Category: Agriculture

• Mapping rootable depth and root zone plant-available water holding capacity of the soil of sub-Saharan AfricaLeenaars J.G.B., L. Claessens, G.B.M. Heuvelink, T. Hengl, M. Ruiperez González, L.G.J. van Bussel, N. Guilpart, H. Yang, K.G. Cassman, 2018
• WoSIS: providing standardised soil profile data for the world 2017-09-01
• Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learningHengl T., J.G.B. Leenaars, K.D. Shepherd, M.G. Walsh, G.B.M. Heuvelink, Tekalign Mamo, Helina Tilahun, E. Berkhout, M. Cooper, E. Fegraus, I. Wheeler, Nketia A. Kwabena, 2017-09
• Mapping of fertilizer recommendations for major crops in West AfricaLeenaars J.G.B., E.J. Dossa, M. Ruiperez Gonzalez, B. Kempen, 2017
• Rooting for food security in Sub-Saharan AfricaGuilpart N., P. Grassini, J. van Wart, H. Yang, M.K. van Ittersum, L.G.J. van Bussel, J. Wolf, L. Claessens, J.G.B. Leenaars, K.G. Cassman, 2017
• Mapping root depth soil water in sub-Saharan Africa. Abstract book Pedometrics 2017. Wageningen: 130.Leenaars Johan G.B., Lieven Claessens, Gerard B.M. Heuvelink, TomHengl, Maria Ruipérez Gonzalez, Lenny G.J. van Bussel, Nicolas Guilpart, Haishun Yang, Kenneth G. Cassman, 2017
• A spatial data infrastructure for storing and exchanging global soil dataKempen B., J. Mendes de Jesus, N.H. Batjes, E. Ribeiro, J.G.B. Leenaars, T. Hengl, 2016-10
• The Land-Potential Knowledge System (LandPKS): mobile apps and collaboration for optimizing climate change investmentsHerrick J. E., A. Beh, E. Barrios, I. Bouvier, M. Coetzee, D. Dent, E. Elias, T. Hengl, J. W. Karl, H. Liniger, J. Matuszak, J. C. Neff, L. W. Ndungu, M. Obersteiner, K. D. Shepherd, K. C. Urama, R. van den Bosch, and N. P. Web, 2016
• Land health surveillance and response: a framework for evidence-informed land managementShepherd, K.D., Shepherd, G. Walsh, M.G. (2015). Land health surveillance and response: a framework for evidence-informed land management. Agricultural Systems 132: 93–106. doi:10.1016/j.agsy.2014.09.002., 2015-01 [+]
  Degradation of land health – the capacity of land, relative to its potential, to sustain delivery of ecosystem services – is recognized as a major global problem in general terms, but remains poorly quantified, resulting in a lack of specific evidence to focus action. Land health surveillance and response is designed to overcome limitations of current assessment approaches. It is modelled on science principles and approaches used in surveillance in the public health sector, which has a long history of evidence-informed policy and practice. Key elements of the science framework are: (i) repeated measurement of land health and associated risk factors using probability based sampling of well defined populations of sample units; (ii) standardized protocols for data collection to enable statistical analysis of patterns, trends, and associations; (iii) case definitions based on specific diagnostic criteria; (iv) rapid low cost screening tests to permit detection of cases and non-cases in large numbers of samples; (v) cost-effectiveness evaluation of interventions based on projected reduction in risks and problem incidence; (vi) design of statistically analysable studies to evaluate interventions in the real-world; (vii) meta-analysis of these data to guide design of public policy and intervention programmes; and (viii) integrating surveillance and the communication and use of results into operational systems as part of regular policy and practice. The scientific rigour of land health surveillance has potential to provide a sound basis for directing and assessing action to combat land degradation. Specialized national surveillance units should be established to harness and realign existing resources to provide integrated national land health systems. An international unit is needed to provide science and technology support to governments and develop standards, whereas an international agency should coordinate land health surveillance globally. Application of the surveillance framework could result in a shift away from a focus on rehabilitation of severely degraded land towards a preventive approach that focuses more on reducing distal risks at national and regional levels.
• Mapping of soil properties and land degradation risk in Africa using MODIS reflectanceVågen,T-G, Winowiecki,LA, Tondoh,JE, Desta,LT, Gumbricht,T, 2015
• The global yield gap atlas for targeting sustainable intensification options for smallholders in Sub-Saharan AfricaClaessens, L, Cassman KG, van Ittersum MK, Leenaars JGB, van Bussel L, Wolf J, van Wart JP, Grassini P, Yang H, Boogaard H, de Groot H, Guilpart N, Heuvelink GBM, Stoorvogel JJ, Hendriks C, Keestra S, Mol G, Zaal A, Wallinga J, and Jansen B, 2015
• Soil map providing basic information for crop and site specific water and fertility recommendations in EthiopiaWösten, H, Leenaars JGB, Eyasu E, Keestra S, Mol G, Zaal A, Wallinga J, and Jansen B, 2015
• Soil information to feed the african soil, crop and peopleLeenaars, JGB, Ruiperez Gonzalez M, Hengl T, Mendes de Jesus J, Kempen B, Claessens L, van Bussel LGJ, Wolf J, Yang H, Cassman KG, van Ittersum MK, Heuvelink GBM, Batjes NH, Keestra S, Mol G, Zaal A, Wallinga J, and Jansen B, 2015
• Soil data harmonisation and geostatistical modelling efforts in support of improved studies of global sustainabilityBatjes, NH, Kempen B, Leenaars JGB, and van den Bosch H, 2015
• Soil data harmonisation and geostatistical modelling efforts in support of improved studies of global sustainabilityBatjes, NH, Kempen B, Leenaars JGB, and van den Bosch H, 2015
• Root zone plant-available water holding capacity of the Sub-Saharan Africa soil, version 1.0. Gridded functional soil informationLeenaars, JGB, Hengl T, Ruiperez Gonzalez M, Mendes de Jesus J, Heuvelink GBM, Wolf J, van Bussel L, Claessens L, Yang H, and Cassman, KG, 2015
• Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictionsHengl, T, Heuvelink GBM, Kempen B, Leenaars JGB, Walsh MG, Shepherd KD, Sila A, MacMillan RA, Mendes de Jesus J, Tamene L, and Tondoh JE, 2015
• Africa Soil Profiles Database, Version 1.2. A compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa (with datasetLeenaars, JGB, van Oostrum AJM, and Ruiperez Gonzalez M, 2014
• SoilGrids1km — Global Soil Information Based on Automated MappingHengl, T, Mendes de Jesus J, MacMillan RA, Batjes NH, Heuvelink GBM, Ribeiro E, Samuel-Rosa A, Kempen B, Leenaars JGB, Walsh MG, and Ruiperez Gonzalez M, 2014
• Africa Soil Profiles Database: a compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan AfricaLeenaars, JGB, Kempen B, van Oostrum AJM, and Batjes NH, 2014
• Africa Soil Profiles Database, Version 1.1. A compilation of geo-referenced and standardized legacy soil profile data for Sub Saharan AfricaLeenaars, JGB, 2013
• Soil property maps of Africa at 1 kmISRIC – World Soil Information, 2013
• Soil hydraulic information for river basin studies in semi-arid regionsWösten, JHM, Verzandtvoort SJE, Leenaars JGB, Hoogland T, and Wesseling JG, 2013
• Soil Heterogeneity and Soil Fertility Gradients in Smallholder Agricultural Systems of the East African HighlandsTittonell, P., Muriuki, A., Klapwijk, C.J., Shepherd, K.D., Coe, R., Vanlauwe, B. 2013. Soil Heterogeneity and Soil Fertility Gradients in Smallholder Agricultural Systems of the East African Highlands. Soil Science Society of America Journal 2013 77: 5, 2012-08-09 [+]
  Heterogeneity in soil fertility in these smallholder systems is caused by both inherent soil-landscape and human-induced variability across farms differing in resources and practices. Interventions to address the problem of poor soil fertility in Africa must be designed to target such diversity and spatially heterogeneity. Data on soil management and soil fertility from six districts in Kenya and Uganda were gathered to understand the determinants of soil heterogeneity within farms. Analysis of the variance of soil fertility indicators across 250 randomly selected farms (i.e., 2607 fields), using a mixed model that considered site, sampling frame, farm type, and field as random terms, revealed that the variation in soil organic C (6.5–27.7 g kg−1), total N (0.6–3.0 g kg−1), and available P (0.9–27 mg kg−1) was mostly related to differences in the inherent properties of the soils across sites (50 to 60% of total variance). Exchangeable K+ (0.1–1.1 cmol(+) kg−1), Ca2+ (1.5–14.5 cmol(+) kg−1), Mg2+ (0.6–3.7 cmol(+) kg−1), and pH (5.1–6.9) exhibited larger residual variability associated with field-to-field differences within farms (30 to 50%). Soil fertility indicators decreased significantly with increasing distance from the homesteads. When this variable was included in the model, the unexplained residual variances—associated with soil heterogeneity within farms—were 38% for soil C; 32% for total N; 49% for available P; 56, 49, and 38% for exchangeable K+, Ca2+ and Mg2+, respectively; and 49% for the pH. In allocating nutrient resources, farmers prioritized fields they perceived as most fertile, reinforcing soil heterogeneity. Categorization of fields within a farm with respect to distance from the homestead, and soil fertility classes as perceived by farmers, were identified as entry points to target soil fertility recommendations to easily recognizable, distinct entities.
• Africa Soil Profiles Database, Version 1.0. A compilation of geo-referenced and standardized legacy soil profile data for Sub Saharan Africa (with dataset).Leenaars, JGB, 2012
• The challenges of collating legacy data for digital mapping of Nigerian soilsOdeh, I.O.A.; Leenaars, J.G.B.; Hartemink, A.E.; Amapu, I., 2012