Is deeper always better? Evaluating deep learning models for yield forecasting with small data.

Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible deep learning approach to forecast crop yield at the provincial administrative level based on deep 1D and 2D convolutional neural networks using limited data. This approach meets the operational requirements-public and global records of satellite data in an application ready format with near real time updates-and can be transferred to any country with reliable yield statistics.

High temporal resolution of leaf area data improves empirical estimation of grain yield.

Empirical yield estimation from satellite data has long lacked suitable combinations of spatial and temporal resolutions. Consequently, the selection of metrics, i.e.

A simple mathematical model using centred loops and random perturbations accurately reconstructs search patterns observed in desert ants.

This paper describes a new mathematical model that is based on centred loops to reconstruct the "Systematic Search" behaviour of Cataglyphis desert ants. The notable advantage of this model is the combination of simplicity, efficiency and performance. All model input is kept to a minimum, using only parameters that previous research has shown to be available to the animals at all times: distance from the origin, direction of the last step and home vector.

Social capital and transaction costs in millet markets.

In sub-Saharan Africa, transaction costs are believed to be the most significant barrier that prevents smallholders and farmers from gaining access to markets and productive assets. In this study, we explore the impact of social capital on millet prices for three contrasted years in Senegal. Social capital is approximated using a unique data set on mobile phone communications between 9 million people allowing to simulate the business network between economic agents.