Agro-meteorological risks to maize production in Tanzania: sensitivity of an adapted water requirements satisfaction index (WRSI) model to rainfall

The water requirements satisfaction index (WRSI) – a simplified crop water stress model – is widely used in drought and famine early warning systems, as well as in agro-meteorological risk management instruments such as crop insurance. We developed an adapted WRSI model, as introduced here, to characterise the impact of using different rainfall input datasets, ARC2, CHIRPS, and TAMSAT, on key WRSI model parameters and outputs. Results from our analyses indicate that CHIRPS best captures seasonal rainfall characteristics such as season onset and duration, which are critical for the WRSI model. Additionally, we consider planting scenarios for short-, medium-, and long-growing cycle maize and compare simulated WRSI and model outputs against reported yield at the national level for maize-growing areas in Tan- zania. We find that over half of the variability in yield is explained by water stress when the CHIRPS dataset is used in the WRSI model (R2 = 0.52- 0.61 for maize varieties of 120-160 days growing length). Overall, CHIRPS and TAMSAT show highest skill (R2 = 0.46-0.55 and 0.44-0.58, respectively) in capturing country-level crop yield losses related to seasonal soil moisture deficit, which is critical for drought early warning and agro-meteorological risk applications

Machine learning for regional crop yield forecasting in Europe

Crop yield forecasting at national level relies on predictors aggregated from smaller spatial units to larger ones according to harvested crop areas. Such crop areas come from land cover maps or reported statistics, both of which can have errors and uncertainties. Sub-national or regional crop yield forecasting minimizes the propagation of these errors to some extent. In addition, regional forecasts provide added value and insights to stakeholders on regional differences within a country, which would otherwise compensate each other at national level. We propose a crop yield forecasting approach for multiple spatial levels based on regional crop yield forecasts from machine learning. Machine learning, with its data-driven approach, can leverage larger data sizes and capture nonlinear relationships between predictors and yield at regional level. We designed a generic machine learning workflow to demonstrate the benefits of regional crop yield forecasting in Europe. To evaluate the quality and usefulness of regional forecasts, we predicted crop yields for 35 case studies, including nine countries that are major producers of six crops (soft wheat, spring barley, sunflower, grain maize, sugar beets and potatoes). Machine learning models at regional level had lower normalized root mean squared errors (NRMSE) and uncertainty than a linear trend model, with Wilcoxon p-values of 3e-7 and 2e-7 for 60 days before harvest and end of season respectively. Similarly, regional machine learning forecasts aggregated to national level had lower NRMSEs than forecasts from an operational system in 18 out of 35 cases 60 days before harvest, with a Wilcoxon p-value of 0.95 indicating similar performance. Our models have room for improvement, especially during extreme years. Nevertheless, regional crop yield forecasts from machine learning and aggregated national forecasts provide a consistent forecasting method across spatial levels and insights from regional differences to support important policy decisions

ASAP Water Satisfaction Index

This technical report describes the Water Satisfaction Index model that used in the ASAP (Anomaly hotSpots of Agricultural Production) early warning system.JRC.D.5-Food Securit

Can Ethiopia feed itself by 2050? Estimating cereal self-sufficiency to 2050

Producing adequate food to meet global demand by 2050 is widely recognized as a major challenge, particularly for sub-Saharan Africa (SSA) (Godfray et al. 2010; Alexandratos and Bruinsma 2012; van Ittersum et al. 2016). Increased price volatility of major food crops (Koning et al. 2008; Lagi et al. 2011), an abrupt surge in land area devoted to crop production in recent years (Grassini et al. 2013) and extensive labour force mobilization (NEPAD 2013) reflect the powerful forces underpinning this challenge to increase production. The 2008 price spikes triggered the Food and Agriculture Organization of the United Nations (FAO) and the World Food Programme (WFP) to issue warnings, noting the 60–70 percent increase in food production by 2050 that will be needed to meet the escalating food demand for the expected 9.7 billion global population. In this policy brief we focus on the feasibility to meet such increase by 2050 with scenarios of population increase and dietary changes under current climate conditions. Current climate variability is very high in sub-Saharan Africa causing significant yield variations across years (e.g., Shiferaw et al. 2014; www.yieldgap.org). Climate change will further add to the food production challenge (Porter et al. 2014; Vermeulen et al. 2012; McKersie 2015). Smallholder farmers will need to adapt to a changing climate while at the same time they are expected to increase production in such way that it has a minimum effect on the drivers of climate change, i.e. mitigating greenhouse gas emissions

Use of agro-climatic zones to upscale simulated crop yield potential

Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped food production capacity. Reliable location-specific estimates of yield gaps, either derived from research plots or simulation models, are available only for a limited number of locations and crops due to cost and time required for field studies or for obtaining data on long-term weather, crop rotations and management practices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemes for suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimating yield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluated for climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficient CZ scheme should strike an effective balance between zone size and number of zones required to cover a large portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemes with less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain (GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index, temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones. While CZ schemes derived from two climate-related categorical variables require a similar number of zones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-ED for most weather variables that are sensitive drivers of crop production. Some CZ schemes are cropspecific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species

Minimum emission pathways to triple Africa’s cereal production by 2050

Cereals play a central role in food security in sub-Saharan Africa (SSA), where they account for approximately 50% of caloric intake and total crop area. Cereal demand in the region is projected to nearly triple between 2015 and 2050 due to rapid population growth (van Ittersum et al. 2016). Increases in cereal yields are very slow in most SSA countries and agricultural area expansion is still an important means to keep up with the growing demand, causing losses of forests or grasslands, thereby reducing carbon stocks. At the same time the Paris Conference of the Parties (COP21) Agreement aims to keep global warming below 2 °C or even 1.5 °C by 2100. SSA has already seen a continuous increase in emissions from agriculture-driven deforestation between 1990 and 2015. Yet, intensification, i.e. higher yields per hectare with sufficient and judicious use of inputs, will also lead to higher emissions per unit area because of the required fertiliser use. This info note summarizes results of three recent studies that assessed whether SSA can be self-sufficient in cereals by 2050 under different scenarios of intensification on existing cereal area. For each scenario, yield increases and area expansion to meet cereal demand by 2050 were assessed. Increased demands for fertiliser use and associated GHG emissions were quantified