Latin America (LA) has many social indicators similar to those of highly developed economies but most frequently falls midway between least developed countries and industrialized regions. To move forward, LA must address uncontrolled urbanization, agricultural production, social inequity, and destruction of natural resources. We discuss these interrelated challenges in terms of human impact on the nitrogen (N) cycle. Human activity has caused unprecedented changes to the global N cycle; in the past century; total global fixation of reactive N (Nr) has at least doubled.
Although weather data are widely acknowledged to contain measurement errors, the implications of these errors for models that relate weather to yields have not been adequately examined. From statistical theory and applications in many other fields, it is clear that measurement error in a single predictor variable can lead to bias in estimating the effects of that variable, as well as any other correlated predictors.
Statistical studies of rainfed maize yields in the United States and elsewhere have indicated two clear features: a strong negative yield response to accumulation of temperatures above 30 °C (or extreme degree days (EDD)), and a relatively weak response to seasonal rainfall. Here we show that the process-based Agricultural Production Systems Simulator (APSIM) is able to reproduce both of these relationships in the Midwestern United States and provide insight into underlying mechanisms.
Sugarcane area is currently expanding in Brazil, largely in response to domestic and international demand for sugar-based ethanol. To investigate the potential hydroclimatic impacts of future expansion, a regional climate model is used to simulate 5 years of a scenario in which cerrado and cropland areas (~1.1E6 km2) within south-central Brazil are converted to sugarcane. Results indicate a cooling of up to ~1.0°C during the peak of the growing season, mainly as a result of increased albedo of sugarcane relative to the previous landscape.
Field experiments and simulation models are useful tools for understanding crop yield gaps, but scaling up these approaches to understand entire regions over time has remained a considerable challenge. Satellite data have repeatedly been shown to provide information that, by themselves or in combination with other data and models, can accurately measure crop yields in farmers’ fields. The resulting yield maps provide a unique opportunity to overcome both spatial and temporal scaling challenges and thus improve understanding of crop yield gaps.
Successful adaptation of agriculture to ongoing climate changes would help to maintain productivity growth and thereby reduce pressure to bring new lands into agriculture. In this paper we investigate the potential co-benefits of adaptation in terms of the avoided emissions from land use change. A model of global agricultural trade and land use, called SIMPLE, is utilized to link adaptation investments, yield growth rates, land conversion rates, and land use emissions.
Rapid population growth, urbanization and rising incomes will present an unprecedented opportunity for growth of commercial agriculture and agribusiness in coming years. The value of food consumed in urban areas is set to expand by four times to 2030, but given evidence of a continuing decline in competitiveness much of this could be sourced from imports even in countries with an apparent comparative advantage in agriculture.
For decades, earnings from farming in many developing countries, including in Sub-Saharan Africa, have been depressed by a pro-urban and anti-trade bias in own-country policies, as well as by governments of richer countries favoring their farmers with import barriers and subsidies. Both sets of policies reduced global economic welfare and agricultural trade, and almost certainly added to global inequality and poverty and to food insecurity in many low-income countries.