Satellite Data and Machine Learning for Weather Risk Management and Food Security.

The increase in frequency and severity of extreme weather events poses challenges for the agricultural sector in developing economies and for food security globally. In this article, we demonstrate how machine learning can be used to mine satellite data and identify pixel-level optimal weather indices that can be used to inform the design of risk transfers and the quantification of the benefits of resilient production technology adoption. We implement the model to study maize production in Mozambique, and show how the approach can be used to produce countrywide risk profiles resulting from the aggregation of local, heterogeneous exposures to rainfall precipitation and excess temperature. We then develop a framework to quantify the economic gains from technology adoption by using insurance costs as the relevant metric, where insurance is broadly understood as the transfer of weather-driven crop losses to a dedicated facility. We consider the case of irrigation in detail, estimating a reduction in insurance costs of at least 30%, which is robust to different configurations of the model. The approach offers a robust framework to understand the costs versus benefits of investment in irrigation infrastructure, but could clearly be used to explore in detail the benefits of more advanced input packages, allowing, for example, for different crop varieties, sowing dates, or fertilizers.

Results of the BRD CAP project: progress toward identifying genetic markers associated with BRD susceptibility.

The Bovine Respiratory Disease Coordinated Agricultural Project (BRD CAP) is a 5-year project funded by the United States Department of Agriculture (USDA), with an overriding objective to use the tools of modern genomics to identify cattle that are less susceptible to BRD. To do this, two large genome wide association studies (GWAS) were conducted using a case:control design on preweaned Holstein dairy heifers and beef feedlot cattle. A health scoring system was used to identify BRD cases and controls. Heritability estimates for BRD susceptibility ranged from 19 to 21% in dairy calves to 29.2% in beef cattle when using numerical scores as a semi-quantitative definition of BRD. A GWAS analysis conducted on the dairy calf data showed that single nucleotide polymorphism (SNP) effects explained 20% of the variation in BRD incidence and 17-20% of the variation in clinical signs. These results represent a preliminary analysis of ongoing work to identify loci associated with BRD. Future work includes validation of the chromosomal regions and SNPs that have been identified as important for BRD susceptibility, fine mapping of chromosomes to identify causal SNPs, and integration of predictive markers for BRD susceptibility into genetic tests and national cattle genetic evaluations.

Integrated production of xylitol and ethanol using corncob.

Xylitol production from corncob hemicellulose is a popular process in China. Microbial conversion of xylose to xylitol, as a biological process with many advantages, has drawn increasing attention. As a by-product from the manufacturing of xylitol, corncob cellulosic residues are produced in very large amounts and represent an environmental problem. As a result, considering the large amount of xylitol production in China, the conversion of corncob cellulosic residues has become a widespread issue having to be tackled. After the hemicellulose in corncob has been hydrolyzed for xylitol production, the corncob cellulosic residue is porous and can easily be hydrolyzed by cellulases into glucose and further converted to ethanol, another high-added-value chemical. Based on the latest technology advancements in xylitol, cellulase, and ethanol production, the integrated production of ethanol from corncob cellulosic residues appears as a promising way to improve the profit of the whole xylitol production process.