Vulnerabilities, impacts and adaptations to climate change in the agricultural sector and agricultural soils.

2009

Evaluating social and environmental impacts at Embrapa.

A strategic planning process has been implemented at the Brazilian Agricultural Research Agency (Embrapa) to introduce sustainable development objectives in all steps of agricultural Research and Development. An essential component of the institutional mission statement hence devised has called for the systematic assessment of social and environmental impacts (in addition to the traditionally studied economic ones) of all technology innovations resulting from R&D. The proposed approach emphasizes the interest of promoting close interaction between R&D teams and technology-adopting producers, under actual field contexts, in order to improve both the technology development and the demand probing processes. Given the multiplicity of technological applications ensuing from Embrapa?s very broad research encompassment, and the variety of environmental and productive contexts involved, a customized impact assessment system has been proposed. Directed at the appraisal of agricultural technology development research projects (ex-ante) as well as their ensuing innovations (ex-post), the Ambitec-Agro System comprises a set of integrated socio-environmental indicators, constructed in modules suited to Agricultural, Animal husbandry, and Agro-industrial activities, besides a specific module for Social Impact Assessment. The system has been routinely applied in technology appraisal in all of Embrapa?s Units, as a basis for their institutional performance evaluations, and toward the formulation of the annual Social Balance Report. Following the inception of this institutional technology appraisal initiative, several methodological innovations have been proposed within Embrapa, including technical improvements and applicability adaptations of the Ambitec-Agro system, and approaches to further-reaching objectives, such as the sustainable development of rural communities, and the environmental management of agricultural activities.

Field-oriented assessment of agricultural crops through temporal segmentation of modis VI data.

Monitoring agricultural crops constitutes a vital task for the general understanding of land use spatio-temporal dynamics. This paper presents an approach for the enhancement of current crop monitoring capabilities on a regional scale, in order to allow for the analysis of environmental and socio-economic drivers and impacts of agricultural land use. This work discusses the advantages and current limitations of using 250m VI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for this purpose, with emphasis in the difficulty of correctly analyzing pixels whose temporal responses are disturbed due to certain sources of interference such as mixed or heterogeneous land cover. It is shown that the influence of noisy or disturbed pixels can be minimized, and a much more consistent and useful result can be attained, if individual agricultural fields are identified and each field's pixels are analyzed in a collective manner. As such, a method is proposed that makes use of image segmentation techniques based on MODIS temporal information in order to identify portions of the study area that agree with actual agricultural field borders. The pixels of each portion or segment are then analyzed individually in order to estimate the reliability of the temporal signal observed and the consequent relevance of any estimation of land use from that data. The proposed method was applied in the state of Mato Grosso, in mid-western Brazil, where extensive ground truth data was available. Experiments were carried out using several supervised classification algorithms as well as different subsets of land cover classes, in order to test the methodology in a comprehensive way. Results show that the proposed method is capable of consistently improving classification results not only in terms of overall accuracy but also qualitatively by allowing a better understanding of the land use patterns detected. It thus provides a practical and straightforward procedure for enhancing crop-mapping capabilities using temporal series of moderate resolution remote sensing data.