Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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A spatial framework for ex-ante impact assessment of agricultural technologies
Andrade, José F. ; Rattalino Edreira, Juan I. ; Farrow, Andrew ; Loon, Marloes P. van; Craufurd, Peter Q. ; Rurinda, Jairos ; Zingore, Shamie ; Chamberlin, Jordan ; Claessens, Lieven ; Adewopo, Julius ; Ittersum, Martin K. van; Cassman, Kenneth G. ; Grassini, Patricio - \ 2019
Global Food Security 20 (2019). - ISSN 2211-9124 - p. 72 - 81.
Agricultural R&D - Impact assessment - Scaling out - Spatial framework

Traditional agricultural research and extension relies on replicated field experiments, on-farm trials, and demonstration plots to evaluate and adapt agronomic technologies that aim to increase productivity, reduce risk, and protect the environment for a given biophysical and socio-economic context. To date, these efforts lack a generic and robust spatial framework for ex-ante assessment that: (i) provides strategic insight to guide decisions about the number and location of testing sites, (ii) define the target domain for scaling-out a given technology or technology package, and (iii) estimate potential impact from widespread adoption of the technology(ies) being evaluated. In this study, we developed a data-rich spatial framework to guide agricultural research and development (AR&D) prioritization and to perform ex-ante impact assessment. The framework uses “technology extrapolation domains”, which delineate regions with similar climate and soil type combined with other biophysical and socio-economic factors that influence technology adoption. We provide proof of concept for the framework using a maize agronomy project in three sub-Saharan Africa countries (Ethiopia, Nigeria, and Tanzania) as a case study. We used maize area and rural population coverage as indicators to estimate potential project impact in each country. The project conducted 496 nutrient omission trials located at both on-farm and research station sites across these three countries. Reallocation of test sites towards domains with a larger proportion of national maize area could increase coverage of maize area by 79–134% and of rural population by 14–33% in Nigeria and Ethiopia. This study represents a first step in developing a generic, transparent, and scientifically robust framework to estimate ex-ante impact of AR&D programs that aim to increase food production and reduce poverty and hunger.

Water productivity of rainfed maize and wheat : A local to global perspective
Rattalino Edreira, Juan I. ; Guilpart, Nicolas ; Sadras, Victor ; Cassman, Kenneth G. ; Ittersum, Martin K. van; Schils, René L.M. ; Grassini, Patricio - \ 2018
Agricultural and Forest Meteorology 259 (2018). - ISSN 0168-1923 - p. 364 - 373.
Maize - Management - Spatial framework - Water productivity - Wheat - Yield

Water productivity (WP) is a robust benchmark for crop production in relation to available water supply across spatial scales. Quantifying water-limited potential (WPw) and actual on-farm (WPa) WP to estimate WP gaps is an essential first step to identify the most sensitive factors influencing production capacity with limited water supply. This study combines local weather, soil, and agronomic data, and crop modeling in a spatial framework to determine WPw and WPa at local and regional levels for rainfed cropping systems in 17 (maize) and 18 (wheat) major grain-producing countries representing a wide range of cropping systems, from intensive, high-yield maize in north America and wheat in west Europe to low-input, low-yield maize systems in sub-Saharan Africa and south Asia. WP was calculated as the quotient of either water-limited yield potential or actual yield, and simulated crop evapotranspiration. Estimated WPw upper limits compared well with maximum WP reported for field-grown crops. However, there was large WPw variation across regions with different climate and soil (CV = 29% for maize and 27% for wheat), which cautions against the use of generic WPw benchmarks and highlights the need for region-specific WPw. Differences in simulated evaporative demand, crop evapotranspiration after flowering, soil evaporation, and intensity of water stress around flowering collectively explained two thirds of the variation in WPw. Average WP gaps were 13 (maize) and 10 (wheat) kg ha−1 mm−1, equivalent to about half of their respective WPw. We found that non-water related factors (i.e., management deficiencies, biotic and abiotic stresses, and their interactions) constrained yield more than water supply in ca. half of the regions. These findings highlight the opportunity to produce more food with same amount of water, provided limiting factors other than water supply can be identified and alleviated with improved management practices. Our study provides a consistent protocol for estimating WP at local to regional scale, which can be used to understand WP gaps and their mitigation.

Beyond the plot: technology extrapolation domains for scaling out agronomic science
Rattalino Edreira, Juan I. ; Cassman, Kenneth G. ; Hochman, Zvi ; Ittersum, Martin K. van; Bussel, Lenny van; Claessens, Lieven ; Grassini, Patricio - \ 2018
Environmental Research Letters 13 (2018)5. - ISSN 1748-9318
geospatial analysis - impact assessment - research prioritization - technology extrapolation

Ensuring 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
Leenaars, Johan G.B. ; Claessens, Lieven ; Heuvelink, Gerard B.M. ; Hengl, Tom ; Ruiperez González, Maria ; Bussel, Lenny G.J. van; Guilpart, Nicolas ; Yang, Haishun ; Cassman, Kenneth G. - \ 2018
Geoderma 324 (2018). - ISSN 0016-7061 - p. 18 - 36.
Digital soil map - Maize - Root zone depth - Rootability - Soil data - Soil water - 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 geo-referenced 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 74 mm 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.
Kenya public weather processed by the Global Yield Gap Atlas project
Groot, Hugo de; Adimo, Ochieng ; Claessens, Lieven ; Wart, Justin Van; Bussel, Lenny G.J. van; Grassini, Patricio ; Wolf, Joost ; Guilpart, Nicolas ; Boogaard, Hendrik ; Oort, Pepijn A.J. van; Yang, Haishun S. ; Ittersum, Martin K. van; Cassman, Kenneth G. - \ 2017
ODjAR : open data journal for agricultural research 3 (2017). - ISSN 2352-6378 - p. 16 - 18.
The Global Yield Gap Atlas project (GYGA - http://yieldgap.org) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et al. (2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers daily weather data for 12 locations in Kenya for the years 1998-2012. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate bias corrected weather data from a combination of observed data and other external weather data. The bias corrected weather data consist of daily TRMM rain data and NASA POWER Tmax, Tmin, and Tdew data. These data are corrected based on calibrations with short-term (<10 years) observed weather data.
Mapping root depth soil water in sub-Saharan Africa
Leenaars, J.G.B. ; Claessens, L.F.G. ; Heuvelink, G.B.M. ; Hengl, T. ; Ruiperez Gonzalez, M. ; Bussel, L.G.J. van; Guilpart, Nicolas ; Yang, H. ; Cassman, K.G. - \ 2017
In: Abstract Book Pedometrics 2017. - Wageningen : - p. 130 - 130.
Rooting for food security in Sub-Saharan Africa
Guilpart, Nicolas ; Grassini, Patricio ; Wart, Justin van; Yang, Haishun ; Ittersum, M.K. van; Bussel, Lenny G.J. van; Wolf, J. ; Claessens, L.F.G. ; Leenaars, Johan G.B. ; Cassman, Kenneth G. - \ 2017
Environmental Research Letters 12 (2017)11. - ISSN 1748-9326 - 7 p.
There is a persistent narrative about the potential of Sub-Saharan Africa (SSA) to be a 'grain breadbasket' because of large gaps between current low yields and yield potential with good management, and vast land resources with adequate rainfall. However, rigorous evaluation of the extent to which soils can support high, stable yields has been limited by lack of data on rootable soil depth of sufficient quality and spatial resolution. Here we use location-specific climate data, a robust spatial upscaling approach, and crop simulation to assess sensitivity of rainfed maize yields to root-zone water holding capacity. We find that SSA could produce a modest maize surplus but only if rootable soil depths are comparable to that of other major breadbaskets, such as the US Corn Belt and South American Pampas, which is unlikely based on currently available information. Otherwise, producing surplus grain for export will depend on expansion of crop area with the challenge of directing this expansion to regions where soil depth and rainfall are supportive of high and consistent yields, and where negative impacts on biodiversity are minimal
Can sub-Saharan Africa feed itself?
Ittersum, M.K. van; Bussel, L.G.J. van; Wolf, J. ; Grassini, Patricio ; Wart, Justin van; Guilpart, Nicolas ; Claessens, L.F.G. ; Groot, H.L.E. de; Wiebe, Keith ; Mason-d’Croz, Daniel ; Yang, Haishun ; Boogaard, H.L. ; Oort, P.A.J. van; Loon, M.P. van; Saito, Kazuki ; Adimo, Ochieng ; Adjei-Nsiah, Samuel ; Agali, Alhassane ; Bala, Abdullahi ; Chikowo, Regis ; Kaizzi, Kayuki ; Kouressy, Mamoutou ; Makoi, Joachim H.J.R. ; Ouattara, Korodjouma ; Tesfaye, Kindie ; Cassman, Kenneth G. ; Hall, Lindsey ; Kalka, Gogi - \ 2017
Environmental Science Journal for Teens (2017). - 4 p.
By the year 2050, the world’s population will need 60% more food than it did in 2005. In sub-Saharan Africa (we’ll call it SSA) (Fig. 1) this problem will be even greater, with the demand for cereals increasing by more than three times as the population rises.
We collected and calculated farming data for 10 countries in sub-Saharan Africa. This made us realize that countries in SSA must make many large changes to ncrease their yield of cereals (the amount of cereals that are grown on the current farmland each year) to meet this greater demand.
If countries in SSA are unable to increase cereal yield, there are two options. either farmland areas will have to increase drastically, at the expense of natural land, or SSA will need to buy more cereal from other countries than it does today. This may put more people in these countries at risk of not having enough food to be able to live healthily.
Exploring Maize Intensification with the Global Yield Gap Atlas
Grassini, Patricio ; Cassman, Kenneth G. ; Ittersum, M.K. van - \ 2017
Better crops with plant food 101 (2017)2. - ISSN 0006-0089 - p. 7 - 9.
Trade-off analysis has become an increasingly important approach far evaluating system level outcomes of agricultural production and for prioritising and taigeting management interventions in multi-functional agricultural landscapes. We review the strengths and weakness of different techniques available for performing trade-off analysis. These techniques, including mathematical programming and par­ticipatory approaches, have developed substantially in recent years aided by mathematical advancement, increased computing power, and emerging insights into systems behaviour. The strengths and weaknesses of the different approaches are identified and discussed, and we make suggestions for a tiered approach for situations with different data availability. This chapter is a modified and extended version of Klapwijk et al. (2014).
Africa soil information to predict response to soil fertility management practices
Leenaars, J.G.B. ; Ruiperez Gonzalez, M. ; Hengl, T. ; Mendes de Jesus, J.S. ; Kempen, B. ; Heuvelink, G.B.M. ; Batjes, N.H. ; Wosten, J.H.M. ; Bussel, L.G.J. van; Wolf, J. ; Yang, H. ; Claessens, L.F.G. ; Cassman, K.G. ; Ittersum, M.K. van - \ 2016
Kenya public weather processed by the Global Yield Gap Atlas project (revised version)
Groot, H.L.E. de; Adimo, A.O. ; Claessens, L.F.G. ; Wart, J. van; Bussel, L.G.J. van; Grassini, P. ; Wolf, J. ; Guilpart, Nicolas ; Boogaard, H.L. ; Oort, P.A.J. van; Yang, H. ; Ittersum, M.K. van; Cassman, K.G. - \ 2016
weather data - crop simulation model - yield gap - yield potential
The Global Yield Gap Atlas project (GYGA - http://yieldgap.org) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et al. (2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers daily weather data for 12 locations in Kenya for the years 1998-2012. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate bias corrected weather data from a combination of observed data and other external weather data. The bias corrected weather data consist of daily TRMM rain data and NASA POWER Tmax, Tmin, and Tdew data. These data are corrected based on calibrations with short-term (<10 years) observed weather data.
Can sub-Saharan Africa feed itself?
Ittersum, Martin K. Van; Bussel, Lenny G.J. Van; Wolf, Joost ; Grassini, Patricio ; Wart, Justin Van; Guilpart, Nicolas ; Claessens, Lieven ; Groot, Hugo de; Wiebe, Keith ; Mason-d’Croz, Daniel ; Yang, Haishun ; Boogaard, Hendrik ; Oort, Pepijn A.J. van; Loon, Marloes P. van; Saito, Kazuki ; Adimo, Ochieng ; Adjei-Nsiah, Samuel ; Agali, Alhassane ; Bala, Abdullahi ; Chikowo, Regis ; Kaizzi, Kayuki ; Kouressy, Mamoutou ; Makoi, Joachim H.J.R. ; Ouattara, Korodjouma ; Tesfaye, Kindie ; Cassman, Kenneth G. - \ 2016
Proceedings of the National Academy of Sciences of the United States of America 113 (2016)52. - ISSN 0027-8424 - p. 14964 - 14969.
Although global food demand is expected to increase 60% by 2050 compared with 2005/2007, the rise will be much greater in sub-Saharan Africa (SSA). Indeed, SSA is the region at greatest food security risk because by 2050 its population will increase 2.5-fold and demand for cereals approximately triple, whereas current levels of cereal consumption already depend on substantial imports. At issue is whether SSA can meet this vast increase in cereal demand without greater reliance on cereal imports or major expansion of agricultural area and associated biodiversity loss and greenhouse gas emissions. Recent studies indicate that the global increase in food demand by 2050 can be met through closing the gap between current farm yield and yield potential on existing cropland. Here, however, we estimate it will not be feasible to meet future SSA cereal demand on existing production area by yield gap closure alone. Our agronomically robust yield gap analysis for 10 countries in SSA using location-specific data and a spatial upscaling approach reveals that, in addition to yield gap closure, other more complex and uncertain components of intensification are also needed, i.e., increasing cropping intensity (the number of crops grown per 12 mo on the same field) and sustainable expansion of irrigated production area. If intensification is not successful and massive cropland land expansion is to be avoided, SSA will depend much more on imports of cereals than it does today.
Can yield gap analysis be used to inform R&D prioritisation?
Oort, P.A.J. van; Saito, K. ; Dieng, I. ; Grassini, P. ; Cassman, K.G. ; Ittersum, M.K. Van - \ 2016
Global Food Security 12 (2016). - ISSN 2211-9124 - p. 109 - 118.
The phrase “biggest bang for a buck” is associated with the policy making question that governments and development agencies face: “Where and which crops should receive highest priority for improving local and global food supply?”. A first step of prioritisation is to identify region x crop combinations for which high impact can be anticipated. We developed a new method for this prioritisation exercise and applied it to data from the Global Yield Gap and Water Productivity Atlas (GYGA). Our prioritisation distinguishes between two policy objectives (humanitarian and economic) and builds upon the relative yield gap and climate risk. Results of the prioritisation are presented and visualised in Google Earth.
Quantifying beef production gaps of two farming systems in the Charolais basin, France
Linden, A. van der; Ven, G.W.J. van de; Oosting, S.J. ; Ittersum, M.K. van; Boer, I.J.M. de - \ 2015
In: Proceedings of the 5th International Symposium for Farming Systems Design. - - p. 9 - 10.
1 Introduction Sustainable intensification of livestock production systems is a way to realise the increasing global demand for meat. Current empirical studies reveal meat production levels obtained by best practices, but do not clarify the theoretically achievable (i.e. potential) and feed limited production. Potential production is defined by animal genotype and climate only (Fig. 1). Feed limited production is determined by genotype, climate, availability of drinking water, and the quality and quantity of feed. Actual production is the production that farmers achieve in practice. This production level is, next to genotype, climate, water, and feed, determined by diseases and stress in livestock (Van de Ven et al., 2003). Fig. 1. Potential, limited, and actual production of crops (left) and livestock (right).In crop production, the production ecological concepts of potential, limited, and actual production (Fig. 1) (Van Ittersum & Rabbinge, 1997) are generally used to give insight in the scope to increase production from their actual levels (Van Ittersum et al., 2013). These concepts are also applicable to livestock production (Van de Ven et al., 2003 ; Van der Linden et al.), but so far the effects of genotype, climate, feed quality, and feed quantity have not been quantified systematically using production ecological concepts in livestock production. This research, therefore, aims to quantify potential, feed quality limited, and actual beef production in two French beef production systems at herd level. Feed quantity limitation is not included. 2 Materials and Methods A mechanistic, dynamic model was developed to simulate beef cattle growth based on genotype, climate, housing, feed quantity, and feed quality. This model is analogous to crop growth models that are based on the production ecological concepts. The beef cattle model combines feed digestion, thermoregulation, and feed utilisation sub-models in a novel way to simulate processes at animal level. Results from animal level are scaled up to herd level. Energy, heat, and protein flows are described in the model, which is programmed in R 3.0.2. Input data for the model are parameters for a specific genotype or breed, daily climate data, and information on housing, feed quality and feed quantity intake. The model was applied to two beef production systems with different feeding strategies of Charolais cattle in the Charolais Basin, France. System A corresponds to farm type 11111 and system B to farm type 31041 as described by Réseaux d’Elevage Charolais (2012). System A produced heavier animals and has a longer grazing period than system B. The fraction concentrates in the diet is larger in system B than in system A.Potential production was expressed as a feed efficiency (FE, g beef kg-1 DM feed). Potential production in both systems was simulated with an ad libitum fed diet containing 65.8 % barley and 34.2% hay. This diet prevented feed quality and quantity limitation. Under potential production, FE was maximized at herd level, and all female calves were kept for replacement. Culling was set at 50% per year after birth of the first calf. Feed quality limited production was simulated with a diet containing concentrates and hay when cattle were housed during winter, and grass during other periods of the year. Concentrate intake (barley) was 4.8% of the DM intake in system A and 18.3% of the DM intake in system B, which corresponded to the diet under actual production. Feed quality limited production was simulated with the same culling rates and slaughter weights as under potential production. Actual production was calculated from data provided by Réseaux d’Elevage Charolais (2012). Yield gaps were calculated as the difference between potential and actual production, and the difference between feed quality limited production and actual production. Relative yield gaps were calculated as the yield gap divided by potential or feed quality limited production. 3 Results and discussion FE at herd level was highest under potential production and feed quality limited production, when male calves were slaughtered at 1000 kg. Potential production in systems A and B (Fig. 2) was slightly different (64.0 vs 64.4 g beef kg-1 DM feed). FE in system A was lower due to a longer grazing period and hence a higher energy requirement for grazing. Feed quality limited production, with the same culling rates and slaughter weights as under potential production, was lower in system A than in system B (51.7 vs 54.1 g beef kg-1 DM feed), which is explained by a lower fraction of concentrates in the diet. Actual production was lower in system A than in system B (24.9 vs 31.2 g beef kg-1 DM feed). Fig. 2. Simulated feed efficiency in beef production systems A and B under potential, feed quality limited, and actual production. The relative yield gap between actual and potential production was 61% in system A and 52% in system B, and the relative yield gap between actual and feed quality limited production was 52% in system A and 42% in system B. The latter yield gaps can be explained by feed quality limitation, as well as stress and diseases. In crop production, yields tend to plateau at 75-85% of potential or water limited production (i.e. minimum yield gaps equal 15-25%), and further yield gap mitigation is not economically or practically feasible (Van Ittersum et al., 2013). In our study, simulated yield gaps are much larger than such minimum yield gaps. Grazing and suckler cow premiums might not urge farmers to mitigate current yield gaps, but also social factors (e.g. labour availability) may play a role. More model validation is required to further improve accuracy of the simulation results. Multiplying beef production (kg beef t-1 DM feed) and feed crop production (t DM ha-1 year-1) results in the beef production per unit of land (kg beef ha-1 year-1). Quantifying potential and limited production of crops and livestock according to production ecology allows us to assess land use per kg of animal product. 4 Conclusions The production ecological concepts were successfully applied to livestock production. We benchmarked actual beef production relative to potential and feed quality limited production of two French beef production systems at herd level. Results indicate that potential production is more than two times the actual production in both systems. Hence, there is considerable scope to increase beef production in the Charolais basin, from a bio-physical perspective. References Réseaux d’Elevage Charolais (2012). Bassin Charolais. Conjoncture économique des systèmes bovins Charolais, Campagne 2012. Document, Chambre d’Agriculture, Institut de l’Elevage, 50pp.Van de Ven, G.W.J., de Ridder, N., van Keulen, H. & van Ittersum M.K. (2003). Concepts in production ecology for analysis and design of animal and plant-animal production systems. Agricultural Systems, 76, 507-525.Van der Linden, A., Oosting, S.J., van de Ven, G.W.J, de Boer, I.J.M. & van Ittersum, M.K. A framework for quantitative analysis of livestock systems using the theoretical concepts of production ecology. Submitted to Agricultural Systems.Van Ittersum, M.K. & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research, 52, 197-208.Van Ittersum, M.K., Cassman, K.G., Grassini, P., Wolf, J., Tittonell, P. & Hochman, Z. (2013). Yield gap analysis with local to global relevance-A review. Field Crops Research, 143, 4-17.
Creating long-term weather data from thin air for crop simulation modeling
Wart, Justin Van; Grassini, Patricio ; Yang, Haishun ; Claessens, Lieven ; Jarvis, Andrew ; Cassman, Kenneth G. - \ 2015
Agricultural and Forest Meteorology 209-210 (2015). - ISSN 0168-1923 - p. 49 - 58.
Simulating crop yield and yield variability requires long-term, high-quality daily weather data, including solar radiation, maximum (Tmax) and minimum temperature (Tmin), and precipitation. In many regions, however, daily weather data of sufficient quality and duration are not available. To overcome this limitation, we evaluated a new method to create long-term weather series based on a few years of observed daily temperature data (hereafter called propagated data). The propagated data are comprised of uncorrected gridded solar radiation from the Prediction of Worldwide Energy Resource dataset from the National Aeronautics and Space Administration (NASA–POWER), rainfall from the Tropical Rainfall Measuring Mission (TRMM) dataset, and location-specific calibration of NASA–POWER Tmax and Tmin using a limited amount of observed daily temperature data. The distributions of simulated yields of maize, rice, or wheat with propagated data were compared with simulated yields using observed weather data at 18 sites in North and South America, Europe, Africa, and Asia. Other sources of weather data typically used in crop modeling for locations without long-term observed weather data were also included in the comparison: (i) uncorrected NASA–POWER weather data and (ii) generated weather data using the MarkSim weather generator. Results indicated good agreement between yields simulated with propagated weather data and yields simulated using observed weather data. For example, the distribution of simulated yields using propagated data was within 10% of the simulated yields using observed data at 78% of locations and degree of yield stability (quantified by coefficient of variation) was very similar at 89% of locations. In contrast, simulated yields based entirely on uncorrected NASA–POWER data or generated weather data using MarkSim were within 10% of yields simulated using observed data in only 44 and 33% of cases, respectively, and the bias was not consistent across locations and crops. We conclude that, for most locations, 3 years of observed daily Tmax and Tmin data would allow creation of a robust weather data set for simulation of long-term mean yield and yield stability of major cereal crops.
Kenya public weather processed by the Global Yield Gap Atlas project (old version)
Groot, H.L.E. de; Adimo, A.O. ; Claessens, L.F.G. ; Wart, J. van; Bussel, L.G.J. van; Grassini, P. ; Wolf, J. ; Guilpart, Nicolas ; Boogaard, H.L. ; Oort, P.A.J. van; Yang, H. ; Ittersum, M.K. van; Cassman, K.G. - \ 2015
weather data - crop simulation model - yield gap - crop yield - yield potential
The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers weather data for 10 locations in Kenya. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate weather data from a combination of observed and other external weather data. One locations holds actually measured weather data, the other 9 locations show propagated weather data. The propagated weather data consist on TRMM rain data (or NASA POWER if TRMM is not available) and NASA POWER Tmax, Tmin, and Tdew data corrected based on calibrations with short-term (<10 years) observed weather data. sources (Van Wart et.al. 2015).
Root zone plant-available water holding capacity of the Sub-Saharan Africa soil, version 1.0. : Gridded functional soil information (dataset RZ-PAWHC SSA v. 1.0)
Leenaars, J.G.B. ; Hengl, T. ; Ruiperez Gonzalez, M. ; Mendes de Jesus, J.S. ; Heuvelink, G.B.M. ; Wolf, J. ; Bussel, L.G.J. van; Claessens, H. ; Yang, H. ; Cassman, K.G. - \ 2015
Wageningen : ISRIC - World Soil Information - 108 p.
The global yield gap atlas for targeting sustainable intensification options for smallholders in Sub-Saharan Africa
Claessens, L.F.G. ; Cassman, K.G. ; Ittersum, M.K. van; Leenaars, J.G.B. ; Bussel, L.G.J. van; Wolf, J. ; Wart, J.P. van; Grassini, P. ; Yang, H. ; Boogaard, H.L. ; Groot, H.L.E. de; Guilpart, Nicolas ; Heuvelink, G.B.M. ; Stoorvogel, J.J. ; Hendriks, C.M.J. - \ 2015
In: Wageningen Soil Conference 2015: Soil science in a changing world / Keesstra, S., Mol, G., Zaal, A., Wallinga, J., Jansen, B., Wageningen : Wageningen UR - ISBN 9789461731685 - p. 43 - 43.
Soil information to feed the African soil, crop and people
Leenaars, J.G.B. ; Ruiperez Gonzalez, M. ; Hengl, T. ; Mendes de Jesus, J.S. ; Kempen, B. ; Claessens, L.F.G. ; Bussel, L.G.J. van; Wolf, J. ; Yang, H. ; Cassman, K.G. ; Ittersum, M.K. van; Heuvelink, G.B.M. ; Batjes, N.H. - \ 2015
In: Wageningen Soil Conference 2015: Soil science in a changing world. - Wageningen UR - ISBN 9789461731685 - p. 174 - 174.
Intensification or expansion of Agriculture in Sub‐Saharan Africa?
Ittersum, M.K. van; Wolf, J. ; Bussel, L. van; Grassini, P. ; Wart, J. van; Claessens, L.F.G. ; Groot, H. de; Oort, P. van; Guilpart, Nicolas ; Cassman, K. - \ 2015
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