conditio...     

Simulating growth,development, and yield of tillering pearl millet: II. Simulation of canopy development

Tillering is an important adaptive feature of pearl millet (Pennisetum americanum L.) to the unpredictable growing conditions of dry areas of the semi-arid tropics. Yet, this feature has largely been ignored in the development of simulation models for pearl millet. The objective of this paper is to parameterise and validate a leaf area module for pearl millet, which dynamically simulates crop leaf area from the leaf area of individual axes through simulating inter-axis competition for light. To derive parameters for the model, four cultivars (contrasting in phenology and tillering habit) were grown under well-watered and well-fertilised conditions across a range of plant densities in three experiments at two locations in India. For selected plants, observations on the number of primary basal tillers and on the number of visible, fully expanded, and senesced leaves on each axis were made twice a week throughout the growing season. Occurrence of panicle initiation (PI) was observed in two experiments only, but data were complemented by published and unpublished data, obtained for comparable cultivars. Parameters were obtained for the time from emergence to PI as a function of daylength, the leaf initiation rate, the rate of leaf and tiller appearance and the leaf senescence rate; parameters for leaf size were determined in a previous paper. Our parameter estimates compared well with published data and were, with the exception of time to PI and leaf size, mostly independent of cultivar, axis and density. Genotypic effects on productive tiller number could be attributed to differences in main shoot leaf size. Validation of the leaf area module showed that the module adequately reproduced the effects of density, photoperiod and genotype on the leaf area of individual axes and on productive tiller number. This was despite the fact that the reduction in leaf area of non-productive tillers was achieved in the module through a reduction in leaf size, whereas the crop reduced leaf area through a reduction in leaf number. Our results indicate that LAI of a tillering crop can be simulated adequately by simulating LAI from individual leaf area and incorporating the effects of competition for light.     

Cadmium transport and tolerance in rice: perspectives for reducing grain cadmium accumulation

In recent decades, numerous studies have attempted to project the impact of hypothesised anthropogenic climate change on rice production. In this study, we offer a comprehensive review of our current understanding related to temperature, CO₂, and water-demand parameters in rice growth models. As to future rice yield, night time temperature should be focused in the models as well as day time temperature owing to the contribution of temperature on the night time respiration. Furthermore, although CO₂-enhanced photosynthesis is critical for the accurate prediction of rice production in a higher CO₂ atmosphere, we found that recent well-developed photosynthesis-stomatal model cannot realize the variation of CO₂ stomatal sensitivity with humidity conditions. To estimate water stress under projected climate-change conditions, rice growth model should be required to link with water resource model, which includes natural processes and anthropogenic regulations. The understanding of abilities and limitations in the models is important not only to improve the schemes that models employ, but to also critically review the simulated results.     

三种数学模型模拟不同播期小麦籽粒灌浆过程的比较分析

为比较分析Logistic、Richards和三次多项式（Cubic）3种小麦籽粒灌浆模型的特点,用其分别对4个小麦品种（济麦44、品育8012、周麦36和晋麦84）在4个播期下的籽粒灌浆过程进行拟合。结果表明,不同播期小麦籽粒灌浆过程用三种模型均可拟合,各模型方程的决定系数均在0.98以上,且达极显著水平,Cubic和Richards模型拟合效果优于Logistic模型;通过Cubic模型模拟的灌浆起始期较观测值晚0.58~6.65 d,10月12日播期起始灌浆日期模拟值与观测值最接近;在本试验条件下,Logistic模型可以假定粒重达理论最大粒重的2.76%左右时为灌浆起始期（开花期）;Richard模型中,播期对起始粒重比例（灌浆起始期粒重/理论最大粒重）有显著影响,第一、第二播期（10月12日、10月20日）的超始粒重比例显著低于第三、第四播期（10月28日、11月5日）;三种模型理论最大粒重在品种和播期间的规律与灌浆终止粒重一致;Cubic模型模拟的理论最大粒重与灌浆终止粒重最接近,终止粒重比例（灌浆终止粒重/理论最大粒重）平均值达 97.84%;Logistic与Richards模型中,终止粒重比例随播期的推迟呈增加趋势,Richards模型终止粒重比例在播期间的变异系数大于Logistic和Cubic模型。小麦快增期灌浆参数T₂（快增期持续时间）、T_max（最大灌浆速率出现时间）、V₂（快增期平均灌浆速率）、V_max（最大灌浆速率）在三种模型间存在差异,Richards模型在播期间差异最大（T₂、T_max、V₂、V_max变异系数分别为35.25%、20.43%、11.94%、10.8%）,Logistic模型在播期间差异最小（T₂、T_max、V₂、V_max变异系数分别为8.79%、7.59%、3.30%、3.30%）。     

不同冬小麦品种株高的高光谱估算模型

为建立小麦株高的高光谱估算适宜模型,通过连续两年田间不同品种试验,在拔节至抽穗期同步测定小麦冠层高光谱数据和株高,并对两者的关系进行系统分析。结果表明,小麦株高与可见光波段呈负相关,与近红外波段呈正相关,与可见光波段的相关性总体上高于近红外波段。株高可以利用统一的光谱参数进行定量反演,其中以F698、D550、Dy、λr、SDr/SDb和(SDr-SDb)/(SDr+SDb)等光谱参数拟合效果较好。经两年的独立试验数据检验表明,以参数F698、D550、Dy及(SDr-SDb)/(SDr+SDb)为变量建立的株高估算模型表现较为稳定,尤其是以光谱参数(SDr-SDb)/(SDr+SDb)建立的模型,建模决定系数为0.85,预测决定系数和均方根偏差分别为0.86和4.27,相对误差为9%。因此,该参数可以作为估测小麦株高的有效光谱参数,对小麦生长中期的株高进行监测。