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One CGIAR and the Integrated Agri-food Systems Initiative: From short-termism to transformation of the world's food systems.

ABSTRACT: Agri-food systems are besieged by malnutrition, yield gaps, and climate vulnerability, but integrated, research-based responses in public policy, agricultural, value chains, and finance are constrained by short-termism and zero sum thinking. As they respond to current and emerging agri-food system challenges, decision makers need new tools that steer toward multi-sector, evidence-based collaboration. To support national agri-food system policy processes, the Integrated Agri-food System Initiative (IASI) methodology was developed and validated through case studies in Mexico and Colombia. This holistic, multi-sector methodology builds on diverse existing data resources and leverages situation analysis, modeled predictions, and scenarios to synchronize public and private action at the national level toward sustainable, equitable, and inclusive agri-food systems. Culminating in collectively agreed strategies and multi-partner tactical plans, the IASI methodology enabled a multi-level systems approach by mobilizing design thinking to foster mindset shifts and stakeholder consensus on sustainable and scalable innovations that respond to real-time dynamics in complex agri-food systems. To build capacity for these types of integrated, context-specific approaches, greater investment is needed in supportive international institutions that function as trusted in-region 'innovation brokers.' This paper calls for a structured global network to advance adaptation and evolution of essential tools like the IASI methodology in support of the One CGIAR mandate and in service of positive agri-food systems transformation.

SUBMITTER: Govaerts B

PROVIDER: S-EPMC8177634 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Soil microorganisms play a critical role in soil biogeochemical processes, nutrient cycling, and resilience of agri-food systems and are immensely influenced by agronomic management practices. Understanding soil bacterial community and nutrient dynamics under contrasting management practices is of utmost importance for building climate-smart agri-food systems. Soil samples were collected at 0-15 cm soil depth from six management scenarios in long-term conservation agriculture (CA) and climate-smart agriculture (CSA) practices. These scenarios (Sc) involved; ScI-conventional tillage based rice-wheat rotation, ScII- partial CA based rice-wheat-mungbean, ScIII- partial CSA based rice-wheat-mungbean, ScIV is partial CSA based maize-wheat-mungbean, ScV and ScVI are CSA based scenarios, were similar to ScIII and ScIV respectively, layered with precision water & nutrient management. The sequencing of soil DNA results revealed that across the six scenarios, a total of forty bacterial phyla were observed, with Proteobacteria as dominant in all scenarios, followed by Acidobacteria and Actinobacteria. The relative abundance of Proteobacteria was 29% higher in rice-based CSA scenarios (ScIII and ScV) and 16% higher in maize-based CSA scenarios (ScIV and ScVI) compared to conventional-till practice (ScI). The relative abundance of Acidobacteria and Actinobacteria was respectively 29% and 91% higher in CT than CSA based rice and 27% and 110% higher than maize-based scenarios. Some taxa are present relatively in very low abundance or exclusively in some scenarios, but these might play important roles there. Three phyla are exclusively present in ScI and ScII i.e., Spirochaetes, Thermi, and Euryarchaeota. Shannon diversity index was 11% higher in CT compared to CSA scenarios. Maize based CSA scenarios recorded higher diversity indices than rice-based CSA scenarios. Similar to changes in soil bacterial community, the nutrient dynamics among the different scenarios also varied significantly. After nine years of continuous cropping, the soil organic carbon was improved by 111% and 31% in CSA and CA scenarios over the CT scenario. Similarly, the available nitrogen, phosphorus, and potassium were improved by, respectively, 38, 70, and 59% in CSA scenarios compared to the CT scenario. These results indicate that CSA based management has a positive influence on soil resilience in terms of relative abundances of bacterial groups, soil organic carbon & available plant nutrients and hence may play a critical role in the sustainability of the intensive cereal based agri-food systems.

Project description:In 2017-2018, a group of international development funding agencies launched the Crops to End Hunger initiative to modernize public plant breeding in lower-income countries. To inform that initiative, USAID asked the International Food Policy Research Institute and the United States Department of Agriculture's Economic Research Service to estimate the impacts of faster productivity growth for 20 food crops on income and other indicators in 106 countries in developing regions in 2030. We first estimated the value of production in 2015 for each crop using data from FAO. We then used the IMPACT and GLOBE economic models to estimate changes in the value of production and changes in economy-wide income under scenarios of faster crop productivity growth, assuming that increased investment will raise annual rates of yield growth by 25% above baseline growth rates over the period 2015-2030. We found that faster productivity growth in rice, wheat and maize increased economy-wide income in the selected countries in 2030 by 59 billion USD, 27 billion USD and 21 billion USD respectively, followed by banana and yams with increases of 9 billion USD each. While these amounts represent small shares of total GDP, they are 2-15 times current public R&D spending on food crops in developing countries. Income increased most in South Asia and Sub-Saharan Africa. Faster productivity growth in rice and wheat reduced the population at risk of hunger by 11 million people and 6 million people respectively, followed by plantain and cassava with reductions of about 2 million people each. Changes in adequacy ratios were relatively large for carbohydrates (already in surplus) and relatively small for micronutrients. In general, we found that impacts of faster productivity growth vary widely across crops, regions and outcome indicators, highlighting the importance of identifying the potentially diverse objectives of different decision makers and recognizing possible tradeoffs between objectives.

Project description:BackgroundIn India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation.ResultsWe generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08).ConclusionOur study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

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One CGIAR and the Integrated Agri-food Systems Initiative: From short-termism to transformation of the world's food systems.

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