Protein-rich legume and pseudo-cereal crop suitability under present and future European climates

Abstract Replacing animal proteins with plant proteins in diets has been demonstrated to have both health and environmental advantages, driving a debate about the potential of protein-rich crops as dietary replacements for animal products. However, there is a lack of knowledge on how climate change could influence the potential for producing protein-rich crops. This study addresses this knowledge gap for the European Union. We analysed 13 protein-rich crops, using the crop suitability model EcoCrop and climate projections for the 2050s, based on 30 Global Circulation Models, under the Representative Concentration Pathway 4.5. The results suggest that current protein-rich crop distributions reflect climatic suitability. We demonstrate the heterogeneous impacts of climate change on crop suitability. In general, conditions in northern Europe were modelled to become more favourable for protein-rich crops, while in southern Europe modelled future climates limit the production of traditional protein-rich crops commonly grown there, including chickpea and lentil. Model results show an expanded area of high suitability for quinoa. Our results confirm the need for concerted breeding and research planning strategies to improve the tolerance of faba bean, lentil, and chickpea to the abiotic stresses that are predicted to become more common with climate change. At the same time, production in northern Europe can benefit from experimentation with protein-rich crops predicted to become more suitable there. Production planning and agricultural policy should consider these likely impacts, to encourage shifts that follow the emerging geographic patterns of crop suitability, and to support the resilience of protein-rich crop production in regions that may be negatively impacted by climate change

Quantifying the risk of deforestation in Latin America and the Caribbean

Latin American and Caribbean countries have seen considerable deforestation due to a complex web of interconnected and interdependent causes, which include agricultural expansion, infrastructure development, social demographics and governmental policies and activity. It is necessary for successful and efficient policy development to understand how variability in these causes can potentially result in increased or decreased deforestation. The purpose of this study is to develop a tool that can quantify the risk, as in the threat or pressure, of potential deforestation, whilst identifying the key indicators that contribute to this risk. This tool will take the form of a composite index that will provide spatial and temporal trends of deforestation risk across Latin America and the Caribbean. The development of the Deforestation Risk Index (DRI) was based upon work performed in the EU project ROBIN1. Indicators of deforestation included in the index were identified based upon the multi-scalar approach adopted in ROBIN- nationally from principal component analysis and econometric modelling, provincially from extensive interviews with experts and farmers (subsistence and commercial) in Amazonian regions of Bolivia and Brazil, and locally from stakeholder workshops in Bolivia, Brazil and Mexico. The identification process was supported by an extensive literature review. In total, 11 indicators were identified and grouped into four components (biophysical, economic, governance and social) capable of explaining the risk of deforestation in Latin America and Caribbean countries

Analysis of current patterns of deforestation in Latin America and the Caribbean

Climate change is a major threat to human and natural systems, and particularly to the functionality of ecosystems and the services they provide (IPCC, 2014). Tropical deforestation contributes to 12?14% of global greenhouse gas emissions (Harris et al., 2014). Deforestation also reduces the capacity of forests as key above ground sinks of carbon, and has considerable effects upon biodiversity (Peres et al., 2010 and Pereira et al., 2012). Forest conservation and management offers a strategy for climate change mitigation through restoration of the capacity of forest carbon sequestration

Validating high frequency deployment of the Diet Quality Questionnaire

In recent work, Manners et al. (2022) crowdsourced the Diet Quality Questionnaire (DDQ), assessing whether a lean and low-cost data collection system could be deployed for mapping of diet quality. In 52 weeks of data collection, the system generated responses from more than 80,000 unique respondents, collecting around 1800 respondents per week. The preliminary success of the piloted system points towards a viable alternative modality for deployment for the DQQ. Crowdsourcing data is an attractive option for the DQQ, generating data at a relatively low-cost. The scaling potential of a high-frequency, crowdsourced based system is evidenced by a second pilot launching imminently in Guatemala. However, there remain questions regarding the accuracy and reliability of crowdsourced data- respondents may inaccurately respond intentionally (for malicious purposes or gaming of the system), or unintentionally (due to a lack of understanding). Validation of crowdsourced data has been done via simple phone based follow ups, to more complex machine learning frameworks. Despite the uncertainties around crowdsourced data, crowdsourcing may provide respondents with a sense of anonymity, responding more accurately, without the feeling of enumerator expectations. Enumerator biases have been well documented in enumerator administered data collection, where respondents may adapt responses based upon their perceptions of what they think the enumerator wants to hear. Enumerator and mobile phone generated diet quality data may be hindered by different issues of reliability and accuracy. Previous studies have sought to address similar problems of comparing different technologies, through observational benchmarking (e.g. Matthys et al., 2007; Fallaize et al., 2014; Putz et al., 2019). In a recent study, Rogers et al. (2021) assessed the accuracy of two dietary recall data collection methods, against a weighed food record. The application of this method permitted a quantitative dietary benchmark to be established, through enumerator observation of consumption. This benchmark was used to compare the accuracy and reliability of the data collection methods under study

High-frequency collection of food systems data: Pilots in Rwanda and Guatemala

As a part of CGIAR Research Initiative on Digital Innovation's Work Package 4, "Real-time Monitoring," digital agriculture and food systems scientists at IITA and Alliance of Bioversity International and CIAT engaged with demand partners in Rwanda and Guatemala to support their timely food security and risk management decisions using high-frequency data. Pilots were developed to collate high-frequency data on diet quality, food commodity flows, markets, and infrastructure networks and develop unprecedented insights into the dynamics of food environments. This new approach is expected to empower decision-makers with quality data, serving as a basis for early warning systems and timely interventions to address hunger and malnutrition. This report presents preliminary results achieved in 2022

Explanatory Factors for Farm Income Diversity in Kalehe District, South Kivu Province, DR Congo

peer reviewedDespite repeated wars and the persistence of feudal land tenure, the agricultural sector is at the center of economic activity for most rural households in the Democratic Republic of Congo. This study aims to assess the competitivity of the agricultural sector in relation to other sectors of economic activity, such as mining. To achieve this aim, it analyses and compares the agricultural incomes of different farmers. It also compares these incomes with their incomes from other sectors of economic activity. This study paid particular attention to two factors of production which may explain the differences in income between farms, namely access to land and family agricultural work labor. A survey was carried out among the 33 dynamic and efficient farmers selected on the basis of the results of previous research carried out in Kalehe territory, sud-Kivu province. Households selected depend almost entirely on agriculture and their agricultural activity makes up 91% of the overall household income. Statistical analysis suggests two important facts. First, there are no relation between the mode of access to land, family labor and rural households’ income level. Second, it is only the area farmed and the number of fields that have an impact on overall household income

The tricot approach. Guide for large-scale participatory experiments

Triadic comparison of technology options (tricot) is a research methodology that helps farmers to identify the most suitable technologies for the local conditions of their farm. Tricot (read: ‘try-cot’) engages farmers as ‘farmer researchers’ in the testing or validation of new crop varieties and other promising technologies. Tricot is supported by the ClimMob digital platform (https://climmob.net). This guide provides an introduction to tricot and each of the steps in the experimental cycle

Measuring what the world eats: Insights from a new approach

Diet quality is critical for human health. Current diets are the main drivers of ill health and premature mortality, with negative spillover effects on the environment and economy. Monitoring diet quality globally is thus essential for holding decision makers accountable for progress toward global nutrition, health, and development goals. Yet there has been no way of monitoring diet quality in a credible, affordable, and timely way. Gallup, Harvard University, and the Global Alliance for Improved Nutrition teamed up to overcome this challenge by initiating the Global Diet Quality Project. Through this project we have created a new approach that enables countries to track diet quality year to year, seasonally, or even more frequently. The new approach allows users to investigate both people’s overall dietary adequacy and their consumption of foods that protect against or increase risk for noncommuni-cable diseases (NCDs). The project has worked with the Gallup World Poll data collection platform to provide the first round of diet quality data from 2021 for 41 countries, representing two-thirds of the world’s population. The project aims to collect data for 140 countries in the future

Suitability of root, tuber, and banana crops in Central Africa can be favoured under future climates

Context Climate change is projected to negatively impact food systems in Sub-Saharan Africa. The magnitude of these impacts is expected to be amplified by the extensive reliance on rainfed agriculture and the prevalence of subsistence farming. In the Great Lakes Region of Central Africa, smallholder farming households are largely dependent on root, tuber and banana crops. However, the potential impacts of various climate change scenarios on these crops are not well reported. Yet, data-rich insights about the future impacts of climate change on these crops and the adaptive capacity of food systems in the Great Lakes Region is critical to inform research and development investments towards regional climate change adaptation. Objectives We aimed to gain insights of potential impacts of climate change on root, tuber, and banana crops in the Great Lakes Region, specifically investigating changes to localised crop suitability, planting dates, and identifying potential ‘climate-proof’ variety types of each crop for specific geographies. Methods We developed a modified version of the EcoCrop model to analyse the suitability of future climates for four key root, tuber, and banana crops (banana, cassava, potato, and sweetpotato) and a suite of varieties for each (typical, heat-tolerant, drought-tolerant, and early maturing). The model considers only the direct impacts of climate change on crop suitability. It does not consider how climate change impacts crop suitability by affecting the occurrence of extreme weather events or indirect effects on incidence and severity of pest and disease outbreaks. Results and conclusions Our results demonstrate that climate change will be somewhat favourable to root, tuber, and banana-based systems, with only widespread negative impacts seen for potato. These changes should be qualified by the observation that in most cases the environmental suitability for banana, cassava, and sweetpotato will remain constant or improve if farmers shift planting schedules. Location- and crop-dependent shifts to different variety types were found to be effective in improving suitability under future climates. Significance Data driven insights generated from this work can be used as a first step in developing spatially explicit recommendations for both farmers and decision-makers on how to adapt to climate change and plan investment in the research needed to adapt root, tuber, and banana-based livelihoods and systems to those long-term changes

CGIAR’s role in digital extension services

CGIAR’s digital extension services bridge the gap between the development and the adoption of new climate change adaptation strategies. These services include new ways to disperse information on rainfed systems of agriculture, nutrition, pest control, new crop varieties, crop management practices, and more