A fuzzy clustering approach to delineate agroecozones

Methodology for the Definition of Durum Wheat Yield Homogeneous Zones by Using Satellite Spectral Indices

One of the main questions facing precision agriculture is the evaluation of different algorithms for the delineation of homogeneous management zones. In the present study, a new approach based on the use of time series of satellite imagery, collected during two consecutive growing seasons, was proposed. Texture analysis performed using the Gray-Level Co-Occurrence Matrix (GLCM) was used to integrate and correct the sum of the vegetation indices maps (NDVI and MCARI2) and define the homogenous productivity zones on ten durum wheat fields in southern Italy. The homogenous zones identified through the method that integrates the GLCM indices with the spectral indices studied showed a greater accuracy (0.18–0.22 Mg ha−1 for ∑NDVIs + GLCM and 0.05–0.49 Mg ha−1 for ∑MCARI2s + GLCM) with respect to the methods that considered only the sum of the indices. Best results were also obtained with respect to the homogeneous zones derived by using yield maps of the previous year or vegetation indices acquired in a single day. Therefore, the survey methods based on the data collected over the entire study period provided the best results in terms of estimated yield; the addition of clustering analysis performed with the GLCM method allowed to further improve the accuracy of the estimate and better define homogeneous productivity zones of durum wheat fields.

Identification of Agricultural Management Zones Through Clustering Algorithms with Thermal and Multispectral Satellite Imagery

Precision Agriculture entails the appropriate management of the inherent variability of soil and crops, resulting in an increase of economic benefits and a reduction of environmental impact. However, site-specific treatments require maps of the soil variability to identify areas of land that share similar properties. In order to produce these maps, we propose a cost-efficient method that combines clustering algorithms with publicly available satellite imagery. The method does not require exploring the parcels with any special equipment or taking samples of the soil for laboratory analysis. The proposed method was tested in a case study for three vineyard parcels with topographical dissimilarities. The study compares different spectral and thermal bands from the Landsat 8 satellite as well as vegetation and moisture indices to determine which one produces the best clustering. The experimental results seem promising for identification of agricultural management zones. The findings suggest that thermal bands produce better clustering than those based on the NDVI index.

From Observation to Information: Data-Driven Understanding of on Farm Yield Variation

Agriculture research uses “recommendation domains” to develop and transfer crop management practices adapted to specific contexts. The scale of recommendation domains is large when compared to individual production sites and often encompasses less environmental variation than farmers manage. Farmers constantly observe crop response to management practices at a field scale. These observations are of little use for other farms if the site and the weather are not described. The value of information obtained from farmers’ experiences and controlled experiments is enhanced when the circumstances under which it was generated are characterized within the conceptual framework of a recommendation domain, this latter defined by Non-Controllable Factors (NCFs). Controllable Factors (CFs) refer to those which farmers manage. Using a combination of expert guidance and a multi-stage analytic process, we evaluated the interplay of CFs and NCFs on plantain productivity in farmers’ fields. Data were obtained from multiple sources, including farmers. Experts identified candidate variables likely to influence yields. The influence of the candidate variables on yields was tested through conditional forests analysis. Factor analysis then clustered harvests produced under similar NCFs, into Homologous Events (HEs). The relationship between NCFs, CFs and productivity in intercropped plantain were analyzed with mixed models. Inclusion of HEs increased the explanatory power of models. Low median yields in monocropping coupled with the occasional high yields within most HEs indicated that most of these farmers were not using practices that exploited the yield potential of those HEs. Varieties grown by farmers were associated with particular HEs. This indicates that farmers do adapt their management to the particular conditions of their HEs. Our observations confirm that the definition of HEs as recommendation domains at a small-scale is valid, and that the effectiveness of distinct management practices for specific micro-recommendation domains can be identified with the methodologies developed.

Transportability of confined field trial data for environmental risk assessment of genetically engineered plants: a conceptual framework

It is commonly held that confined field trials (CFTs) used to evaluate the potential adverse environmental impacts of a genetically engineered (GE) plant should be conducted in each country where cultivation is intended, even when relevant and potentially sufficient data are already available from studies conducted elsewhere. The acceptance of data generated in CFTs "out of country" can only be realized in practice if the agro-climatic zone where a CFT is conducted is demonstrably representative of the agro-climatic zones in those geographies to which the data will be transported. In an attempt to elaborate this idea, a multi-disciplinary Working Group of scientists collaborated to develop a conceptual framework and associated process that can be used by the regulated and regulatory communities to support transportability of CFT data for environmental risk assessment (ERA). As proposed here, application of the conceptual framework provides a scientifically defensible process for evaluating if existing CFT data from remote sites are relevant and/or sufficient for local ERAs. Additionally, it promotes a strategic approach to identifying CFT site locations so that field data will be transportable from one regulatory jurisdiction to another. Application of the framework and process should be particularly beneficial to public sector product developers and small enterprises that develop innovative GE events but cannot afford to replicate redundant CFTs, and to regulatory authorities seeking to improve the deployment of limited institutional resources.

Division scheme for environmental management regionalization in China

Based on regional heterogeneity of environmental characteristics and factors influencing those characteristics, environmental units may be classified into homogeneous zones. Then, essential strategies and mitigation measures in each zone may be developed and environmental quality may be holistically improved using a system of environmental management regionalization (EMRZ). Here, an EMRZ scheme for China was devised by outlining regional management using integrated qualitative and quantitative methods. First, the land was subdivided into four environmental management domains using China's three physical geographic domains. Second, using a regionalization indicator system, the environmental characteristics of neighboring provinces in four domains was quantified using the one-dimensional Euclidean distance method; regions with high similarity were merged into one environmental management region (EMR) and the preliminary draft of EMRs was obtained. Based on the preliminary scheme, eight EMRs using prefecture-level administrative regions were obtained through boundary adjustment based on important natural geographical boundaries and existing regional plans. These include the Northeastern China Region, the North China Plain Region, the Loess Plateau Region, Southeast Coast Region, the Middle Reaches of the Yangtze River, the Southwestern China Region, the Northwestern China Region, and the Tibetan Plateau Region. Priority environmental problems of each EMR were discussed. The main contribution of this study is that it develops a novel methodological framework for EMRZ integrating qualitative and quantitative methodologies, and considers the spatial contiguity of each EMR and the spatial integrity of each administrative unit. Future research will focus on priority goals and establishment of suitable environmental management policies for each EMR based on known local environmental problems.

Fuzzy-logic modeling of land suitability for hybrid poplar across the Prairie Provinces of Canada

Determining the feasibility of a large-scale afforestation program is one approach being investigated by the Government of Canada to increase Canada's potential to sequester carbon from the atmosphere. Large-scale afforestation, however, requires knowledge of where it is suitable to establish and grow trees. Spatial models based on Boolean logic and/or statistical models within a geographic information system may be used for this purpose, but empirical environmental data are often lacking, and the association of these data to land suitability is most often a subjective process. As a solution to this problem, this paper presents a fuzzy-logic modeling approach to assess land suitability for afforestation of hybrid poplar (Populus spp.) over the Prairie Provinces of Canada. Expert knowledge regarding the selection and magnitudes of environmental variables were integrated into fuzzy rule sets from which estimates of land suitability were generated and presented in map form. The environmental variables selected included growing season precipitation, climate moisture index, growing degree days, and Canada Land Inventory capability for agriculture and elevation. Approximately 150,000 km2, or 28% of the eligible land base within the Prairie Provinces was found to be suitable for afforestation. Accuracy assessments conducted with fuzzy accuracy methods provided a more descriptive assessment of the resulting land suitability map than figures generated from a more conventional Boolean-based accuracy measure. Modeling, mapping and accuracy assessment issues were identified for future extension of this work to map hybrid poplar land suitability over Canada.