Providing food and other products to a growing human population while safeguarding natural ecosystems and the provision of their services is a significant scientific, social and political challenge. With food demand likely to double over the next four decades, anthropization is already driving climate change and is the principal force behind species extinction, among other environmental impacts. The sustainable intensification of production on current agricultural lands has been suggested as a key solution to the competition for land between agriculture and natural ecosystems. However, few investigations have shown the extent to which these lands can meet projected demands while considering biophysical constraints. Here we investigate the improved use of existing agricultural lands and present insights into avoiding future competition for land. We focus on Brazil, a country projected to experience the largest increase in agricultural production over the next four decades and the richest nation in terrestrial carbon and biodiversity. Using various models and climatic datasets, we produced the first estimate of the carrying capacity of Brazil's 115 million hectares of cultivated pasturelands. We then investigated if the improved use of cultivated pasturelands would free enough land for the expansion of meat, crops, wood and biofuel, respecting biophysical constraints (i.e., terrain, climate) and including climate change impacts. We found that the current productivity of Brazilian cultivated pasturelands is 32–34% of its potential and that increasing productivity to 49–52% of the potential would suffice to meet demands for meat, crops, wood products and biofuels until at least 2040, without further conversion of natural ecosystems. As a result up to 14.3 Gt CO2 Eq could be mitigated. The fact that the country poised to undergo the largest expansion of agricultural production over the coming decades can do so without further conversion of natural habitats provokes the question whether the same can be true in other regional contexts and, ultimately, at the global scale
Recent debate about agricultural greenhouse gases (GHG) emissions mitigation highlights tradeoffs inherent in the way we produce and consume food, with increasing scrutiny on emissionsintensive livestock products 1-3. While most research has focussed on mitigation through improved productivity 4,5 , systemic interactions resulting from reduced beef production at regional level are still unexplored. A detailed optimisation model of beef production encompassing pasture degradation and recovery processes, animal and deforestation emissions, soil organic carbon (SOC) dynamics and upstream lifecycle inventory was developed and parameterized for the Brazilian Cerrado. Economic return was maximized considering two alternative scenarios: Decoupled Livestock Deforestation (DLD), assuming baseline deforestation rates controlled by effective policy; and Coupled Livestock Deforestation (CLD), where shifting beef demand alters deforestation rates. In DLD, reduced consumption actually leads to less productive beef systems, associated with higher emissions intensities and total emissions, while increased production leads to more efficient systems with boosted SOC stocks, reducing both per kg and total emissions. Under CLD, increased production leads to 60% higher emissions than in DLD. The results indicate the extent to which deforestation control contributes to sustainable intensification in Cerrado beef systems, and how alternative life-cycle analytical approaches 6 result in significantly different emission estimates.
Agriculture in Brazil is booming. Brazil has the world's second largest cattle herd and is the second largest producer of soybeans, with the production of beef, soybeans, and bioethanol forecast to increase further. Questions remain, however, about how Brazil can reconcile increases in agricultural production with protection of its remaining natural vegetation. While high hopes have been placed on the potential for intensification of low-productivity cattle ranching to spare land for other agricultural uses, cattle productivity in the Amazon biome (29% of the Brazilian cattle herd) remains stubbornly low, and it is not clear how to realize theoretical productivity gains in practice. We provide results from six initiatives in the Brazilian Amazon, which are successfully improving cattle productivity in beef and dairy production on more than 500,000 hectares of pastureland, while supporting compliance with the Brazilian Forest Code. Spread across diverse geographies, and using a wide range of technologies, participating farms have improved productivity by 30-490%. High-productivity cattle ranching requires some initial investment
Most deforested lands in Brazil are occupied by low-productivity cattle ranching. Brazil is the second biggest meat producer worldwide and is projected to increase its agricultural output more than any other country. Biochar has been shown to improve soil properties and agricultural productivity when added to degraded soils, but these effects are context-dependent. The impact of biochar, fertilizer and inoculant on the productivity of forage grasses in Brazil ( Brachiaria spp. and Panicum spp.) was investigated from environmental and socio-economic perspectives. We showed a 27% average increase in Brachiaria production over two years but no significant effects of amendment on Panicum yield. Biochar addition also increased the contents of macronutrients, soil pH and CEC. Each hectare amended with biochar saved 91 tonnes of CO 2 eq through land sparing effect, 13 tonnes of CO 2 eq sequestered in the soil, equating to U$455 in carbon payments. The costs of biochar production for smallholder farmers, mostly because of labour cost, outweighed the potential benefits of its use. Biochar is 617% more expensive than common fertilizers. Biochar could improve productivity of degraded pasturelands in Brazil if investments in efficient biochar production techniques are used and biochar is subsidized by low emission incentive schemes.
Mathematical models can be used to improve performance, reduce cost of production, and reduce nutrient excretion by accounting for more of the variation in predicting requirements and feed utilization in each unique production situation. Mathematical models can be classified into five or more categories based on their nature and behavior. Determining the appropriate level of aggregation of equations is a major problem in formulating models. The most critical step is to describe the purpose of the model and then to determine the appropriate mix of empirical and mechanistic representations of physiological functions, given development and evaluation dataset availability, inputs typically available and the benefits versus the risks of use associated with increased sensitivity. We discussed five major feeding systems used around the world. They share common concepts of energy and nutrient requirement and supply by feeds, but differ in structure and application of the concepts. Animal models are used for a variety of purposes, including the simple description of observations, prediction of responses to management, and explanation of biological mechanisms. Depending upon the objectives, a number of different approaches may be used, including classical algebraic equations, predictive empirical relationships, and dynamic, mechanistic models. The latter offer the best opportunity to make full use of the growing body of knowledge regarding animal biology. Continuing development of these types of models and computer technology and software for their implementation holds great promise for improvements in the effectiveness with which fundamental knowledge of animal function can be applied to improve animal agriculture and reduce its impact on the environment. Key words: cattle, feeding, nutrient, requirement, supply MODELOS MATEMÁTICOS NA NUTRIÇÃO DE RUMINANTESRESUMO: Modelos matemáticos podem ser utilizados para melhorar a performance, reduzir os custos de produção, e minimizar a exceção de nutrientes através de melhores estimativas da exigência e utilização de alimentos em vários cenários produtivos. Modelos matemáticos podem ser classificados em cinco ou mais categorias dependendo da sua natureza. Um dos maiores problemas na construção de modelos matemáticos é o nível de agregação das equações. Os passos mais importantes são o estabelecimento do propósito do modelo, determinação da melhor combinação de equações empíricas e teóricas para representar das funções fisiológicas dado a disponibilidade de banco de dados, informações tipicamente encontradas a nível de campo, e os benefícios e riscos associados com o uso do modelo na produção animal. Nesse artigo são discutidos cinco sistemas de alimentação padrão de ruminantes mais utilizados atualmente. Eles compartilham de conceitos de exigência e disponibilidade de energia e nutrientes, mas diferem na estrutura e como esses conceitos são abordados. Modelos animais podem ser utilizados para vários propósitos, entre eles uma simples descrição de observações, estimativa...
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