Insights into innovative contract design to improve the integration of biodiversity and ecosystem services in agricultural management

Funding Information: This publication is part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 818190. The authors thank the reviewers for their valuable comments and suggestions, which considerably improved an earlier version of the manuscript. We also appreciate the support of Bettina Matzdorf, C?line Dutilly, Edward Ott, Jennifer Dodsworth, Katarzyna Zagorska, Lisa Deijl, Rena Barghusen, Salomon Espinosa Diaz and Sven Defrijn from the Contracts2.0 team.Peer reviewedPublisher PD

Beyond the plot: technology extrapolation domains for scaling out agronomic science

Open Access Journal; Published online: 14 May 2018Ensuring an adequate food supply in systems that protect environmental quality and conserve natural resources requires productive and resource-efficient cropping systems on existing farmland. Meeting this challenge will be difficult without a robust spatial framework that facilitates rapid evaluation and scaling-out of currently available and emerging technologies. Here we develop a global spatial framework to delineate 'technology extrapolation domains' based on key climate and soil factors that govern crop yields and yield stability in rainfed crop production. The proposed framework adequately represents the spatial pattern of crop yields and stability when evaluated over the data-rich US Corn Belt. It also facilitates evaluation of cropping system performance across continents, which can improve efficiency of agricultural research that seeks to intensify production on existing farmland. Populating this biophysical spatial framework with appropriate socio-economic attributes provides the potential to amplify the return on investments in agricultural research and development by improving the effectiveness of research prioritization and impact assessment

Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa

In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled georeferenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km2. The mean RZ-PAWHC for SSA is 74mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km3) due to soil conditions restricting root zone depth. Of these, 4800 km3 are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km2), and 2500 km3 due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km3, aluminium toxicity by 600 km3, porosity by 120 km3 and alkalinity by 20 km3. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available

Participatory research in times of COVID-19 and beyond: Adjusting your methodological toolkits

Solving grand environmental societal challenges calls for transdisciplinary and participatory methods in social-ecological research. These methods enable co-designing the research, co-producing the results, and co-creating the impacts together with concerned stakeholders. COVID-19 has had serious impacts on the choice of research methods, but reflections on recent experiences of "moving online"are still rare. In this perspective, we focus on the challenge of adjusting different participatory methods to online formats used in five transdisciplinary social-ecological research projects. The key added value of our research is the lessons learned from a comparison of the pros and cons of adjusting a broader set of methods to online formats. We conclude that combining the adjusted online approaches with well-established face-to-face formats into more inclusive hybrid approaches can enrich and diversify the pool of available methods for postpandemic research. Furthermore, a more diverse group of participants can be engaged in the research process

Quantifying trade-offs between future yield levels, food availability and forest and woodland conservation in Benin

Meeting the dual objectives of food security and ecosystem protection is a major challenge in sub-Saharan Africa (SSA). To this end agricultural intensification is considered desirable, yet, there remain uncertainties regarding the impact of climate change on opportunities for agricultural intensification and the adequacy of intensification options given the rapid population growth. We quantify trade-offs between levels of yield gap closure, food availability and forest and woodland conservation under different scenarios. Each scenario is made up of a combination of variants of four parameters i.e. (1) climate change based on Representative Concentration Pathways (RCPs); (2) population growth based on Shared Socioeconomic Pathways (SSPs); (3) cropland expansion with varying degrees of deforestation; and (4) different degrees of yield gap closure. We carry out these analyses for three major food crops, i.e. maize, cassava and yam, in Benin. Our analyses show that in most of the scenarios, the required levels of yield gap closures required to maintain the current levels of food availability can be achieved by 2050 by maintaining the average rate of yield increases recorded over the past two and half decades in addition to the current cropping intensity. However, yields will have to increase at a faster rate than has been recorded over the past two and half decades in order to achieve the required levels of yield gap closures by 2100. Our analyses also show that without the stated levels of yield gap closure, the areas under maize, cassava and yam cultivation will have to increase by 95%, 102% and 250% respectively in order to maintain the current levels of per capita food availability. Our study shows that food security outcomes and forest and woodland conservation goals in Benin and likely the larger SSA region are inextricably linked together and require holistic management strategies that considers trade-offs and co-benefits

Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan Africa

In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled georeferenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth. Maps of these secondary soil properties were derived using the primary soil property maps as input for the evaluation rules and the results were aggregated over the rootable depth to obtain a map of RZ-PAWHC, with a spatial resolution of 1 km2. The mean RZ-PAWHC for SSA is 74mm and the associated average root zone depth is 96 cm. Pearson correlation between the two is 0.95. RZ-PAWHC proves most limited by the rootable depth but is also highly sensitive to the definition of field capacity. The total soil volume of SSA potentially rootable by maize is reduced by one third (over 10,500 km3) due to soil conditions restricting root zone depth. Of these, 4800 km3 are due to limited depth of aeration, which is the factor most severely limiting in terms of extent (km2), and 2500 km3 due to sodicity which is most severely limiting in terms of degree (depth in cm). Depth of soil to bedrock reduces the rootable soil volume by 2500 km3, aluminium toxicity by 600 km3, porosity by 120 km3 and alkalinity by 20 km3. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available

How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis

Numerous studies have been published during the past two decades that use simulation models to assess crop yield gaps (quantified as the difference between potential and actual farm yields), impact of climate change on future crop yields, and land-use change. However, there is a wide range in quality and spatial and temporal scale and resolution of climate and soil data underpinning these studies, as well as widely differing assumptions about cropping-system context and crop model calibration. Here we present an explicit rationale and methodology for selecting data sources for simulating crop yields and estimating yield gaps at specific locations that can be applied across widely different levels of data availability and quality. The method consists of a tiered approach that identifies the most scientifically robust requirements for data availability and quality, as well as other, less rigorous options when data are not available or are of poor quality. Examples are given using this approach to estimate maize yield gaps in the state of Nebraska (USA), and at a national scale for Argentina and Kenya. These examples were selected to represent contrasting scenarios of data availability and quality for the variables used to estimate yield gaps. The goal of the proposed methods is to provide transparent, reproducible, and scientifically robust guidelines for estimating yield gaps; guidelines which are also relevant for simulating the impact of climate change and land-use change at local to global spatial scales. Likewise, the improved understanding of data requirements and alternatives for simulating crop yields and estimating yield gaps as described here can help identify the most critical “data gaps” and focus global efforts to fill them. A related paper (Van Bussel et al., 2015) examines issues of site selection to minimize data requirements and up-scaling from location-specific estimates to regional and national spatial scales

Soil data for yield gap assessment and soil suitability index for sustainable intensification

Providing food and water security for a population expected to exceed 9 billion by 2050 while conserving natural resources requires achieving high yields on every hectare of currently used arable land suitable for intensification. This is especially relevant for sub-Saharan Africa (SSA), where, unlike in other regions of the world, food production is not keeping pace with population growth. While recognizing there are other aspects to food security than production alone (e.g. distribution, demand, waste, governance, population), efficiently increasing production on existing farmland forms an essential component of the sustainable intensification paradigm. In SSA 80% of the food is currently produced by smallholder farmers, and rural population is projected to increase for the next 20 years while average farm size will decrease in most SSA countries. Therefore, smallholder farms must be part of the solution to local and global food security. In agricultural systems with current low yields, there are important opportunities for sustainable intensification through improving ecosystem services and yields simultaneously. However, smallholder production systems across SSA are extremely diverse in terms of agro-ecology (climate, soil, landform) and socio-economic conditions and there is a need for targeting “best fit” approaches from a basket of options, rather than pushing “silver bullet” blanket solutions. Examples of potentially successful options include integrated soil fertility management (ISFM), crop-livestock integration, alternative cropping systems, improved soil and water management, and agroforestry. However, for any of these interventions to be effective, soil quality and responsiveness to improved management is critical because very marginal or degraded soils cannot support intensified systems in a sustainable fashion. Unfortunately degraded and poorly responsive soils cover large areas of Africa and represent the majority of smallholder farmers’ fields in certain regions. Given this situation, a robust, quantitative index to identify and map soils for their suitability to support sustainable intensification is needed. Suitable soils are those that, in their current state, support resource-efficient and cost-effective responses to inputs such as fertilizers and are not prone to erosion, salinization or other forms of degradation that would occur under intensified cropping. In this paper we propose a soil suitability index composed of soil information currently available from the Africa Soil Information Service (AfSIS). The index combines inherent soil properties that are not easily modifiable but important for crop production (e.g. water holding capacity, soil depth) as well as soil attributes that are, in principle, amenable to modification through management and inputs (e.g. soil fertility, pH, and compaction). The index would also include measures of existing soil constraints (e.g. toxicities, salinity, etc). We illustrate the use of the index by combining it with geospatially explicit methodologies for assessing yield gaps that are currently being developed in the Global Yield Gap Atlas project (www.yieldgap.org). We show how this combination can be used to identify areas with climate-soil-cropping systems suitable for sustainable intensification

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 crop-specific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species

From field to atlas: Upscaling of location-specific yield gap estimates

Accurate estimation of yield gaps is only possible for locations where high quality local data are available, which are, however, lacking in many regions of the world. The challenge is how yield gap estimates based on location-specific input data can be used to obtain yield gap estimates for larger spatial areas. Hence, insight about the minimum number of locations required to achieve robust estimates of yield gaps at larger spatial scales is essential because data collection at a large number of locations is expensive and time consuming. In this paper we describe an approach that consists of a climate zonation scheme supplemented by agronomical and locally relevant weather, soil and cropping system data. Two elements of this methodology are evaluated here: the effects on simulated national crop yield potentials attributable to missing and/or poor quality data and the error that might be introduced in scaled up yield gap estimates due to the selected climate zonation scheme. Variation in simulated yield potentials among weather stations located within the same climate zone, represented by the coefficient of variation, served as a measure of the performance of the climate zonation scheme for upscaling of yield potentials. We found that our approach was most appropriate for countries with homogeneous topography and large climate zones, and that local up-to-date knowledge of crop area distribution is required for selecting relevant locations for data collection. Estimated national water-limited yield potentials were found to be robust if data could be collected that are representative for approximately 50% of the national harvested area of a crop. In a sensitivity analysis for rainfed maize in four countries, assuming only 25% coverage of the national harvested crop area (to represent countries with poor data availability), national water-limited yield potentials were found to be over- or underestimated by 3 to 27% compared to estimates with the recommended crop area coverage of ≥50%. It was shown that the variation of simulated yield potentials within the same climate zone is small. Water-limited potentials in semi-arid areas are an exception, because the climate zones in these semi-arid areas represent aridity limits of crop production for the studied crops. We conclude that the developed approach is robust for scaling up yield gap estimates from field, i.e. weather station data supplemented by local soil and cropping system data, to regional and national levels. Possible errors occur in semi-arid areas with large variability in rainfall and in countries with more heterogeneous topography and climatic conditions in which data availability hindered full application of the approach