Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil

Manabe, Víctor Danilo; Melo, Marcio R. S.; Rocha, Jansle Vieira

doi:10.3390/rs10091322

Cited by 21 publications

(15 citation statements)

References 47 publications

(70 reference statements)

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“…Finally, there is an urgent need to improve government data collection and remote sensing efforts to characterize and assess the management of global pasture and livestock areas . These data are needed to better understand current levels of ICLS adoption, their drivers, and their ecological outcomes (Manabe et al 2018).…”

Section: Resultsmentioning

confidence: 99%

Drivers of decoupling and recoupling of crop and livestock systems at farm and territorial scales

Garrett¹,

Ryschawy²,

Bell³

et al. 2020

E&S

119

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Crop and livestock production have become spatially decoupled in existing commercial agricultural regimes throughout the world. These segregated high input production systems contribute to some of the world's most pressing sustainability challenges, including climate change, nutrient imbalances, water pollution, biodiversity decline, and increasingly precarious rural livelihoods. There is substantial evidence that by closing the loop in nutrient and energy cycles, recoupling crop and livestock systems at farm and territorial scales can help reduce the environmental externalities associated with conventional commercial farming without declines in profitability or yields. Yet such "integrated" crop and livestock systems remain rare as a proportion of global agricultural area. Based on an interdisciplinary workshop and additional literature review, we provide a comprehensive historical and international perspective on why integrated crop and livestock systems have declined in most regions and what conditions have fostered their persistence and reemergence in others. We also identify levers for encouraging the reemergence of integrated crop and livestock systems worldwide. We conclude that a major disruption of the current regime would be needed to foster crop-livestock reintegration, including a redesign of research programs, credit systems, payments for ecosystem services, insurance programs, and food safety regulations to focus on whole farm outcomes and the creation of a circular economy. An expansion of the number of integrated crop and livestock systems field trials and demonstrations and efforts to brand integrated crop and livestock systems as a form of sustainable agriculture through the development of eco-labels could also improve adoption, but would likely be unsuccessful at encouraging wide-scale change without a more radical transformation of the research and policy landscape.

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Section: Resultsmentioning

confidence: 99%

Drivers of decoupling and recoupling of crop and livestock systems at farm and territorial scales

Garrett¹,

Ryschawy²,

Bell³

et al. 2020

E&S

119

View full text Add to dashboard Cite

show abstract

“…However, other vegetation indices have also been widely used and have achieved satisfactory results for temporal pattern recognition of vegetation. For example, Manabe et al [67] studied the temporal patterns of cropland using EVI time-series data and achieved classification based on the TWDTW method. Viana et al [68] combined NDVI and normalized difference water index (NDWI) and used them to identify the characteristics of land-cover types and developed land cover maps.…”

Section: Identification Of Temporal Patternsmentioning

confidence: 99%

Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

Cheng

Wang

2019

Forests

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Efficient methodologies for mapping forest types in complicated mountain areas are essential for the implementation of sustainable forest management practices and monitoring. Existing solutions dedicated to forest-type mapping are primarily focused on supervised machine learning algorithms (MLAs) using remote sensing time-series images. However, MLAs are challenged by complex and problematic forest type compositions, lack of training data, loss of temporal data caused by clouds obscuration, and selection of input feature sets for mountainous areas. The time-weighted dynamic time warping (TWDTW) is a supervised classifier, an adaptation of the dynamic time warping method for time series analysis for land cover classification. This study evaluates the performance of the TWDTW method that uses a combination of Sentinel-2 and Landsat-8 time-series images when applied to complicated mountain forest-type classifications in southern China with complex topographic conditions and forest-type compositions. The classification outputs were compared to those produced by MLAs, including random forest (RF) and support vector machine (SVM). The results presented that the three forest-type maps obtained by TWDTW, RF, and SVM have high consistency in spatial distribution. TWDTW outperformed SVM and RF with mean overall accuracy and mean kappa coefficient of 93.81% and 0.93, respectively, followed by RF and SVM. Compared with MLAs, TWDTW method achieved the higher classification accuracy than RF and SVM, with even less training data. This proved the robustness and less sensitivities to training samples of the TWDTW method when applied to mountain forest-type classifications.

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“…This class could be distinguished using the distance matrix to filter the traditional areas and then perform a second classification only in these pixels. Manabe et al (2018) performed a two stage classification with TWDTW algorithm in crop-livestock systems and achieved interesting results for intensified pastures.…”

Section: Discussionmentioning

confidence: 99%

Mapping Pasture Areas in Western Region of São Paulo State, Brazil

Bonamigo

Oliveira

Lamparelli

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Brazil is one of the largest exporters of cattle meat production. Most of this production is under pasture areas, with different levels of livestock and field management. Remotely sensed images could be interesting tools to detect distinct temporal and spatial patterns of these systems. In this context, classification algorithms have been proposed to use information from satellite images to map different land covers. The Time-Weighted Dynamic Time Warping (TWDTW) is an algorithm that has the advantage of working well with datasets with enough amounts of temporal information and seasonality patterns. In the present work, the TWDTW was performed to classify pasture managements in farms located in Western region of São Paulo State in Brazil for the years 2017 and 2018, as a primary study. It was used Normalized Difference Vegetation Index (NDVI) time series images from Moderate Resolution Imaging Spectroradiometer – MODIS sensor (products MOD13Q1 and MYD13Q) with 250 meters of spatial resolution. In classifications for the years 2017 and 2018, it was observed a predominance of traditional pasture. Total areas of degraded and traditional pasture were very similar between 2017 and 2018. The year of 2017 showed higher spatial distribution of intensified pastures than year 2018. The classification achieved satisfying results with complete accuracy in validation. The information collected from field visits were important to analyse general aspects of the results. Therefore, in this pilot study TWDTW algorithm demonstrated to have potential in differentiating classes of pasture management. Next steps will be to explore the possibilities to classify pasture systems in large areas.

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Framework for Mapping Integrated Crop-Livestock Systems in Mato Grosso, Brazil

Cited by 21 publications

References 47 publications

Drivers of decoupling and recoupling of crop and livestock systems at farm and territorial scales

Drivers of decoupling and recoupling of crop and livestock systems at farm and territorial scales

Forest-Type Classification Using Time-Weighted Dynamic Time Warping Analysis in Mountain Areas: A Case Study in Southern China

Mapping Pasture Areas in Western Region of São Paulo State, Brazil

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