Deforestation Data Research Articles

Spatiotemporal relationship between agriculture, livestock, deforestation, and visceral leishmaniasis in Brazilian legal Amazon

Visceral leishmaniasis (VL) is an urgent public health concern in Brazil. We evaluated the spatiotemporal distribution of VL to better understand the effects of economic activities related to agriculture, livestock, and deforestation on its incidence in the Brazilian Legal Amazon (BLA). The data on newly confirmed cases of VL in Brazilian municipalities from 2007 to 2020 were extracted from the Brazilian Notifiable Diseases Information System (SINAN) and analyzed. The data on agricultural production (planted area in hectares) and livestock (total number of cattle) were obtained from the Brazilian Institute of Geography and Statistics (IBGE), whereas deforestation data (in hectares) were obtained from the Amazon Deforestation Estimation Project (PRODES). SatScan and the local indicators of spatial association (LISA) were used to identify the spatial and temporal patterns of VL and its relationships with economic and environmental variables. The cumulative incidence rate was found to be 4.5 cases per 100,000 inhabitants. Based on the LISA results, areas with a high incidence of VL and deforestation were identified in the states of Roraima, Pará, and Maranhão. Strengthening deforestation monitoring programs and environmental enforcement actions can help implement public policies to control illegal deforestation and mitigate the socio-environmental vulnerability in the BLA. Therefore, areas identified in this study should be prioritized for controlling VL.

Applicability of pre-trained CNNs in temperate deforestation detection

ABSTRACT Technological advancements have opened up new possibilities for accurately identifying deforested areas and developing effective data collection strategies. Our paper proposes a practical approach to improve deforestation monitoring using satellite images and Convolutional Neural Networks (CNNs) for image classification. To train the CNNs, we used labeled data primarily sourced from the Amazon basin, while images from the Romanian Carpathian mountains were reserved for testing. Building on this foundation, we evaluated various pre-trained CNN architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, GoogLeNet, and Swin Transformer, comparing their performance with a typical CNN architecture. In addition, we refined the performance by testing four ensemble learning methods. We found a decrease of only 10% in accuracy for using the models on data from the temperate area. Our findings contribute to this interdisciplinary field by providing insights into the effectiveness of pre-trained CNNs in classifying satellite images from temperate regions, trained with knowledge from tropical deforestation data. This contribution may open ways for the development of a quasi real-time deforestation monitoring system and serve as a reference for future research in temperate areas.

Relationships between current climate and deforestation on citrus productivity in Northeastern Pará (Eastern Amazon)

This study analyzed the temporal and spatial patterns of the precipitation (PRP) and maximum temperature (TMAX) time series of 37 municipalities proportionally distributed in the northeastern part of Pará (Eastern Amazon), from 1981 to 2018 (37 years). Environmental data on citrus productivity-PROD (oranges and lime) (IBGE), evapotranspiration (ET) and deforestation (PRODES) were used for the last 15 years to identify the impacts of climate change. The non-parametric approach, specifically the Mann-Kendall (MK) test and delta variation in the period 1989-2003 and 2004-2018 (15 years), were applied to estimate annual and seasonal trends and detect variations after using heatmap and boxplots. To compose the climate change study, Principal Component Analysis (PCA) was applied to environmental variables and Principal Component Regression (PCR) was used to test the hypothesis. The results showed an increasing positive trend in the annual and seasonal TMAX time series over 37 years, with the most significant trends in the dry season. The trend results in PRP, on the other hand, showed regions with both negative and positive trend significance. On annual and seasonal scales, regions such as R5 in northeastern Pará showed positive trends. On the other hand, negative trends of decreasing PRP were observed in the coastal areas of Pará (R1 and R3). Based on PCA and PCR results, deforestation data are associated with higher maximum temperatures and orange productivity follows correlated with PRP and ET. In contrast, PRP and ET are considered limiting factors for lime productivity.

DEFORESTATION DETECTION IN THE AMAZON WITH SENTINEL-1 SAR IMAGE TIME SERIES

Abstract. Deforestation has a significant impact on the environment, accelerating global warming and causing irreversible damage to ecosystems. Large-scale deforestation monitoring techniques still mostly rely on statistical approaches and traditional machine learning models applied to multi-spectral, optical satellite imagery and meta-data like land cover maps. However, clouds often obstruct observations of land in optical satellite imagery, especially in the tropics, which limits their effectiveness. Moreover, statistical approaches and traditional machine learning methods may not capture the wide range of underlying distributions in deforestation data due to limited model capacity. To overcome these drawbacks, we apply an attention-based neural network architecture that learns to detect deforestation end-to-end from time series of synthetic aperture radar (SAR) images. Sentinel-1 C-Band SAR data are mostly independent of the weather conditions and our trained neural network model generalizes across a wide range of deforestation patterns of Amazon forests. We curate a new dataset, called BraDD-S1TS, comprising approximately 25,000 image sequences for deforested and unchanged land throughout the Brazilian Amazon. We experimentally evaluate our method on this dataset and compare it to state-of-the-art approaches. We find it outperforms still-in-use methods by 13.7 percentage points in intersection over union (IoU). We make BraDD-S1TS publicly available along with this publication to serve as a novel testbed for comparing different deforestation detection methods in future studies.

Analysis of land cover change due to deforestation at Holiday Resort Nature Park, North Sumatra Province, Indonesia

. Deforestation activities in Indonesia are increasingly becoming a real concern. All of these actions resulted in the loss of 50% of the world's tropical forests, impacting the livelihoods of forest-dependent people. Changes in land cover are common in natural resource use areas. Land change is an important issue for planners and policymakers. One of them is the encroachment of the Holiday Resort Nature Park (HRNP) area of North Sumatra Province, which is a form of forest governance and weak law enforcement. Especially in this area, there are Sumatran elephants (Elephas maximus sumatranus), which are classified as critically endangered species. Analysis of land cover change can be used as material for formulating policies. By utilizing the Geographic Information System (GIS), it will be easier to analyze land cover and land use changes. The purpose of this study was to analyze land cover changes due to deforestation at HRNP. Spatial analysis of cover change using imagery and testing the accuracy of deforestation data, land cover satellite imagery, and verification in the field. The results showed that during the past 30 years, there has been deforestation from secondary dryland forest of 2079.3 ha to forest, namely swamp shrubs covering an area of 30.5 ha (1.5%) and non-forest covering an area of 2048.8 ha (98.6%). The highest deforestation rate occurred from 1996 to 2000 as much as 2027.3 ha (97.5%). The form of land cover due to deforestation is plantations 1990.8 ha (95.7%) and land cover forms 57.8 ha (2.8%). The conclusion shows that deforestation was triggered by weak law enforcement from the beginning until now, thus providing a more realistic opportunity for communities to encroach

Has global deforestation accelerated due to the COVID-19 pandemic?

As the COVID-19 pandemic unfolded, questions arose as to whether the pandemic would amplify or pacify tropical deforestation. Early reports warned of increased deforestation rates; however, these studies were limited to a few months in 2020 or to selected regions. To better understand how the pandemic influenced tropical deforestation globally, this study used historical deforestation data (2004–2019) from the Terra-i pantropical land cover change monitoring system to project expected deforestation trends for 2020, which were used to determine whether observed deforestation deviated from expected trajectories after the first COVID-19 cases were reported. Time series analyses were conducted at the regional level for the Americas, Africa and Asia and at the country level for Brazil, Colombia, Peru, the Democratic Republic of Congo and Indonesia. Our results suggest that the pandemic did not alter the course of deforestation trends in some countries (e.g., Brazil, Indonesia), while it did in others (e.g., Peru). We posit the importance of monitoring the long-term effects of the pandemic on deforestation trends as countries prioritize economic recovery in the aftermath of the pandemic.

Tekanan Kapitalis dan Jatuhbangunnya Wanatani Krui di Sumatra

We present a brief description of the processes that have led to changes in Krui's damar agroforests. To explain these changes, we use the idea of a 'capitalist squeeze', where factors of reproduction and economy - drought, inheritance patterns, and local trade relations - combine with state and agribusiness pressure. We integrate remote sensing data at the historical level of damar agroforestry with deforestation data and small oil palm plantations throughout Krui. In January 2016, we carried out a two-week quick assessment at Krui agroforestry to assess the yield of damar per hectare and the arrival of sawmills paying high prices for resin wood. Participant observation, interviews (n=12), and focus group discussions (n=3) established that previous collaborative efforts had failed to protect the Krui damar forests from extensive felling, mostly by smallholders taking advantage of high prices of damar timber and oil palm. Attention to the capitalist squeeze bring a focus to the need to create social relations that are just, inclusive, and egalitarian in the production of resin itself.

Land tenure drives Brazil’s deforestation rates across socio-environmental contexts

Many tropical forestlands are experiencing changes in land-tenure regimes, but how these changes may affect deforestation rates remains ambiguous. Here, we use Brazil’s land-tenure and deforestation data and quasi-experimental methods to analyze how six land-tenure regimes (undesignated/untitled, private, strictly-protected and sustainable-use protected areas, indigenous, and quilombola lands) affect deforestation across 49 spatiotemporal scales. We find that undesignated/untitled public regimes with poorly defined tenure rights increase deforestation relative to any alternative regime in most contexts. The privatization of these undesignated/untitled lands often reduces this deforestation, particularly when private regimes are subject to strict environmental regulations such as the Forest Code in Amazonia. However, private regimes decrease deforestation less effectively and less reliably than alternative well-defined regimes, and directly privatizing either conservation regimes or indigenous lands would most likely increase deforestation. This study informs the ongoing political debate around land privatization/protection in tropical landscapes and can be used to envisage policy aligned with sustainable development goals.

TENDENTIAL MODELING OF DEFORESTATION IN CAATINGA BIOME IN PIAUÍ STATE

The work aims to simulate the dynamics of deforestation in the area that covers the caatinga biome in the state of Piauí for the next five decades. In the simulation, the program Dinamica EGO was used. Were acquired deforestation data for 2002 and 2008 and spatial data of the explanatory variables of deforestation in the area. Five steps were performed: calculating the transition matrices, determining the weights of evidence, adjusting, validating and projecting the model in the trend scenario. Transition rates for total and annual deforestation resulted in 4.6% and 0.8%, respectively. The weights of evidence revealed that distances up to 600 m from deforested areas influence in the process of transition to deforestation. The variables with the highest weights were altitude, up to 100 meters; areas closer to urban patch and sustainable use units, in addition to land use and cover classes: urban influence, agriculture, livestock. With the simulated images it was observed a reduction of the forest remnants of caatinga area in the state, from 69% in 2008 to 43% in 2070. These results serve as a warning to the public authorities and the population. The proposed methodology can be applied to other Brazilian biomes with different approaches.

Differential Adaptive Potential and Vulnerability to Climate-Driven Habitat Loss in Brazilian Mangroves

Geographic and environmental differences have been identified as factors influencing Brazilian mangrove trees' genetic diversity. Geographically, distinct species have convergent spatial genetic structures, indicating a limited gene flow between northern and southern populations. Environmentally, genomic studies and common garden experiments have found evidence of local adaptations along the latitudinal gradient of the Brazilian coast. However, little is known about how such adaptive heterogeneity could be affected by a rapidly changing climate in the coming decades, and the combination of deforestation and climate-induced habitat loss may affect these forests and their genetic diversity. Here, we applied two genomic-environmental association methods to model the turnover of potentially adaptive alleles for two dominant mangrove trees: Avicennia germinans and A. schaueriana. We analyzed a total of 134 individuals from six populations of A. germinans and 10 populations of A. schaueriana spanning the Brazilian coast from 1 °S to 28 °S. Gradient forest models identified temperature-related variables as the most important predictors for A. germinans outlier loci, whereas both temperature and precipitation were important for A. schaueriana. We modeled allele frequencies and projected them for future climatic scenarios to estimate adaptively driven vulnerability. We assessed climate-driven habitat loss through climate-only distribution models and calculated annual deforestation rates for each sampled region. Finally, to assess the vulnerability of individual populations, we combined the environmental suitability, deforestation data, and adaptive vulnerability projections. For both species, subtropical populations presented a higher vulnerability than equatorial populations to climate-driven habitat loss. We also identified deforestation rates at the sampled sites that were alarmingly higher than the global average mangrove deforestation rate. Our results provide improved estimates of the impacts of ongoing climate change and human-caused habitat loss on the distribution of mangroves and highlight the importance of site-based conservation strategies that consider individual subtropical and equatorial mangrove forests.

What Peace Means for Deforestation: An Analysis of Local Deforestation Dynamics in Times of Conflict and Peace in Colombia

Using Colombia as a case study, this analysis provides insights on deforestation dynamics in times of conflict and peace and the different factors driving these dynamics. We performed time series clustering of yearly deforestation data (2001–2018) from 708 out of 1,122 mainland Colombian municipalities (accounting for 98% of the total deforestation areas in Colombia) and produced regression models using a gradient tree boosting framework (XGBoost) to identify drivers that explain varying, local-level deforestation dynamics. Municipalities were characterized by seven categories of deforestation dynamics, with the Amazon region being largely represented by only four categories and the Andes region displaying all categories of deforestation dynamics. Notably, six of the seven representative categories exhibit substantial increases in deforestation in the years following the peace agreement. The regression analysis revealed that coca cultivation area, number of cattle, and municipality area are the top three drivers of deforestation dynamics at national, regional, and category levels. However, the importance of the different variables varied according to the different spatial dimensions. Results provide further understanding on how the drivers of deforestation change not only at a regional scale, as assumed by much of the current literature about drivers of deforestation, but also at a lower scale of analysis (intraregional and intradepartmental variation in the case of Colombia). Insights from this study can be used to understand deforestation dynamics in other countries experiencing times of conflict and peace and will support decision-makers in creating programs that align actions for peacebuilding, climate change mitigation, and biodiversity conservation more effectively.

The effect of financial crises on deforestation: a global and regional panel data analysis

Managing our transition to sustainability requires a solid understanding of how conditions of financial crisis affect our natural environment. Yet, there has been little focus on the nature of the relationship between financial crises and environmental sustainability, especially in relation to forests and deforestation. This study addressed this gap by providing novel evidence on the impact of financial crises on deforestation. A panel data approach is used looking at Global Forest Watch deforestation data from > 150 countries in > 100 crises in the twenty-first century. This includes an analysis of crises effects on principle drivers of deforestation; timber and agricultural commodities—palm oil, soybean, coffee, cattle, and cocoa. At a global level, financial crises are associated with a reduction in deforestation rates (− 36 p.p) and deforestation drivers; roundwood (− 6.7 p.p.), cattle (− 2.3 p.p.) and cocoa production (− 8.3 p.p.). Regionally, deforestation rates in Asia, Africa, and Europe decreased by − 83, − 43, and 22 p.p, respectively. Drivers behind these effects may be different, from palm oil (− 1.3 p.p.) and cocoa (− 10.5 p.p.) reductions in Africa, to a combination of timber (− 9.5 p.p) and palm oil in Asia. Moreover, financial crises have a larger effect on deforestation in low-income, than upper middle- and high-income countries (− 51 vs − 39 and − 18 p.p. respectively). Using another main dataset on yearly forest cover—the ESA-Climate Change Initiative—a picture arises showing financial crises leading to small global decreases in forest cover (− 0.1 p.p.) with a small agricultural cover increase (0.1 p.p). Our findings point to financial crises as important moments for global deforestation dynamics. Yet, to consolidate benefits on decreasing deforestation, governments need to enhance their sustainable forest management during crisis periods rather than let it slip down national agendas. Finally, to achieve the SDGs related to forests, better global forest cover datasets are needed, with better forest loss/gain data, disturbance history, and understanding of mosaicked landscape dynamics within a satellite pixel.

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

Wildfires drive deforestation that causes various losses. Although many studies have used spatial approaches, a multi-dimensional analysis is required to determine priority areas for mitigation. This study identified priority areas for wildfire mitigation in Indonesia using a multi-dimensional approach including disaster, environmental, historical, and administrative parameters by integrating 20 types of multi-source spatial data. Spatial data were combined to produce susceptibility, carbon stock, and carbon emission models that form the basis for prioritization modelling. The developed priority model was compared with historical deforestation data. Legal aspects were evaluated for oil-palm plantations and mining with respect to their impact on wildfire mitigation. Results showed that 379,516 km2 of forests in Indonesia belong to the high-priority category and most of these are located in Sumatra, Kalimantan, and North Maluku. Historical data suggest that 19.50% of priority areas for wildfire mitigation have experienced deforestation caused by wildfires over the last ten years. Based on legal aspects of land use, 5.2% and 3.9% of high-priority areas for wildfire mitigation are in oil palm and mining areas, respectively. These results can be used to support the determination of high-priority areas for the REDD+ program and the evaluation of land use policies.

The sustainability of indigenous lands in Amapá state

We herein assess population growth in indigenous lands (ILs) Wajãpi, Uaçá, Galibi and Juminã in Amapá State-Brazil, which has influenced deforestation increase. We assumed the hypothesis of no association between demographic density and deforestation because population density in these areas is low. We used population growth, deaths, and deforestation data by considering a historical series (2002-2018). Demographic data have shown that Uaçá and Wajãpi ILs recorded the highest population growth. The highest demographic density was observed for Galibi ILs and the lowest one for Wajãpi ILs. The highest deforestation was observed for Uaçá ILs and the lowest one for Juminã ILs. Therefore, indigenous lands in Amapá State have an essential role in forest conservation.

Reconstructing deforestation patterns in China from 2000 to 2019

Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China.

Interplay of governance elements and their effects on deforestation in tropical landscapes: Quantitative insights from Ecuador

After state-centered and market-centered approaches have driven international development cooperation activities in previous decades, improved governance has now come into the focus as a means to help reversing global trends of tropical deforestation. Yet, “good governance” remains a normative, broad and often underspecified concept consisting of a wide range of elements and implicit value judgements. Specific knowledge is missing on the relative importance of single elements, on their interdependencies and their specific effects. Following an analytical approach, we aimed to investigate if single governance elements affect each other and whether they relate to decreasing deforestation. We conducted a quantitative field study in twelve selected landscapes across 160,000 ha of tropical lowland forest in Ecuador. We mapped governance arrangements and land use in participatory exercises. The performance of single governance elements including tenure, forest management practices, law enforcement, institutions, and participation was quantified based on the governance assessment framework of the World Resource Institute. We assessed context information and used satellite based deforestation data. Principal component analysis showed that all governance elements loaded positively on the first axis. This shows that specific governance elements acted conjointly. They are in general not antagonistic, but interact positively and might reinforce each other. Policy and development work may therefore focus on a smaller number of well-selected governance elements. High performance of specific governance elements, in particular tenure and participation was linked to reduced deforestation. This supports the notion of a number of governance elements as being indeed “good” for low deforestation. This functional understanding draws a more differentiated picture for single governance elements and supports outcome oriented decisions instead of value-oriented principles that underlie “good governance”. Direct deforestation drivers such as agriculture and infrastructure explained larger shares of deforestation as compared to governance. A number of conclusions and recommendations for the specific governance situation in tropical lowland forests of Ecuador are given.

Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.

Advance of soy commodity in the southern Amazonia with deforestation via PRODES and ImazonGeo: a moratorium-based approach

The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo.

Deforestation risk in the Peruvian Amazon basin

SummaryThe prevention of tropical forest deforestation is essential for mitigating climate change. We tested the machine learning algorithm Maxent to predict deforestation across the Peruvian Amazon. We used official annual 2001–2019 deforestation data to develop a predictive model and to test the model’s accuracy using near-real-time forest loss data for 2020. Distance from agricultural land and distance from roads were the predictor variables that contributed most to the final model, indicating that a narrower set of variables contribute nearly 80% of the information necessary for prediction at scale. The permutation importance indicating variable information not present in the other variables was also highest for distance from agricultural land and distance from roads, at 40.5% and 14.3%, respectively. The predictive model registered 73.2% of the 2020 early alerts in a high or very high risk category; less than 1% of forest cover in national protected areas were registered as very high risk, but buffer zones were far more vulnerable, with 15% of forest cover being in this category. To our knowledge, this is the first study to use 19 years of annual data for deforestation risk. The open-source machine learning method could be applied to other forest regions, at scale, to improve strategies for reducing future deforestation.

Deforestation, fires, and lack of governance are displacing thousands of jaguars in Brazilian Amazon

AbstractThe rate of deforestation and the number of large wildfires are increasing in the Amazon. Illegal loggers, miners, ranchers, and farmers all have contributed to this increase. Their activities have dramatic consequences for biodiversity, ecological services, and people. In this study, we estimated the number of jaguars affected by deforestation. We focused on the Brazilian Amazon from August 2016 to December 2019. Further, we analyzed the effects of socio‐geographic determinants of deforestation and state policies. To do so, we used deforestation data from DETER‐B satellite system. The number of jaguars within each deforested area was pulled from a previous study, which provided jaguar abundances for jaguar entire range. We assumed all jaguars within a deforested area were affected (displaced or killed). To determine the underlying causes of jaguar loss, we regressed the number of jaguars lost per state and year against the proportion of total forest area within reserves, distance to forest border, and monetary efficiency in cattle production. We estimate a total of 1,422 jaguars have been displaced/killed in recent years (2016: 488, 2017: 360, 2018: 268, 2019: 354). Only the proportion of protected area had an effect in reducing jaguar deforestation. We discuss how our work could result in near real‐time monitoring of jaguar displacement and how policies such as wood certification, more efficient cattle production, and centralizing governance may be solutions.