Ecological approaches to human nutrition

Malnutrition affects a large number of people throughout the developing world. Approaches to reducing malnutrition rarely focus on ecology and agriculture to simultaneously improve human nutrition and environmental sustainability. However, evidence suggests that interdisciplinary approaches that combine the knowledge bases of these disciplines can serve as a central strategy in alleviating hidden hunger for the world's poorest. To describe the role that ecological knowledge plays in alleviating hidden hunger, considering human nutrition as an overlooked ecosystem service. We review existing literature and propose a framework that expands on earlier work on econutri-tion. We provide novel evidence from case studies con-ducted by the authors in western Kenya and propose a framework for interdisciplinary collaboration to alleviate hidden hunger, increase agricultural productivity, and improve environmental sustainability. Our review supports the concept that an inte-grated approach will impact human nutrition. We pro-vide evidence that increased functional agrobiodiversity can alleviate anemia, and interventions that contribute to environmental sustainability can have both direct and indirect effects on human health and nutritional well-being. Integrated and interdisciplinary approaches are critical to reaching development goals. Ecologists must begin to consider not only how their field can contribute to biodiversity conservation, but also, the relationship between biodiversity and provisioning of nontraditional ecosystem services such as human health. Likewise, nutritionists and agronomists must recognize that many of the solutions to increasing human well-being and health can best be achieved by focusing on a healthy environment and the conservation of ecosystem services

Quantifying model uncertainty to improve watershed-level ecosystem service quantification: a global sensitivity analysis of the RUSLE

Ecosystem service-support tools are commonly used to guide natural resource management. Often, empirically based models are preferred due to low data requirements, simplicity and clarity. Yet, uncertainty produced by local context or parameter estimation remains poorly quantified and documented. We assessed model uncertainty of the Revised Universal Soil Loss Equation – RUSLE developed mainly from US data. RUSLE is the most commonly applied model to assess watershed-level soil loss. We performed a global sensitivity analysis (GSA) on RUSLE with four dissimilar datasets to understand uncertainty and to provide recommendations for data collection and model parameterization. The datasets cover varying spatial levels (plot, watershed and continental) and environmental conditions (temperate and tropical). We found cover management and topography create the most uncertainty regardless of environmental conditions or data parameterization techniques. The importance of other RUSLE factors varies across contexts. We argue that model uncertainty could be reduced through better parameterization of cover management and topography factors while avoiding severe soil losses by targeting soil conservation practices in areas where both factors interact and enhance soil loss. We recommend incorporating GSA to assess empirical models’ uncertainty, to guide model parameterization and to target soil conservation efforts

Integrating local knowledge and remote sensing for eco-type classification map in the Barotse Floodplain, Zambia

This eco-type map presents land units with distinct vegetation and exposure to floods (or droughts) in three villages in the Barotseland, Zambia. The knowledge and eco-types descriptions were collected from participatory mapping and focus group discussions with 77 participants from Mapungu, Lealui, and Nalitoya. We used two Landsat 8 Enhanced Thematic Mapper (TM) images taken in March 24th and July 14th, 2014 (path 175, row 71) to calculate water level and vegetation type which are the two main criteria used by Lozi People for differentiating eco-types. We calculated water levels by using the Water Index (WI) and vegetation type by using the Normalized Difference Vegetation Index (NDVI). We also calculated the Normalized Burn Ratio (NBR) index. We excluded burned areas in 2014 and built areas to reduce classification error. Control points include field data from 99 farmers’ fields, 91 plots of 100 m2 and 65 waypoints randomly selected in a 6 km radius around each village. We also used Google Earth Pro to create control points in areas flooded year-round (e.g., deep waters and large canals), patches of forest and built areas. The eco-type map has a classification accuracy of 81% and a pixel resolution of 30 m. The eco-type map provides a useful resource for agriculture and conservation planning at the landscape level in the Barotse Floodplain

Big data and multiple methods for mapping small reservoirs: comparing accuracies for applications in agricultural landscapes

Whether or not reservoirs contain water throughout the dry season is critical to avoiding late season crop failure in seasonally-arid agricultural landscapes. Locations, volumes, and temporal dynamics, particularly of small (<1 Mm3) reservoirs are poorly documented globally, thus making it difficult to identify geographic and intra-annual gaps in reservoir water availability. Yet, small reservoirs are the most vulnerable to drying out and often service the poorest of farmers. Using the transboundary Volta River Basin (~413,000 sq km) in West Africa as a case study, we present a novel method to map reservoirs and quantify the uncertainty of Landsat derived reservoir area estimates, which can be readily applied anywhere in the globe. We applied our method to compare the accuracy of reservoir areas that are derived from the Global Surface Water Monthly Water History (GSW) dataset to those that are derived when surface water is classified on Landsat 8 OLI imagery using the Normalised Difference Water Index (NDWI), Modified NDWI with band 6 (MNDWI1), and Modified NDWI with band 7 (MNDWI2). We quantified how the areal accuracies of reservoir size estimates vary with the water classification method, reservoir properties, and environmental context, and assessed the options and limitations of using uncertain reservoir area estimates to monitor reservoir dynamics in an agricultural context. Results show that reservoir area estimates that are derived from the GSW data are 19% less accurate for our study site than MNDWI1 derived estimates, for a sample of 272 reservoir extents of 0.09 to 72 ha. The accuracy of Landsat-derived estimates improves with reservoir size and perimeter-area ratio, while accuracy may decline as surface vegetation increases. We show that GSW derived reservoir area estimates can provide an upper limit for current reservoir capacity and seasonal dynamics of larger reservoirs. Data gaps and uncertainties make GSW derived reservoir extents unsuitable for monitoring reservoirs that are smaller than 5.1 ha (holding ~49,759 m3), which constitute 674 (56%) reservoirs in the Volta basin, or monitoring seasonal fluctuations of most small reservoirs, limiting its utility for agricultural planning. This study is one of the first to test the utility and limitations of the newly available GSW dataset and provides guidance on the conditions under which this, and other Landsat-based surface water maps, can be reliably used to monitor reservoir resources

Collaborative effort to operationalize the gender transformative approach in the Barotse Floodplain

Agricultural interventions that aim at alleviating rural poverty have important gender implications. The paper explores a Gender Transformative Approach recognizing that fishing, post- harvest processing, and trading are all gendered activities. On the Barotse Floodplain (Zambia) women are relegated to perform tasks within less profitable nodes of the fish value chain. The assessment of ecosystem services in a select number of Aquatic Agricultural Systems (AAS) focal communities included women’s and men’s perspectives and diverse provisioning, regulating and cultural ecosystem services.Cultivate Africa’s Future Fund (CULTIAF

Sustainable intensification of agriculture for human prosperity and global sustainability

There is an ongoing debate on what constitutes sustainable intensification of agriculture (SIA). In this paper, we propose that a paradigm for sustainable intensification can be defined and translated into an operational framework for agricultural development. We argue that this paradigm must now be defined—at all scales—in the context of rapidly rising global environmental changes in the Anthropocene, while focusing on eradicating poverty and hunger and contributing to human wellbeing. The criteria and approach we propose, for a paradigm shift towards sustainable intensification of agriculture, integrates the dual and interdependent goals of using sustainable practices to meet rising human needs while contributing to resilience and sustainability of landscapes, the biosphere, and the Earth system. Both of these, in turn, are required to sustain the future viability of agriculture. This paradigm shift aims at repositioning world agriculture from its current role as the world’s single largest driver of global environmental change, to becoming a key contributor of a global transition to a sustainable world within a safe operating space on Earth