Improvement of spatially and temporally continuous crop leaf area index by integration of CERES-Maize model and MODIS data

The spatially and temporally continuous leaf area index (LAI) mapping is very crucial for many agricultural applications, such as crop yield estimation and growth status monitoring. Data assimilation technology provides an innovational way to improve spatio-temporally continuous crop LAI estimation through integration of remotely sensed observations and crop growth models. In this study, a very fast simulated annealing (VFSA)-based variational assimilation scheme was proposed to integrate the crop growth model (CERES-Maize), MODIS reflectance product (MOD09A1) and a two-layer canopy reflectance model (ACRM) to estimate time-series crop LAI at regional scale. Simultaneously, a new sensitivity analysis method (called “histogram comparison”) was developed to identify sensitive parameters of CERES-Maize and ACRM models. The proposed scheme was applied for continuous crop LAI estimation during the maize growing season in the dominating spring maize planting area of Jilin province, China. Results showed that R² values between LAI estimations from the proposed assimilation scheme (referred to as assimilated LAI) and fine resolution LAI reference maps were 0.24 and 0.63, with RMSE values of 0.21 and 0.54 for Julian day 176, 2010, and Julian day 196, 2010, respectively. The assimilated results were closer to LAI reference maps than the MODIS LAI product and ACRM-based inversion results (referred to as ACRM LAI). Moreover, by introducing the prior information of LAI dynamics depicted by a crop growth model, the assimilated LAI showed better temporal consistency than the MODIS LAI product, LAI profiles simulated by CERES-Maize model (referred to as CERES-Maize LAI), and ACRM LAI. It was found that the accuracies of LAI estimations could be enhanced by assimilating satellite observations into a crop simulation model in the VFSA framework at a regional scale.     

Forcing a wheat crop model with LAI data to access agronomic variables: Evaluation of the impact of model and LAI uncertainties and comparison with an empirical approach

The objective of this study is to evaluate the performances of estimating agronomic variables, such as total above ground biomass at key stages, or yield, from LAI data that could potentially be obtained from remote sensing observations. Approaches based either on empirical relationships or on forcing LAI within the STICS model (Brisson et al., 2009) are considered, with emphasis on the effect of the accuracy and frequency of LAI data used. Both actual and simulated case studies on wheat for Northern France conditions were investigated under several levels of knowledge of the model input parameters and initial conditions.The results highlight the interest of using model based approaches for the estimation of agronomic variables. Forcing LAI data into the crop model allows compensating for the lack of detailed knowledge on management practices or soil characteristics. However, error and frequency of LAI observations may have an important impact on the estimation of agronomic variables, particularly for the early growth stages. In these conditions, an empirical approach, based on the calibration of a relationship between LAI at a given stage and the agronomic variable, provides an efficient alternative, though the validity of empirical relationships depends greatly on the database on which they have been obtained.     

Estimation of Leaf Area Index of Beta vulgaris L. Based on Optical Remote Sensing Data

Remote sensing and vegetation indices can be used to characterize the canopy of crops with a non‐destructive method on a large scale. Leaf area formation of sugar beet in early summer is the most important variable for crop growth models. This study aimed at estimating whether differences in leaf area development of sugar beet resulting from different agronomic practices can be determined with remote sensing. The relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI) during the season and yield of the storage root in autumn was studied in six field trials in 2001 and nine field trials in 2002. The vegetation index NDVI gave a good impression of differences in leaf development of sugar beet in early summer. LAI increased with increasing NDVI up to an NDVI of 0.65. Above that the NDVI did not respond as distinctly to treatments as the LAI. An exponential function was developed to calculate sugar beet LAI from NDVI, so that remote sensing data can be used as input variable for crop growth models. The yield of the storage root in autumn did not show any relationship to LAI or NDVI during the season, regardless of whether it was measured in June or September. Therefore, it seems to be necessary to combine NDVI data with crop growth models to forecast a potential sugar beet yield in autumn. For this purpose the formula presented is a valuable tool.     

遥感信息应用于水分胁迫条件下的华北冬小麦生长模拟研究

由于初始土壤水分、灌溉量等变量的空间分布不易获得,区域尺度水分胁迫条件下作物生长模拟存在一定难度。本文在WOFOST模型本地化和区域化的基础上,采用调控型方法,重点探讨了利用MODIS数据反演的地表蒸散在大范围内估算土壤水分平衡过程中的参数或变量初始值,以实现水分胁迫条件下作物模型区域模拟的可行性。2002年模拟结果显示,引入遥感信息优化获得初始土壤有效含水量、返青期生物量及抽穗期灌溉量后,土壤水分的模拟效果得到改善;32个农业气象试验站点模拟产量的相对均方根误差（RRMSE）由0.63降至0.20;华北冬小麦模拟产量的空间分布与实际产量分布更加接近,产量低估的情况得到较好改善;河北、河南、山东3省平均产量的模拟误差分别为－4.9%、4.3%和8.6%。初步结果表明,结合卫星遥感信息通过优化方法在大范围内估算作物模型的相关参变量,以实现水分胁迫条件下作物模型的区域应用是行之有效的。     

玉米和大豆LAI高光谱遥感估算模型研究

以ASD FieldSpec光谱仪实测了不同生长季的大田玉米、大豆的冠层高光谱与作物的叶面积指数LAI。采用单变量线性与非线性拟合和逐步回归分析的方式,建立了玉米、大豆LAI高光谱遥感估算模型,并对模型的估算结果进行了初步分析。分析结果表明,绿光波段反射峰区、红光波段以及近红外区的单波段反射率与作物的LAI有较强的相关性,而其他波段的反射率与作物的LAI的相关性相对较弱;以高光谱的窄波段构造的NDVI和RVI与作物的LAI的相关程度高,回归模型的预测水平高;而以多波段逐步回归方式构造的统计模型的预测效果最好。     

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。     

基于遥感信息与水稻模型相结合对镇江地区水稻种植面积与产量的估测

为了实现遥感信息与作物模型相结合对镇江地区的水稻种植面积与产量的估测,以便于可以直接利用遥感信息与模型对该地区的水稻生长进行监测,将遥感资料与水稻生产模型(ORYZA2000)相结合,建立遥感数值模拟模型,进行由点及面的区域水稻种植面积及产量的估测。利用遥感数据（8天合成的MODIS和环境小卫星数据）,计算归一化植被指数(NDVI)和增强植被指数(EVI),结合试验区实测的叶面积指数(LAI),建立植被指数与LAI之间的关系,通过模型模拟出的LAI计算出植被指数的浮动值,结合相对应的多时相的遥感数据识别镇江市的水稻,由此可以预报镇江市的水稻种植面积及产量。研究结果表明,模型对水稻生长发育期内的生物量和LAI的模拟较好,水稻LAI与遥感资料计算出的植被指数EVI的幂函数拟合性较好,可以应用这种相关模式识别水稻,并结合ORYZA2000模型提高区域范围的水稻估测精度,同时也体现了遥感信息与作物模型相结合可以很好的监测区域内水稻的生长情况,取得较好的模拟效果。     

基于穗层反射光谱的小麦籽粒蛋白质含量监测的研究

本试验对近地面高光谱仪监测小麦的不同测定方法进行了探讨，并比较了各方法对籽粒蛋白质含量（GPC）的预测能力。结果表明，穗全氮含量（ETNC）与穗层光谱反射率（Rel）的相关系数普遍高于与冠层光谱反射率（Rc）的相关系数。同时，基于小麦光谱反射率、穗全氮含量、籽粒蛋白质含量三者之间的相关性，选择了包括植被指数在内的20个穗层光谱特征参量，与ETNC进行相关分析，建立了最佳光谱特征参量预测ETNC以及ETNC预测籽粒蛋白质含量（GPC）的统计相关模型。通过2个模型的链接，建立了利用比值植被指数RVI[890,670]预测GPC的回归模型，可以较好地预测小麦籽粒蛋白质含量。在相同条件下，相对于以往基于冠层光谱的方法，基于穗层光谱的RVI[890,670]对GPC的预测表现出较大的优势，决定系数R2由0.662提高到0.865，总均方根差RMSE由0.851降低到0.734。本研究为实现田间条件下小麦氮素及相关品质指标的便携式监测仪的开发研制提供了初步的依据。     

棉花形态发生和叶面积指数的模拟模型

棉花形态发生的模拟以每日生理热效应为主要驱动因子,同时考虑了地膜覆盖后地温升高对气积温的补偿作用。叶面积指数的模拟基于棉花LAI动态消长与物质流的库源关系,量化了棉花生育过程中源或库不足时的LAI增长过程。通过对模型参数的校正和核实,使模型适应于不同类型品种的形态发生和LAI动态模拟。结果表明,叶片数、株高、果枝和果节的模拟精度均差方根(RMSE)分别达到了0.7片、1.6cm、0.8个和3.3个;叶面积指数在不同品种和管理方式下的模拟精度RMSE达到了0.3,表现出较好的吻合度和适用性。     

Performance of DSSAT-Nwheat across a wide range of current and future growing conditions

Crop models are widely used in agricultural impact studies. However, many studies have reported large uncertainties from single-model-based simulation analyses, suggesting the need for multi-model simulation capabilities. In this study, the APSIM-Nwheat model was integrated into the Decision Support System for Agro-technology (DSSAT), which already includes two wheat models, to create multi-model simulation capabilities for wheat cropping systems analysis. The new model in DSSAT (DSSAT-Nwheat) was evaluated using more than 1000 observations from field experiments of 65 treatments, which included a wide range of nitrogen fertilizer applications, water supply (irrigation and rainout shelter), planting dates, elevated atmospheric CO₂ concentrations, temperature variations, cultivars, and soil types in diverse climatic regions that represented the main wheat growing areas of the world.DSSAT-Nwheat reproduced the observed grain yields well with an overall root mean square deviation (RMSD) of 0.89 t/ha (13%). Nitrogen applications, water supply, and planting dates had large effects on observed biomass and grain yields, and the model reproduced these crop responses well. Crop total biomass and nitrogen uptake were reproduced well despite relatively poor simulations of observed leaf area measurements during the growing season. The low sensitivity of biomass simulations to poor simulations of leaf area index (LAI) were due to little changes in intercepted solar radiation at LAI >3 and water and nitrogen stress often limiting photosynthesis and growth rather than light interception at low LAI.The responses of DSSAT-Nwheat to temperature variations and elevated atmospheric CO₂ concentrations were close to observed responses. When compared with the two other DSSAT-wheat models (CERES and CROPSIM), these responses were similar, except for the responses to hot environments, due to different approaches in modeling heat stress effects.The comprehensive evaluation of the DSSAT-Nwheat model with field measurements, including a comparison with two other DSSAT-wheat models, created a multi-model simulation platform that allows the quantification of model uncertainties in wheat impact assessments.     

CO2 and ozone effects on canopy development of potato crops across Europe

This paper describes the effects of elevated CO₂ (550 and 680 μl l⁻¹) and O₃ (60 nl l⁻¹ O₃ as an 8 h mean), alone or in combination, on canopy development and senescence in potato (Solanum tuberosum L. cv Bintje) across a range of European agro-climatic conditions. The assessments were made within the European CHIP project (CHanging climate and potential Impacts on Potato yield and quality) that was conducted for two growing seasons (1998 and 1999) in free air CO₂ enrichment systems (FACE) and open-top chamber facilities (OTCs) at seven European sites. A comparison of chambered and unchambered experimental plots was included to examine the effects of chamber enclosure. Phenological growth stages, plant height, leaf area index (LAI) and the number of green and yellow leaves were recorded non-destructively throughout the growing season and by a destructive intermediate harvest at maximum leaf area (MLA). In the dynamic growth analysis CO₂ and O₃ effects were studied over three developmental stages: canopy expansion, full canopy and canopy senescence. Chamber enclosures promoted potato crop development (taller plants, more leaves) during the initial growth stages and led to a faster decline of LAI and a higher number of yellow leaves. The growth in ambient plots varied between sites and seasons, as did the scale of the treatment responses. Despite the large background variation, some overall treatment effects could be detected across all sites. Both levels of increased CO₂ reduced final plant height in comparison to ambient concentrations, which indicates a premature ending of the active plant growth. At the stage of full canopy and crop senescence the average number of green leaves was significantly (P<0.05) decreased by 680 μl l⁻¹ CO₂ (OTC experiments) and LAI showed the same tendency (P=0.07). As there was however no indication of a decreased leaf formation during initial growth and at full canopy, this must have been due to an earlier leaf fall. In the FACE experiments LAI had already began to decline at the stage of full canopy at 550 μl l⁻¹ CO₂ but not in ambient CO₂ (DAE×CO₂, P<0.05). These observations strongly indicated that elevated CO₂ induced a premature senescence during full canopy. O₃ did not have an overall detrimental effect on crop development during initial growth nor at full canopy, but did induce a faster reduction of LAI during crop senescence (DAE×O₃, P<0.05). Final plant height was not affected by O₃. There were few CO₂×O₃ interactions detected. There was a suggestion (P=0.06) that O₃ counteracted the CO₂-induced decrease of green leaves at full canopy, but on the other hand during crop senescence the decline of LAI due to elevated O₃ was faster at ambient compared to elevated CO₂ (P<0.05). These responses of canopy development to elevated CO₂ and O₃ help to explain the treatment responses of potato yield within the CHIP project at sites across Europe.     

遥感信息与棉花模型结合反演模型初始值和参数的方法研究

通过将遥感信息(叶面积指数LAI)与棉花模型结合,建立了遥感-棉花反演模型,用以反演棉花模型所需的初始数据和参数,解决模式在从单点扩展到区域应用时缺少初始输入的问题。反演的参数为播种期、种植密度、生育期施氮量和灌溉量。通过对反演模型检验,初步确定反演模型是正确的。另外,在区域上初步应用的结果表明,反演的参数和模拟的产量与实际情况吻合较好。     

Using Digital Image Analysis to Describe Canopies of Winter Oilseed Rape (Brassica napus L.) during Vegetative Developmental Stages

In our experiment digital image processing is used to predict characteristics in a winter oilseed rape canopy. A large series of images was taken in 2002–2003 in close intervals from a measuring area of 1 m². These images were automatically evaluated by a self‐written computer program analysing the red/green/blue colour channels of each pixel. The number of determined green pixels was then related to the total pixel count of the image. Image evaluation helped to determine canopy structure by digital image analysis subjected to several applications, i.e. soil coverage, leaf area index (LAI), dynamics of plant number during vegetative developmental stages including entire winter season. Furthermore, number of plants per m² and position of each plant were determined by image analysis. Results show that all parameters are dynamic during the vegetative developmental stages (germination–beginning of flowering) mainly depending on temperature. During the vegetative developmental stages number of plants varied. Emergence lasted 30 days resulting in large differences in growth and development of individual plants. During winter number of plants decreased due to longer phases of frost. Plant growth indicated by dynamics of LAI alternated with phases of cessation due to low temperatures above zero or frost. Reductions in soil coverage and LAI clearly started at daily mean temperatures below 5 °C. After the analysis, differences in LAI as well as changes in number of plants during the early phase crop development can serve (i) as input parameters to growth models, (ii) to improve canopy reflectance measurements by separating spectral signatures of soil and canopy and (iii) to determine and explain heterogeneities within the canopy.     

基于GF-1/2卫星数据的冬小麦叶面积指数反演

叶面积指数(leaf area index,LAI)是监测作物生长状况的重要参数,准确、快速、大面积估算LAI不仅有助于更好地监测农作物,而且也有助于其在建模、总体作物管理及精准农业中的应用。本研究为了利用国产遥感影像快速、大面积反演冬小麦LAI,以GF-1/2影像作为数据源,提取常用植被指数,结合不同生育期(起身期、拔节期、开花期)实测LAI数据,建立反演冬小麦LAI的单变量和多变量经验模型,并对其进行验证。结果表明,GF-1起身期、GF-1拔节期以及GF-1开花期提取的植被指数中,MSR(modified simple ratio)、GNDVI(green normalized difference vegetation index)、EVI(enhanced vegetation index)与LAI间的相关系数最大,分别为0.708、0.671和0.743,说明这些植被指数与冬小麦LAI间的相关性较显著;GF-1不同生育期的反演模型相比,基于拔节期GNDVIGF-1建立的二次多项式模型和基于开花期EVIGF-1、GSRGF-1(green simple ratio)、NDVIGF-1(normalized difference vegetation index)建立的PLSR(partial least squares regression)模型R2最大,均为0.783,PLSR模型的RMSE小于二次多项式模型,说明该多变量模型的稳定性优于单变量模型;同一个生育期不同影像相比,基于GF-2的NDVIGF-2建立的二次多项式模型和基于NDVIGF-2、MSRGF-2、SAVIGF-2(soil-adjusted vegetation index)建立的PLSR模型精度高于基于GF-1的2种模型,R2分别为0.768和0.809;不同生育期反演的LAI分布图表明,LAI反演值与实测LAI值基本吻合。以上研究结果说明国产高分辨率遥感影像在农作物生理参数反演中有一定的应用价值,可以为以后的相关研究提供一定的参考。     

干旱对农业生态系统影响研究进展

笔者综述了国内外近10 年干旱对农业生态系统的影响研究,国内外学者主要基于田间试验观测,从细胞微观、单株作物、站点尺度较好地研究了干旱对农作物干物质合成、分配的影响机制,分析了不同生长阶段干旱对作物生长的影响差异。干旱明显降低了作物气孔导度,影响了ATP 合成酶的活性,降低了光合作用效率。在部分作物生长前期,干旱会适当地增加干物质向根部的分配比例,提高水分利用效率;但在大部分作物生长中后期,干旱将直接影响作物地上生物量合成,降低作物产量。但是,干旱对农业生态系统的区域性研究稍显不足。干旱影响范围广,持续时间长,受灾地区农作物品种复杂。单一站点田间试验观测不能反映干旱的区域性影响,且在大范围下开展多站点田间实时观测耗时费力。遥感可以实现干旱影响的大范围监测,但却不足以反映干旱对不同作物的影响差异。随着计算机模拟技术的不断发展,基于田间试验观测构建的作物生长模型能有效克服田间试验的时空局限性,且通过不同的作物模型或参数设置,能够较为准确地反映干旱对不同作物的影响差异,成为开展干旱对农业生态系统影响的重要手段。     

Diagnosis tool for plant and crop N status in vegetative stage: Theory and practices for crop N management

The environmental constraints to agriculture imply that nitrogen (N) fertilizer management should be adjusted to crop N requirements determined by target yields. Nowadays for environmental and economical reasons target yield of farmers can be lower than the potential crop yields as permitted by soil and climatic conditions. So it is important to provide farmers crop N status diagnostic tools in order to decide the rate and the timing of N fertilizer applications. Theory on crop N uptake and allocation allows the determination of a diagnostic tool, the Nitrogen Nutrition Index, based on the determination of the critical N dilution curve for each crop species considered. During the vegetative growth period of all the crop species studied, including C3 and C4 species and monocots and dicots, plant N concentration decreases monotonically as crop grows because of (i) the ontogenetic decline in leaf area per unit of plant mass, and (ii) the remobilisation of N from shaded leaves at the bottom of the canopy to well illuminated leaves at the top. NNI appears then as an indicator well connected with the physiological regulation of N uptake at canopy level. So this indicator can be used as the basis for determination of crop N nutrition status, and then for decision making on the necessity of an N application for achieving target yield. Nevertheless despite its high physiological relevance, NNI cannot be used directly in farm conditions because its determination is very time consuming. So it is necessary to develop indirect methods for NNI estimation through more operational procedures. Several methods have been proposed in literature, such as nitrate concentration in sap or chlorophyll meter. But the calibration or validation of these methods with NNI have not been always made and, when they have been, they did not give univocal relationships, showing a strong dependence of the relationship with cultivar and environment, that limits considerably the relevance of such diagnostic tools in a large range of situations. Easier to use is the indirect estimation of crop NNI by remote sensing measurements. This method allows the estimation of both actual crop mass, through LAI estimation and crop N content, through crop chlorophyll content. The possibility to have repeated estimations of crop NNI during the period of vegetative growth would allow a dynamic diagnostic tool of crop N status. The coupling of indirect measurements of crop N status with dynamic models of crop growth and development should allow a very promising method for crop N diagnostics for decision tools in N fertilization.     

棉花叶绿素密度和叶片氮积累量的高光谱监测研究

利用非成像高光谱仪,获取棉花不同品种、不同密度冠层关键生育时期的反射光谱数据,应用光谱多元统计分析技术,研究表明,棉花冠层叶绿素密度（CH.D）和叶片氮积累量（LNA）分别在反射光谱762 nm和763 nm处的相关系数达最大值（R_CH.D= 0.8845**和R_LNA= 0.7870**,n = 47）;而一阶微分光谱数据对CH.D、LNA最敏感的波段均发生在750 nm处(R_CH.D= 0.9098**和R_LNA = 0.9164**,n = 47);采用47个建模样本的一阶微分光谱750 nm处的数值与棉花冠层CH.D建立线性相关模型方程,估算47个检验样本的棉花冠层CH.D,再根据CH.D与LNA建立的线性相关方程估算检验样本的LNA,47个检验样本的实测LNA与估测LNA极显著线性相关（R = 0.8982**,n = 94）,模型方程的估算精度达86.3%,实测值与估算值的R_MSE = 1.0155,相对误差为0.1380。说明基于高光谱数据的棉花冠层叶绿素密度的遥感估测,可以间接用于棉花冠层叶片氮积累量的监测研究。     

Identifying nitrogen-efficient potato cultivars for organic farming

In organic farming, nitrogen efficiency of potato might vary among cultivars, even within the same maturity type. We therefore analysed in depth the response to nitrogen of a diverse set of cultivars, grown at different locations (differing in soil type and management) and in four years (differing in temperature and rainfall patterns). Yield increased with an increase in nitrogen supply and with growing later cultivars if the crop cycle lasted long enough. When crops had to be flamed to prevent spread of late blight, late cultivars yielded less than early cultivars, especially under high nitrogen. By measuring the fraction of soil covered by green leaves throughout the growing season and using a model, we analysed canopy development in detail and related nitrogen and genotype sensitive model parameters to tuber yield. In one year with early, temporary drought, model prediction was poor. We observed that cultivars that rapidly established a high maximum soil cover, maintained that maximum for long and senesced slowly, could sustain high yields. When late-blight infection was late, these (mid)-late cultivars showed high agronomic nitrogen use efficiency, but were not (always) high in nitrogen uptake efficiency, accumulation of nitrogen in the tubers or nitrogen utilisation efficiency. When late-blight infection started early, early or mid-late cultivars that rapidly established a high maximum soil cover under low nitrogen availability gave best performance. In most years, early canopy development is responsive to nitrogen, shows genetic variation, and is significantly related to early tuber yield. Nitrogen-efficient cultivars suitable for organic production should have rapid early canopy development, a high agronomic nitrogen use efficiency and nitrogen utilisation efficiency, but a low nitrogen concentration in the tubers.     

基于高光谱数据提取土壤养分信息的研究进展

主要综述了利用高光谱遥感技术提取土壤养分信息的研究进展,论述了当前国内外学者通过高光谱遥感技术获取土壤养分信息的研究现状、存在的问题以及应用前景展望。旨在探索新疆绿洲农区如何应用高光谱遥感技术,分析、模拟、评价、预测土壤养分含量,促进高光谱遥感技术在田间农情监测、土壤诊断、水肥分区管理、作物科学种植中的应用,为新疆及兵团实施精准农业提供科学理论参考。     

An overview of the crop model

is a model that has been developed at INRA (France) since 1996. It simulates crop growth as well as soil water and nitrogen balances driven by daily climatic data. It calculates both agricultural variables (yield, input consumption) and environmental variables (water and nitrogen losses). From a conceptual point of view, relies essentially on well-known relationships or on simplifications of existing models. One of the key elements of is its adaptability to various crops. This is achieved by the use of generic parameters relevant for most crops and on options in the model formalisations concerning both physiology and management, that have to be chosen for each crop. All the users of the model form a group that participates in making the model and the software evolve, because is not a fixed model but rather an interactive modelling platform. This article presents version 5.0 by giving details on the model formalisations concerning shoot ecophysiology, soil functioning in interaction with roots, and relationships between crop management and the soil–crop system. The data required to run the model relate to climate, soil (water and nitrogen initial profiles and permanent soil features) and crop management. The species and varietal parameters are provided by the specialists of each species. The data required to validate the model relate to the agronomic or environmental outputs at the end of the cropping season. Some examples of validation and application are given, demonstrating the generality of the model and its ability to adapt to a wide range of agro-environmental issues. Finally, the conceptual limits of the model are discussed.