One of the key challenges in the management of pest control in citrus production is ensuring the infestation samples collected in the field are processed in an efficient, effective manner to produce representative digital models that will support the decision-making process associated with planning and monitoring the application of pest control measures. This paper describes a research project that focuses on applying mining and geological tools for pest control in agriculture. Such tools have been successfully used in a pilot-project application for pest control planning and management of different citrus varieties. The pilot-project has been carried out in partnership with a major citrus producer in Brazil. The results indicated a significant improvement in the pest-control decision-making processes, with a significant reduction in the total areas for pest control application, including more than 68% reduction for the P. oleivora pest and over 92% reduction for the P. latus pest. The evaluation of the pilot-project results indicates that the citrus industry would benefit considerably in terms of reducing both operational costs and the impact of the pest control processes on the environment.
The Amazon Rainforest is the scene of a large number of deforestation activities such as artisanal mining, agriculture and timber trade. For the purpose of have reduced environmental impacts, regulatory agencies have attempted to regulate these activities and direct them towards more responsible methods of operation. This paper describes the initiative by monitoring these forest areas located near these regions of deforestation, because the core elements, such as biomass and carbon accumulation of the trees can be adequately monitored against occasional disturbances brought by these activities. The current standard approach in the Amazon is to monitor all the trees of the forest within an area called the transect, also designated as forest inventory, keeping a strict record of their behavior and growth. However, these activities are restricted to control areas that are located in strategic regions and do not represent the whole area to be monitored. This research explores a new methodology based on geostatistics and designed to optimize the sampling, extending the study of much larger forest areas, keeping unchanged the use of human resources unit, and at the same time increase the surface areas of study and to maintain confidence in the results. The proposed methodology allows the selection of the Legal Reserve-RL, to be made according to the actual distribution of carbon accumulation in the forest, instead relying in using area percentage proposed by law and common sense of proprietary / regulatory agencies. This methodology was applied in the Tapajós National Forest (FLONA Tapajós), State of Pará, Brazil, we used the data set available, to optimize the sample and monitor the forest's ability to store carbon. This methodology intends to contribute to reducing the cost of monitoring activities per unit area, increased precision for location RL, and simplifying procedures by applying a set of easy to use tools. The results showed that application of geostatistical studies for determination of RL is a viable procedure, because the structure of the variogram is maintained even with a random sampling suffering decreased to 50% of the area of vegetation, even managing to keep the sampling result the total vegetation cover.
There is a great concern nowadays with the development of new technologies that prioritize environmental impact. This trend also occurs in the agribusiness, where one of the main concerns is the reduction in the use of agrochemicals. Phytosanitary issues have traditionally been a barrier for the increase in productivity of the citrus plants, as the leprosis mite (Brevipalpus phoenicis) responds for approximately 80 % of the pests control costs. The development of new technologies to control existing pests and diseases that affect citrus production is a major challenge of the agribusiness. In this research, software successfully developed for the mining industry has been applied to model the spatial distribution of citrus rust mite (Phyllocoptruta oleiva), broad mite (Plyphagotarsonemus latus) and leprosis mite (Brevipalpus phoenicis) with the objective of improving the planning and management of the localized application of agrochemicals. The resulting analysis have allowed the measurement of spatial variations of these pests and diseases. The spatial variation has then been used as the basis for planning the controlled application of agrochemicals for these pests and diseases. The research has applied geostatistical analysis for the detailed modeling of the regionalized behaviour of citrus pests and diseases according to the spatial positions of the field samples. The results obtained in the research include the improvement of the decisionmaking process of planning the application of agrochemicals and the development of a specific methodology which has been tested and calibrated in a test-implementation at an
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