Showing papers by "James S. Gerber published in 2019"

Climate change has likely already affected global food production.

Abstract: Crop yields are projected to decrease under future climate conditions, and recent research suggests that yields have already been impacted. However, current impacts on a diversity of crops subnationally and implications for food security remains unclear. Here, we constructed linear regression relationships using weather and reported crop data to assess the potential impact of observed climate change on the yields of the top ten global crops-barley, cassava, maize, oil palm, rapeseed, rice, sorghum, soybean, sugarcane and wheat at ~20,000 political units. We find that the impact of global climate change on yields of different crops from climate trends ranged from -13.4% (oil palm) to 3.5% (soybean). Our results show that impacts are mostly negative in Europe, Southern Africa and Australia but generally positive in Latin America. Impacts in Asia and Northern and Central America are mixed. This has likely led to ~1% average reduction (-3.5 X 1013 kcal/year) in consumable food calories in these ten crops. In nearly half of food insecure countries, estimated caloric availability decreased. Our results suggest that climate change has already affected global food production.

The vulnerabilities of agricultural land and food production to future water scarcity

Abstract: Rapidly increasing populations coupled with increased food demand requires either an expansion of agricultural land or sufficient production gains from current resources. However, in a changing world, reduced water availability might undermine improvements in crop and grass productivity and may disproportionately affect different parts of the world. Using multi-model studies, the potential trends, risks and uncertainties to land use and land availability that may arise from reductions in water availability are examined here. In addition, the impacts of different policy interventions on pressures from emerging risks are examined. Results indicate that globally, approximately 11% and 10% of current crop- and grass-lands could be vulnerable to reduction in water availability and may lose some productive capacity, with Africa and the Middle East, China, Europe and Asia particularly at risk. While uncertainties remain, reduction in agricultural land area associated with dietary changes (reduction of food waste and decreased meat consumption) offers the greatest buffer against land loss and food insecurity.

Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors

Abstract: Mapping suitable land for development is essential to land use planning efforts that aim to model, anticipate, and manage trade-offs between economic development and the environment. Previous land suitability assessments have generally focused on a few development sectors or lack consistent methodologies, thereby limiting our ability to plan for cumulative development pressures across geographic regions. Here, we generated 1-km spatially-explicit global land suitability maps, referred to as “development potential indices” (DPIs), for 13 sectors related to renewable energy (concentrated solar power, photovoltaic solar, wind, hydropower), fossil fuels (coal, conventional and unconventional oil and gas), mining (metallic, non-metallic), and agriculture (crop, biofuels expansion). To do so, we applied spatial multi-criteria decision analysis techniques that accounted for both resource potential and development feasibility. For each DPI, we examined both uncertainty and sensitivity, and spatially validated the map using locations of planned development. We illustrate how these DPIs can be used to elucidate potential individual sector expansion and cumulative development patterns. Machine-accessible metadata file describing the reported data (ISA-Tab format)

Voluntary sustainability standards could significantly reduce detrimental impacts of global agriculture.

Abstract: Voluntary sustainability standards (VSS) are stakeholder-derived principles with measurable and enforceable criteria to promote sustainable production outcomes. While institutional commitments to use VSS to meet sustainable procurement policies have grown rapidly over the past decade, we still have relatively little understanding of the ( i ) direct environmental benefits of large-scale VSS adoption; ( ii ) potential perverse indirect impacts of adoption; and ( iii ) implementation pathways. Here, we illustrate and address these knowledge gaps using an ecosystem service modeling and scenario analysis of Bonsucro, the leading VSS for sugarcane. We find that global compliance with the Bonsucro environmental standards would reduce current sugarcane production area (−24%), net tonnage (−11%), irrigation water use (−65%), nutrient loading (−34%), and greenhouse gas emissions from cultivation (−51%). Under a scenario of doubled global sugarcane production, Bonsucro adoption would further limit water use and greenhouse gas emissions by preventing sugarcane expansion into water-stressed and high-carbon stock ecosystems. This outcome was achieved via expansion largely on existing agricultural lands. However, displacement of other crops could drive detrimental impacts from indirect land use. We find that over half of the potential direct environmental benefits of Bonsucro standards under the doubling scenario could be achieved by targeting adoption in just 10% of global sugarcane production areas. However, designing policy that generates the most environmentally beneficial Bonsucro adoption pathway requires a better understanding of the economic and social costs of VSS adoption. Finally, we suggest research directions to advance sustainable consumption and production.

Plantation Mapping in Southeast Asia.

Abstract: Plantation mapping is important for understanding deforestation and climate change. While most existing plantation products are created manually, in this paper we study an ensemble learning based framework for automatically mapping plantations in southern Kalimantan on a yearly scale using remote sensing data. We study the effectiveness of several components in this framework, including class aggregation, data sampling, learning model selection and post-processing, by comparing with multiple baselines. In addition, we analyze the quality of our plantation mapping product by visual examination of high resolution images. We also compare our method to existing manually labeled plantation datasets and show that our method can achieve a better balance of precision (i.e., user’s accuracy) and recall (i.e., producer’s accuracy).