Land Use Intensification Effects on Soil C Dynamics in Subtropical Grazing Land Ecosystems

The impacts of land intensification on carbon (C) responses are important components of soil organic carbon (SOC) management. Grazing land intensification typically involves the use of highly productive plant species that can support greater grazing pressure, removal of higher proportions of site biomass and nutrients during mechanical harvest or grazing, and increased use of fertilizers, particularly N. Current improved grazing land management strategies are aimed at increasing above-ground biomass yield, with less regard for below-ground C dynamics. Because intensive management affects above- and below-ground C inputs (Schuman et al. 1999; Liu et al. 2011a,b), it can therefore have important implications on the amount and characteristics of SOC stored in grazing lands (Franzluebbers and Stuedemann, 2003; Dubeux et al. 2006; Silveira et al. 2013). The objective of this study was to investigate the long-term impacts of converting native rangeland ecosystems into intensively managed systems on SOC dynamics in subtropical ecosystems

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

Top-Ranked Priority Research Questions for Soil Science in the 21st Century

Soils provide critical support essential for life on earth, regulate processes across diverse terrestrial and aquatic ecosystems, and interact with the atmosphere. However, soil science is constrained by a variety of challenges including decreasing funding prospects and a declining number of new students and young professionals. Hence, there is a crucial need to revitalize the impact, relevance, and recognition of soil science as well as promote collaboration beyond traditionally defined soil science research disciplines. Such revitalization and collaboration may be fostered by a shift from discipline-focused soil science research to cross-disciplinary research approaches and issue-driven research. In this paper, we present the outcomes of an initiative to identify priority research questions as a tool for guiding future soil science research. The collaborative approach involved four stages including (i) survey-based solicitation of questions; (ii) criteria-based screening of submitted candidate questions, (iii) criteria-based ranking of screened questions, and (iv) final revision of top ranked questions. The 25 top ranked research questions emerged from 140 submitted candidate questions within five predetermined thematic areas that represent current and emerging research areas. We expect that the identified questions will inspire both existing and prospective researchers, enhance multi-disciplinary collaboration both within and outside soil science, draw the attention of grant-awarding bodies, and guide soil science research to address pressing societal, agricultural, and environmental challenges. Furthermore, we hope that the approach and findings presented in this paper will advance soil sciences by fostering improved collaboration among soil science practitioners and researchers, as well as with other sciences, policy experts, and emerging professionals (including students) to meet societal needs

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

A quality approach to real-time smartphone and citizen-driven food market price data: The case of Food Price Crowdsourcing Africa (FPCA) in Nigeria

Timely and reliable monitoring of commodity food prices is an essential requirement for the assessment of market and food security risks and the establishment of early warning systems, especially in developing economies. However, data from regional or national systems for tracking changes of food prices in sub-Saharan Africa lacks the temporal or spatial richness and is often insufficient to inform targeted interventions. In addition to limited opportunity for [near-]real-time assessment of food prices, various stages in the commodity supply chain are mostly unrepresented, thereby limiting insights on stage-related price evolution. Yet, governments and market stakeholders rely on commodity price data to make decisions on appropriate interventions or commodity-focused investments. Recent rapid technological development indicates that digital devices and connectivity services are becoming affordable for many, including in remote areas of developing economies. This offers a great opportunity both for the harvesting of price data (via new data collection methodologies, such as crowdsourcing/crowdsensing — i.e. citizen-generated data — using mobile apps/devices), and for disseminating it (via web dashboards or other means) to provide real-time data that can support decisions at various levels and related policy-making processes. However, market information that aims at improving the functioning of markets and supply chains requires a continuous data flow as well as quality, accessibility and trust. More data does not necessarily translate into better information. Citizen-based data-generation systems are often confronted by challenges related to data quality and citizen participation, which may be further complicated by the volume of data generated compared to traditional approaches. Following the food price hikes during the first noughties of the 21st century, the European Commission's Directorate General for International Cooperation and Development (DG DEVCO) started collaborating with the European Commission’s Joint Research Centre (JRC) on innovative methodologies for real-time food price data collection and analysis in developing countries. The work carried out so far includes a pilot initiative to crowdsource data from selected markets across several African countries, two workshops (with relevant stakeholders and experts), and the development of a spatial statistical quality methodology to facilitate the best possible exploitation of geo-located data. Based on the latter, the JRC designed the Food Price Crowdsourcing Africa (FPCA) project and implemented it within two states in Northern Nigeria. The FPCA is a credible methodology, based on the voluntary provision of data by a crowd (people living in urban, suburban, and rural areas) using a mobile app, leveraging monetary and non-monetary incentives to enhance contribution, which makes it possible to collect, analyse and validate, and disseminate staple food price data in real time across market segments.JRC.D.4-Economics of Agricultur

Can a Combination of UAV-Derived Vegetation Indices with Biophysical Variables Improve Yield Variability Assessment in Smallholder Farms?

The rapid assessment of maize yields in a smallholder farming system is important for understanding its spatial and temporal variability and for timely agronomic decision-support. We assessed the predictability of maize grain yield using unmanned aerial/air vehicle (UAV)-derived vegetation indices (VI) with (out) biophysical variables on smallholder farms. High-resolution imageries were acquired with UAV-borne multispectral sensor at four and eight weeks after sowing (WAS) on 31 farmer managed fields (FMFs) and 12 nearby nutrient omission trials (NOTs) sown with two genotypes (hybrid and open-pollinated maize) across five locations within the core maize region of Nigeria. Acquired multispectral imageries were post-processed into three VIs, normalized difference VI (NDVI), normalized difference red-edge (NDRE), and green-normalized difference VI (GNDVI) while plant height (Ht) and percent canopy cover (CC) were measured within georeferenced plot locations. Result shows that the nutrient status had a significant effect on the grain yield (and variability) in NOTs, with a maximum grain yield of 9.3 t/ha, compared to 5.4 t/ha in FMFs. Generally, there was no relationship between UAV-derived VIs and grain yield at 4WAS (r < 0.02, p > 0.1), but significant correlations were observed at 8WAS (r ≤ 0.3; p < 0.001). Ht was positively correlated with grain yield at 4WAS (r = 0.5, R2 = 0.25, p < 0.001) and more strongly at 8WAS (r = 0.7, R2 = 0.55, p < 0.001), while the relationship between CC and yield was only significant at 8WAS. By accounting for within- and between-field variations in NOTs and FMFs (separately), predictability of grain yield from UAV-derived VIs was generally low (R2 ≤ 0.24); however, the inclusion of ground-measured biophysical variable (mainly Ht) improved the explained yield variability (R2 ≥ 0.62, Root Mean Square Error of Prediction, RMSEP ≤ 0.35) in NOTs but not in FMFs. We conclude that yield prediction with UAV-acquired imageries (before harvest) is more reliable under controlled experimental conditions (NOTs), compared to actual farmer managed fields where various confounding agronomic factors can amplify noise-signal ratio

Crowdsourced data reveal threats to household food security in near real-time during COVID-19 pandemic

The COVID-19 pandemic and related lockdown measures have disrupted food systems globally, leading to fluctuations in the prices of some food commodities, from local to national levels. Yet detailed data-driven evidence of the extent, timing, and localization of the impact on food security are rarely available quickly enough or with sufficient granularity to guide policy responses.Non-PRIFPRI4; CRP4DGO; A4NHCGIAR Research Program on Agriculture for Nutrition and Health (A4NH

Are farmers ready to use phone-based digital tools for agronomic advice? Ex-ante user readiness assessment using the case of Rwandan banana farmers

Purpose: Digital extension is widely embraced in African agricultural development, promising unprecedented outcomes and impact. Especially phone-based services attract attention as tools for effective and efficient agricultural extension. To date, assessments of digital extension services are generally ex-post in nature, thus consideration of users and broader systems occurs once an intervention is broadly identified. However, early understanding of user needs, readiness, and relevant context is a prerequisite for successful adoption and sustainable use of digital extension services. We conducted an ex-ante assessment of user readiness (UR) for phone-based services. Design/Methodology/Approach: We developed an ex-ante framework to assess UR, considering capabilities, opportunities, and motivations of targeted users. The case study of Rwandan banana farmers served to verify the UR framework, using survey data from 690 smallholder farmers. Findings: Findings demonstrate limited capacity to access and use phone-based extension services, especially those requiring a smartphone, and a mismatch between expected UR and actual UR, current capabilities and opportunities. Findings provide entry points for designing suitable digital extension projects and interventions, suggesting a need for capacity building. Practical implications: The UR-framework provided understanding about current limitations in farmer readiness for digital extension. This ex-ante approach to explore UR before designing digital interventions for African farmers is recommended. It points at the importance of embedding digital technologies into existing practices and creating blends of ‘digital’ and ‘analogue’ or 'high-tech' and 'low-tech'. Theoretical implications: The UR-framework provides a structured approach to developing pre-intervention insights about users and use-context, supporting informed strategizing and decision-making about digital extension. It is a relevant addition to existing readiness frameworks, participatory design methods, and ex-post intervention performance assessments, as part of a balanced readiness level assessment. Originality/Value: This is the first ex-ante assessment of UR for digital extension services in an African context, and the first attempt to analyse Rwandan farmers’ readiness for digital extension services

Uav-based mapping of banana land area for village-level decision-support in rwanda

Crop monitoring is crucial to understand crop production changes, agronomic practice decision-support, pests/diseases mitigation, and developing climate change adaptation strategies. Banana, an important staple food and cash crop in East Africa, is threatened by Banana Xanthomonas Wilt (BXW) disease. Yet, there is no up-to-date information about the spatial distribution and extent of banana lands, especially in Rwanda, where banana plays a key role in food security and livelihood. Therefore, delineation of banana-cultivated lands is important to prioritize resource allocation for optimal productivity. We mapped the spatial extent of smallholder banana farmlands by acquiring and processing high-resolution (25 cm/px) multispectral unmanned aerial vehicles (UAV) imageries, across four villages in Rwanda. Georeferenced ground-truth data on different land cover classes were combined with reflectance data and vegetation indices (NDVI, GNDVI, and EVI2) and compared using pixel-based supervised multi-classifiers (support vector models-SVM, classification and regression trees-CART, and random forest–RF), based on varying ground-truth data richness. Results show that RF consistently outperformed other classifiers regardless of data richness, with overall accuracy above 95%, producer’s/user’s accuracies above 92%, and kappa coefficient above 0.94. Estimated banana farmland areal coverage provides concrete baseline for extension-delivery efforts in terms of targeting banana farmers relative to their scale of production, and highlights opportunity to combine UAV-derived data with machine-learning methods for rapid landcover classification