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Aramburu-Merlos F, Tenorio FAM, Mashingaidze N, Sananka A, Aston S, Ojeda JJ, Grassini P. Adopting yield-improving practices to meet maize demand in Sub-Saharan Africa without cropland expansion. Nat Commun 2024; 15:4492. [PMID: 38802418 PMCID: PMC11130130 DOI: 10.1038/s41467-024-48859-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 05/15/2024] [Indexed: 05/29/2024] Open
Abstract
Maize demand in Sub-Saharan Africa is expected to increase 2.3 times during the next 30 years driven by demographic and dietary changes. Over the past two decades, the area cropped with maize has expanded by 17 million hectares in the region, with limited yield increase. Following this trend could potentially result in further maize cropland expansion and the need for imports to satisfy domestic demand. Here, we use data collected from 14,773 smallholder fields in the region to identify agronomic practices that can improve farm yield gains. We find that agronomic practices related to cultivar selection, and nutrient, pest, and crop management can double on-farm yields and provide an additional 82 million tons of maize within current cropped area. Research and development investments should be oriented towards agricultural practices with proven capacity to raise maize yields in the region.
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Affiliation(s)
- Fernando Aramburu-Merlos
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
- Instituto de Innovación para la Producción Agropecuaria y el Desarrollo Sostenible (IPADS) Balcarce (INTA-CONICET), Balcarce, Buenos Aires, Argentina
| | - Fatima A M Tenorio
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA
| | | | | | | | | | - Patricio Grassini
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA.
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Tabo Z, Breuer L, Fabia C, Samuel G, Albrecht C. A machine learning approach for modeling the occurrence of the major intermediate hosts for schistosomiasis in East Africa. Sci Rep 2024; 14:4274. [PMID: 38383705 PMCID: PMC10881506 DOI: 10.1038/s41598-024-54699-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 02/15/2024] [Indexed: 02/23/2024] Open
Abstract
Schistosomiasis, a prevalent water-borne disease second only to malaria, significantly impacts impoverished rural communities, primarily in Sub-Saharan Africa where over 90% of the severely affected population resides. The disease, majorly caused by Schistosoma mansoni and S. haematobium parasites, relies on freshwater snails, specifically Biomphalaria and Bulinus species, as crucial intermediate host (IH) snails. Targeted snail control is advisable, however, there is still limited knowledge about the community structure of the two genera especially in East Africa. Utilizing a machine learning approach, we employed random forest to identify key features influencing the distribution of both IH snails in this region. Our results reveal geography and climate as primary factors for Biomphalaria, while Bulinus occurrence is additionally influenced by soil clay content and nitrogen concentration. Favorable climate conditions indicate a high prevalence of IHs in East Africa, while the intricate connection with geography might signify either dispersal limitations or environmental filtering. Predicted probabilities demonstrate non-linear patterns, with Bulinus being more likely to occur than Biomphalaria in the region. This study provides foundational framework insights for targeted schistosomiasis prevention and control strategies in the region, assisting health workers and policymakers in their efforts.
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Affiliation(s)
- Zadoki Tabo
- Department of Animal Ecology and Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392, Giessen, Germany.
- Institute for Landscape Ecology and Resource Management, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392, Giessen, Germany.
| | - Lutz Breuer
- Institute for Landscape Ecology and Resource Management, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392, Giessen, Germany
- Centre for International Development and Environmental Research (ZEU), Justus Liebig University Giessen, Senckenbergstrasse 3, 35390, Giessen, Germany
| | - Codalli Fabia
- Institute for Landscape Ecology and Resource Management, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392, Giessen, Germany
| | - Gorata Samuel
- Institute for Landscape Ecology and Resource Management, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392, Giessen, Germany
- Department of Environmental Science, Faculty of Science, University of Botswana, P/Bag UB00704, Gaborone, Botswana
| | - Christian Albrecht
- Department of Animal Ecology and Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26 (iFZ), 35392, Giessen, Germany
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Sakizadeh M, Zhang C, Milewski A. Spatial distribution pattern and health risk of groundwater contamination by cadmium, manganese, lead and nitrate in groundwater of an arid area. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:80. [PMID: 38367130 DOI: 10.1007/s10653-023-01845-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 12/21/2023] [Indexed: 02/19/2024]
Abstract
Combining the results of base models to create a meta-model is one of the ensemble approaches known as stacking. In this study, stacking of five base learners, including eXtreme gradient boosting, random forest, feed-forward neural networks, generalized linear models with Lasso or Elastic Net regularization, and support vector machines, was used to study the spatial variation of Mn, Cd, Pb, and nitrate in Qom-Kahak Aquifers, Iran. The stacking strategy proved to be an effective substitute predictor for existing machine learning approaches due to its high accuracy and stability when compared to individual learners. Contrarily, there was not any best-performing base model for all of the involved parameters. For instance, in the case of cadmium, random forest produced the best results, with adjusted R2 and RMSE of 0.108 and 0.014, as opposed to 0.337 and 0.013 obtained by the stacking method. The Mn and Cd showed a tight link with phosphate by the redundancy analysis (RDA). This demonstrates the effect of phosphate fertilizers on agricultural operations. In order to analyze the causes of groundwater pollution, spatial methodologies can be used with multivariate analytic techniques, such as RDA, to help uncover hidden sources of contamination that would otherwise go undetected. Lead has a larger health risk than nitrate, according to the probabilistic health risk assessment, which found that 34.4% and 6.3% of the simulated values for children and adults, respectively, were higher than HQ = 1. Furthermore, cadmium exposure risk affected 84% of children and 47% of adults in the research area.
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Affiliation(s)
- Mohamad Sakizadeh
- Department of Environmental Sciences, Shahid Rajaee Teacher Training University, Lavizan, 1678815811, Tehran, Iran.
| | - Chaosheng Zhang
- International Network for Environment and Health (INEH), School of Geography, Archaeology and Irish Studies, University of Galway, Galway, Ireland
| | - Adam Milewski
- Department of Geology, University of Georgia, Athens, USA
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Eshetu SB, Kipkulei HK, Koepke J, Kächele H, Sieber S, Löhr K. Impact of forest landscape restoration in combating soil erosion in the Lake Abaya catchment, Southern Ethiopia. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:228. [PMID: 38305922 PMCID: PMC10837221 DOI: 10.1007/s10661-024-12378-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
As an effect of forest degradation, soil erosion is among Ethiopia's most pressing environmental challenges and a major threat to food security where it could potentially compromise the ecosystem functions and services. As the effects of soil erosion intensify, the landscape's capacity to support ecosystem functions and services is compromised. Exploring the ecological implications of soil erosion is crucial. This study investigated the soil loss and land degradation in the Lake Abaya catchment to explore forest landscape restoration (FLR) implementation as a possible countermeasure to the effects. The study used a geographic information system (GIS)-based approach of the Revised Universal Soil Loss Equation (RUSLE) to determine the potential annual soil loss and develop an erosion risk map. Results show that 13% of the catchment, which accounts for approximately 110,000 ha, is under high erosion risk of exceeding the average annual tolerable soil loss of 10 t/ha/year. Allocation of land on steep slopes to crop production is the major reason for the calculated high erosion risk in the catchment. A scenario-based analysis was implemented following the slope-based land-use allocation proposal indicated in the Rural Land Use Proclamation 456/2005 of Ethiopia. The scenario analysis resulted in a reversal erosion effect whereby an estimated 3000 t/ha/year of soil loss in the catchment. Thus, FLR activities hold great potential for minimizing soil loss and contributing to supporting functioning and providing ecosystem services. Tree-based agroforestry systems are among the key FLR measures championed in highly degraded landscapes in Ethiopia. This study helps policymakers and FLR implementors identify erosion risk areas for future FLR activities. Thereby, it contributes to achieving the country's restoration commitment.
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Affiliation(s)
- Shibire Bekele Eshetu
- Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, Germany.
- Thaer-Institute for Agricultural and Horticultural Sciences Agricultural Economics, Humboldt Universität zu Berlin, Invalidenstr, 42, 10115, Berlin, Germany.
| | - Harison Kiplagat Kipkulei
- Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, Germany
- Thaer-Institute for Agricultural and Horticultural Sciences Agricultural Economics, Humboldt Universität zu Berlin, Invalidenstr, 42, 10115, Berlin, Germany
- Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Nairobi, Kenya
| | - Julian Koepke
- Eberswalde University for Sustainable Development, Schicklerstraße 5, 16225, Eberswalde, Germany
| | - Harald Kächele
- Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, Germany
- Eberswalde University for Sustainable Development, Schicklerstraße 5, 16225, Eberswalde, Germany
| | - Stefan Sieber
- Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, Germany
- Thaer-Institute for Agricultural and Horticultural Sciences Agricultural Economics, Humboldt Universität zu Berlin, Invalidenstr, 42, 10115, Berlin, Germany
| | - Katharina Löhr
- Leibniz Centre for Agricultural Landscape Research (ZALF), Eberswalder Str. 84, 15374, Müncheberg, Germany
- Urban Plant Ecophysiology, Thaer-Institute for Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Lentzeallee 55/57, 14195, Berlin, Germany
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Uwiragiye Y, Ngaba MJY, Yang M, Elrys AS, Chen Z, Cheng Y, Zhou J. Spatial prediction of lime requirements by adjusting aluminium saturation in Sub-Saharan Africa croplands. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:167989. [PMID: 37918756 DOI: 10.1016/j.scitotenv.2023.167989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 10/09/2023] [Accepted: 10/17/2023] [Indexed: 11/04/2023]
Abstract
Acidic soils cover over 30 % of Sub-Saharan Africa cropland. Acidic soils deprive crops of calcium, magnesium, potassium, molybdenum, and phosphorus due to aluminium (Al), manganese, and iron toxicities. Thus, liming is required to adjust the level of exchangeable Al3+ to the desired level of Al saturation of the crops grown. Lime requirement (LR) was quantified using soil dataset from Africa soil information service (AfSIS). Spatial variations of LR of cereals, pulses and cash crops were predicted using random forest algorithm. Our results revealed that mean of LR Mg CaCO3 (1 Mg = 106 g) ha-1 for cereal crops were 6.34, 6.35, and 4.41 for maize, sorghum, and upland rice, respectively. Mean of LR (Mg ha-1) for pulses were 6.28, 5.19, and 4.90 for common beans, soybeans, and cowpeas, respectively. Mean of LR Mg CaCO3 (1 Mg = 106 g) ha-1 for cash crops were 3.41 and 6.29 for coffee and cotton, respectively. Spatial variation showed that LR in croplands was higher in tropical humid regions than in semi-arid and arid regions and ranged from 0 to 8.8 Mg ha-1. The results of 10-fold cross validation for high model performance of LR for tested crops were coefficient of determination (R2) of 0.61, a root mean square error (RMSE) of 0.5, and a mean absolute error (MAE) of 0.31, maize LR with RMSE = 0.9, MAE = 0.24, and R2 = 0.51, and cotton LR with RMSE = 0.5, MAE = 0.31, and R2 = 0.60. We recommend predicting lime requirement in acidic soils of Sub-Saharan Africa by adjusting Al saturation up to the tolerance of the grown crop, updating soil surveys in Sab Saharan Africa, and using digital soil mapping to monitor soil acidity and lime requirement.
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Affiliation(s)
- Yves Uwiragiye
- School of Geography, Nanjing Normal University, Nanjing 210023, China; College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China; Department of Agriculture, Faculty of Agriculture, Environmental Management and Renewable Energy, University of Technology and Arts of Byumba, Rwanda; Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling, 712100, Shaanxi, China.
| | - Mbezele Junior Yannick Ngaba
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling, 712100, Shaanxi, China
| | - Mingxia Yang
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling, 712100, Shaanxi, China
| | - Ahmed S Elrys
- Soil Science Department, Faculty of Agriculture, Zagazig University, Zagazig 44511, Egypt; College of Tropical Crops, Hainan University, Haikou 570228, China; Liebig Centre for Agroecology and Climate Impact Research, Justus Liebig University, Germany
| | - Zhujun Chen
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling, 712100, Shaanxi, China
| | - Yi Cheng
- School of Geography, Nanjing Normal University, Nanjing 210023, China
| | - Jianbin Zhou
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, Shaanxi, China; Key Laboratory of Plant Nutrition and the Agri-Environment in Northwest China, Ministry of Agriculture, Yangling, 712100, Shaanxi, China.
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Mosaffa H, Filippucci P, Massari C, Ciabatta L, Brocca L. SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies. Sci Data 2023; 10:749. [PMID: 37907558 PMCID: PMC10618238 DOI: 10.1038/s41597-023-02654-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 10/17/2023] [Indexed: 11/02/2023] Open
Abstract
A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products' high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998-2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend.
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Affiliation(s)
- Hamidreza Mosaffa
- Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy.
| | - Paolo Filippucci
- Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
| | - Christian Massari
- Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
| | - Luca Ciabatta
- Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
| | - Luca Brocca
- Research Institute for Geo-Hydrological Protection, National Research Council, Perugia, Italy
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Khosravani P, Baghernejad M, Moosavi AA, Rezaei M. Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran. ENVIRONMENTAL MONITORING AND ASSESSMENT 2023; 195:1367. [PMID: 37875717 DOI: 10.1007/s10661-023-11980-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 10/11/2023] [Indexed: 10/26/2023]
Abstract
The soil's physical and mechanical (SPM) properties have significant impacts on soil processes, such as water flow, nutrient movement, aeration, microbial activity, erosion, and root growth. To digitally map some SPM properties at four global standard depths, three machine learning algorithms (MLA), namely, random forest, Cubist, and k-nearest neighbor, were employed. A total of 200-point observation was designed with the aim of a field survey across the Marvdasht Plain in Fars Province, Iran. After sampling from topsoil (0 to 30 cm) and subsoil depths (30 to 60 cm), the samples were transferred to the laboratory to determine the mean weight diameter (MWD) and geometric mean diameter (GMD) of aggregates in the laboratory. In addition, shear strength (SS) and penetration resistance (PR) were measured directly during the field survey. In parallel, 79 environmental factors were prepared from topographic and remote sensing data. Four soil variables were also included in the modeling process, as they were co-located with SPM properties based on expert opinion. For selecting the most influential covariates, the variance inflation factor (VIF) and Boruta methods were employed. Two covariate dataset scenarios were used to assess the impact of soil and environmental factors on the modeling of SPM properties including SPM and environmental covariates (scenario 1) and SPM, environmental covariates, and soil variables (scenario 2). From all covariates, nine soil and environmental factors were selected for modeling the SPM properties, of which four of them were the soil variables, three were related to remote sensing, and two factors had topographic sources. The results indicated that scenario 2 outperformed in all standard depths. The findings suggested that clay and SOM are key factors in predicting SPM, highlighting the importance of considering soil variables in addition to environmental covariates for enhancing the accuracy of machine learning prediction. The k-nearest neighbor algorithm was found to be highly effective in predicting SPM, while the random forest algorithm yielded the highest R2 value (0.92) for penetration resistance properties at 15-30 depth. Overall, the approach used in this research has the potential to be extended beyond the Marvdasht Plain of Fars Province, Iran, as well as to other regions worldwide with comparable soil-forming factors. Moreover, this study provides a valuable framework for the digital mapping of SPM properties, serving as a guide for future studies seeking to predict SPM properties. Globally, the output of this research has important significance for soil management and conservation efforts and can facilitate the development of sustainable agricultural practices.
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Affiliation(s)
- Pegah Khosravani
- Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Majid Baghernejad
- Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran.
| | - Ali Akbar Moosavi
- Department of Soil Science, College of Agriculture, Shiraz University, Shiraz, Iran
| | - Meisam Rezaei
- Soil and Water Research Institute (SWRI), Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
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De Geeter S, Verstraeten G, Poesen J, Campforts B, Vanmaercke M. A data driven gully head susceptibility map of Africa at 30 m resolution. ENVIRONMENTAL RESEARCH 2023; 224:115573. [PMID: 36841523 DOI: 10.1016/j.envres.2023.115573] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 06/18/2023]
Abstract
Predicting gully erosion at the continental scale is challenging with current generation models. Moreover, datasets reflecting gully erosion processes are still rather scarce, especially in Africa. This study aims to bridge this gap by collecting an extensive dataset and developing a robust, empirical model that predicts gully head density at high resolution for the African continent. We developed a logistic probability model at 30 m resolution that predicts the likelihood of gully head occurrence using currently available GIS data sources. To calibrate and validate this model, we used a new database of 31,531 gully heads, mapped over 1216 sites across Africa. The exact location of all gully heads was manually mapped by trained experts using high-resolution imagery available from Google Earth. This allowed the extraction of detailed information at the gully head scale, such as the local soil surface slope. Variables included in our empirical model are topography, climate, vegetation, soil characteristics and tectonic context. They are consistent with our current process-based understanding of gully formation and evolution. The model shows that gully occurrences mainly depend on slope steepness, soil texture and vegetation cover and to a lesser extent on rainfall intensity and tectonic activity. The combination of these factors allows for robust and fairly reliable predictions of gully head occurrences, with Areas Under the Curve for validation around 0.8. Based on these results, we present the first gully head susceptibility map for Africa at a 30 m resolution.
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Affiliation(s)
- Sofie De Geeter
- KU Leuven, Division of Geography and Tourism, Department of Earth and Environmental Sciences, Celestijnenlaan 200E, 3001, Heverlee, Belgium; University of Liège, Department of Geography, Clos Mercator 3, 4000, Liège, Belgium; Research Foundation Flanders - FWO, Brussels, Belgium.
| | - Gert Verstraeten
- KU Leuven, Division of Geography and Tourism, Department of Earth and Environmental Sciences, Celestijnenlaan 200E, 3001, Heverlee, Belgium
| | - Jean Poesen
- KU Leuven, Division of Geography and Tourism, Department of Earth and Environmental Sciences, Celestijnenlaan 200E, 3001, Heverlee, Belgium; Maria-Curie Sklodowska University, Institute of Earth and Environmental Sciences, Kraśnicka Av. 2d, 20-718, Lublin, Poland
| | - Benjamin Campforts
- Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA
| | - Matthias Vanmaercke
- KU Leuven, Division of Geography and Tourism, Department of Earth and Environmental Sciences, Celestijnenlaan 200E, 3001, Heverlee, Belgium; University of Liège, Department of Geography, Clos Mercator 3, 4000, Liège, Belgium
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Feeney CJ, Robinson DA, Thomas ARC, Borrelli P, Cooper DM, May L. Agricultural practices drive elevated rates of topsoil decline across Kenya, but terracing and reduced tillage can reverse this. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 870:161925. [PMID: 36736388 DOI: 10.1016/j.scitotenv.2023.161925] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
As agricultural land area increases to feed an expanding global population, soil erosion will likely accelerate, generating unsustainable losses of soil and nutrients. This is critical for Kenya where cropland expansion and nutrient loading from runoff and erosion is contributing to eutrophication of freshwater ecosystems and desertification. We used the Revised Universal Soil Loss Equation (RUSLE) to predict soil erosion rates under present land cover and potential natural vegetation nationally across Kenya. Simulating natural vegetation conditions allows the degree to which erosion rates are elevated under current land use practices to be determined. This methodology exploits new digital soil maps and two vegetation cover maps to model topsoil (top 20 cm) erosion rates, lifespans (the mass of topsoil divided by erosion rate), and lateral nutrient fluxes (nutrient concentration times erosion rate) under both scenarios. We estimated the mean soil erosion rate under current land cover at ~5.5 t ha-1 yr-1, ~3 times the rate estimated for natural vegetation cover (~1.8 t ha-1 yr-1), and equivalent to ~320 Mt yr-1 of topsoil lost nationwide. Under present erosion rates, ~8.8 Mt, ~315 Kt, and ~ 110 Kt of soil organic carbon, nitrogen and phosphorous are lost from soil every year, respectively. Further, 5.3 % of topsoils (~3.1 Mha), including at >25 % of croplands, have short lifespans (<100 years). Additional scenarios were tested that assume combinations of terracing and reduced tillage practices were adopted on croplands to mitigate erosion. Establishing bench terraces with zoned tillage could reduce soil losses by ≥75 %; up to 87.1 t ha-1 yr-1. These reductions are comparable to converting croplands to natural vegetation, demonstrating most agricultural soils can be conserved successfully. Extensive long-term monitoring of croplands with terraces and reduced tillage established is required to verify the efficacy of these agricultural support practices as indicated by our modelling.
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Affiliation(s)
- Christopher J Feeney
- UK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd LL57 2UW, UK.
| | - David A Robinson
- UK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd LL57 2UW, UK
| | - Amy R C Thomas
- UK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd LL57 2UW, UK
| | - Pasquale Borrelli
- Department of Science, Roma Tre University, Viale Guglielmo Marconi, 446, 00146 Rome, Italy
| | - David M Cooper
- UK Centre for Ecology & Hydrology, Environment Centre Wales, Deiniol Road, Bangor, Gwynedd LL57 2UW, UK
| | - Linda May
- UK Centre for Ecology & Hydrology, Bush Estate, Penicuik, Midlothian EH26 OQB, UK
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Jemo M, Devkota KP, Epule TE, Chfadi T, Moutiq R, Hafidi M, Silatsa FBT, Jibrin JM. Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria. FRONTIERS IN PLANT SCIENCE 2023; 14:1120826. [PMID: 37113594 PMCID: PMC10126304 DOI: 10.3389/fpls.2023.1120826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
Rapid and accurate soybean yield prediction at an on-farm scale is important for ensuring sustainable yield increases and contributing to food security maintenance in Nigeria. We used multiple approaches to assess the benefits of rhizobium (Rh) inoculation and phosphorus (P) fertilization on soybean yield increase and profitability from large-scale conducted trials in the savanna areas of Nigeria [i.e., the Sudan Savanna (SS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS)]. Soybean yield results from the established trials managed by farmers with four treatments (i.e., the control without inoculation and P fertilizer, Rh inoculation, P fertilizer, and Rh + P combination treatments) were predicted using mapped soil properties and weather variables in ensemble machine-learning techniques, specifically the conditional inference regression random forest (RF) model. Using the IMPACT model, scenario analyses were employed to simulate long-term adoption impacts on national soybean trade and currency. Our study found that yields of the Rh + P combination were consistently higher than the control in the three agroecological zones. Average yield increases were 128%, 111%, and 162% higher in the Rh + P combination compared to the control treatment in the SS, NGS, and SGS agroecological zones, respectively. The NGS agroecological zone showed a higher yield than SS and SGS. The highest training coefficient of determination (R2 = 0.75) for yield prediction was from the NGS dataset, and the lowest coefficient (R2 = 0.46) was from the SS samples. The results from the IMPACT model showed a reduction of 10% and 22% for the low (35% adoption scenario) and high (75% adoption scenario) soybean imports from 2029 in Nigeria, respectively. A significant reduction in soybean imports is feasible if the Rh + P inputs are large-scaled implemented at the on-farm field and massively adopted by farmers in Nigeria.
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Affiliation(s)
- Martin Jemo
- AgroBiosciences Program, College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco
| | - Krishna Prasad Devkota
- Soil, Water, and Agronomy (SWA) Program, International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat-institute, Rabat, Morocco
| | - Terence Epule Epule
- International Water Research Institute (IWRI), College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco
| | - Tarik Chfadi
- International Water Research Institute (IWRI), College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco
| | - Rkia Moutiq
- National Institute of Agronomical Research (INRA), Regional Center of Kenitra, Kenitra, Morocco
| | - Mohamed Hafidi
- AgroBiosciences Program, College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Bengeurir, Morocco
- Cadi Ayad University, Laboratory of Microbial Biotechnologies, Agrosciences and Environment, Faculty of Science Semlalia, Marrakesh, Morocco
| | - Francis B. T. Silatsa
- Center of Excellence for Soil and Fertilizer Research in Africa (CESFRA), College for Sustainable Agriculture and Environmental Sciences, Mohammed VI Polytechnic University (UM6P), Benguerir, Morocco
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Vanhuysse S, Diédhiou SM, Grippa T, Georganos S, Konaté L, Niang EHA, Wolff E. Fine-scale mapping of urban malaria exposure under data scarcity: an approach centred on vector ecology. Malar J 2023; 22:113. [PMID: 37009873 PMCID: PMC10069057 DOI: 10.1186/s12936-023-04527-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 03/08/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Although malaria transmission has experienced an overall decline in sub-Saharan Africa, urban malaria is now considered an emerging health issue due to rapid and uncontrolled urbanization and the adaptation of vectors to urban environments. Fine-scale hazard and exposure maps are required to support evidence-based policies and targeted interventions, but data-driven predictive spatial modelling is hindered by gaps in epidemiological and entomological data. A knowledge-based geospatial framework is proposed for mapping the heterogeneity of urban malaria hazard and exposure under data scarcity. It builds on proven geospatial methods, implements open-source algorithms, and relies heavily on vector ecology knowledge and the involvement of local experts. METHODS A workflow for producing fine-scale maps was systematized, and most processing steps were automated. The method was evaluated through its application to the metropolitan area of Dakar, Senegal, where urban transmission has long been confirmed. Urban malaria exposure was defined as the contact risk between adult Anopheles vectors (the hazard) and urban population and accounted for socioeconomic vulnerability by including the dimension of urban deprivation that is reflected in the morphology of the built-up fabric. Larval habitat suitability was mapped through a deductive geospatial approach involving the participation of experts with a strong background in vector ecology and validated with existing geolocated entomological data. Adult vector habitat suitability was derived through a similar process, based on dispersal from suitable breeding site locations. The resulting hazard map was combined with a population density map to generate a gridded urban malaria exposure map at a spatial resolution of 100 m. RESULTS The identification of key criteria influencing vector habitat suitability, their translation into geospatial layers, and the assessment of their relative importance are major outcomes of the study that can serve as a basis for replication in other sub-Saharan African cities. Quantitative validation of the larval habitat suitability map demonstrates the reliable performance of the deductive approach, and the added value of including local vector ecology experts in the process. The patterns displayed in the hazard and exposure maps reflect the high degree of heterogeneity that exists throughout the city of Dakar and its suburbs, due not only to the influence of environmental factors, but also to urban deprivation. CONCLUSIONS This study is an effort to bring geospatial research output closer to effective support tools for local stakeholders and decision makers. Its major contributions are the identification of a broad set of criteria related to vector ecology and the systematization of the workflow for producing fine-scale maps. In a context of epidemiological and entomological data scarcity, vector ecology knowledge is key for mapping urban malaria exposure. An application of the framework to Dakar showed its potential in this regard. Fine-grained heterogeneity was revealed by the output maps, and besides the influence of environmental factors, the strong links between urban malaria and deprivation were also highlighted.
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Affiliation(s)
- Sabine Vanhuysse
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles (ULB), 1050, Brussels, Belgium.
| | - Seynabou Mocote Diédhiou
- Laboratoire d'Ecologie Vectorielle et Parasitaire, Université Cheikh-Anta-Diop de Dakar, Dakar, Sénégal
| | - Taïs Grippa
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles (ULB), 1050, Brussels, Belgium
| | - Stefanos Georganos
- Geomatics, Department of Environmental and Life Sciences, Faculty of Health, Science and Technology, Karlstad University, Karlstad, Sweden
| | - Lassana Konaté
- Laboratoire d'Ecologie Vectorielle et Parasitaire, Université Cheikh-Anta-Diop de Dakar, Dakar, Sénégal
| | - El Hadji Amadou Niang
- Laboratoire d'Ecologie Vectorielle et Parasitaire, Université Cheikh-Anta-Diop de Dakar, Dakar, Sénégal
| | - Eléonore Wolff
- Department of Geosciences, Environment and Society, Université Libre de Bruxelles (ULB), 1050, Brussels, Belgium
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Odebiri O, Mutanga O, Odindi J, Naicker R. Mapping soil organic carbon distribution across South Africa's major biomes using remote sensing-topo-climatic covariates and Concrete Autoencoder-Deep neural networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 865:161150. [PMID: 36587704 DOI: 10.1016/j.scitotenv.2022.161150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 12/05/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
The management of soil organic carbon (SOC) stocks remains at the forefront of greenhouse gas mitigation. However, unprecedented anthropogenic disturbances emanating from continued land-use change have significantly altered SOC distribution across global biomes leading to considerable carbon losses. Consequently, understanding the spatial distribution of SOC across different biomes, particularly at larger scales, is critical for climate change policy formulation and planning. Advancements in remote sensing, availability of big data, and deep learning architecture offer great potential in large-scale SOC mapping. In this regard, this study mapped SOC distribution across South Africa's major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep neural networks (CAE-DNN). From the different deep neural frameworks tested, the CAE-DNN model (developed from 26 selected covariates) achieved the best accuracy with an RMSE value of 7.91 t/ha (about 20 % of the mean). Results further showed that SOC stock correlated with general biome coverage, as the Grassland and Savanna biomes contributed the most (32.38 % and 31.28 %) to the overall SOC pool in South Africa. However, despite their smaller footprint, Forests (44.12 t/h) and the Indian Ocean Coastal Belt (43.05 t/h) biomes demonstrated the highest SOC sequestration capacity. The restoration of degraded biomes is advocated for, in order to boost SOC storage; but a balance between carbon sequestration capacity, biodiversity health, and the adequate provision of ecosystem services must be maintained. To this end, these findings provide a guideline to facilitate sustainable SOC stock management within South Africa's major biomes and indeed other regions of the world.
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Affiliation(s)
- Omosalewa Odebiri
- Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa.
| | - Onisimo Mutanga
- Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
| | - John Odindi
- Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
| | - Rowan Naicker
- Discipline of Geography, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
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Artificial Intelligence in Food Safety: A Decade Review and Bibliometric Analysis. Foods 2023; 12:foods12061242. [PMID: 36981168 PMCID: PMC10048131 DOI: 10.3390/foods12061242] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/06/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023] Open
Abstract
Artificial Intelligence (AI) technologies have been powerful solutions used to improve food yield, quality, and nutrition, increase safety and traceability while decreasing resource consumption, and eliminate food waste. Compared with several qualitative reviews on AI in food safety, we conducted an in-depth quantitative and systematic review based on the Core Collection database of WoS (Web of Science). To discover the historical trajectory and identify future trends, we analysed the literature concerning AI technologies in food safety from 2012 to 2022 by CiteSpace. In this review, we used bibliometric methods to describe the development of AI in food safety, including performance analysis, science mapping, and network analysis by CiteSpace. Among the 1855 selected articles, China and the United States contributed the most literature, and the Chinese Academy of Sciences released the largest number of relevant articles. Among all the journals in this field, PLoS ONE and Computers and Electronics in Agriculture ranked first and second in terms of annual publications and co-citation frequency. The present character, hot spots, and future research trends of AI technologies in food safety research were determined. Furthermore, based on our analyses, we provide researchers, practitioners, and policymakers with the big picture of research on AI in food safety across the whole process, from precision agriculture to precision nutrition, through 28 enlightening articles.
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14
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Buenemann M, Coetzee ME, Kutuahupira J, Maynard JJ, Herrick JE. Errors in soil maps: The need for better on-site estimates and soil map predictions. PLoS One 2023; 18:e0270176. [PMID: 36630410 PMCID: PMC9833593 DOI: 10.1371/journal.pone.0270176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 06/06/2022] [Indexed: 01/12/2023] Open
Abstract
High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.
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Affiliation(s)
- Michaela Buenemann
- Department of Geography, New Mexico State University, Las Cruces, New Mexico, United States of America
- * E-mail:
| | - Marina E. Coetzee
- Faculty of Natural Resources and Spatial Sciences, Namibia University of Science and Technology, Windhoek, Namibia
| | - Josephat Kutuahupira
- Division of Plant Production, Ministry of Agriculture, Water, and Forestry, Windhoek, Namibia
| | - Jonathan J. Maynard
- Sustainability Innovation Lab, University of Colorado, Boulder, Colorado, United States of America
| | - Jeffrey E. Herrick
- Jornada Experimental Range, Agricultural Research Service, United States Department of Agriculture, Las Cruces, New Mexico, United States of America
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van Dijk M, de Lange T, van Leeuwen P, Debie P. Occupations on the map: Using a super learner algorithm to downscale labor statistics. PLoS One 2022; 17:e0278120. [PMID: 36476753 PMCID: PMC9728836 DOI: 10.1371/journal.pone.0278120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 11/10/2022] [Indexed: 12/12/2022] Open
Abstract
Detailed and accurate labor statistics are fundamental to support social policies that aim to improve the match between labor supply and demand, and support the creation of jobs. Despite overwhelming evidence that labor activities are distributed unevenly across space, detailed statistics on the geographical distribution of labor and work are not readily available. To fill this gap, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics, informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The super learners outperformed (n = 6) or had similar (n = 1) accuracy in comparison to best-performing single machine learning algorithms. A comparison with an independent high-resolution wealth index showed that the shares of the four low-skilled occupation categories (91% of the labor force), were able to explain between 28% and 43% of the spatial variation in wealth in Vietnam, pointing at a strong spatial relationship between work, income and wealth. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.
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Affiliation(s)
- Michiel van Dijk
- Wageningen Economic Research, the Hague, the Netherlands
- International Institute for Applied Systems Analysis, Laxenburg, Austria
- * E-mail:
| | - Thijs de Lange
- Wageningen Economic Research, the Hague, the Netherlands
| | | | - Philippe Debie
- Marketing and Consumer Behaviour Group, Wageningen University, Wageningen, The Netherlands
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Gruszczyński S, Gruszczyński W. Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:15210. [PMID: 36429929 PMCID: PMC9691257 DOI: 10.3390/ijerph192215210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 06/16/2023]
Abstract
The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the determination of many soil properties by reference methods. The modeled subset of these properties included texture (three variables), bulk density, moisture content at soil water characteristic curves (SWCC, 4 variables), total and organic C and total N content (3 variables), total elemental content (32 variables), elemental content in bioavailable forms (12 variables), electrical conductivity, exchangeable acidity, exchangeable bases, pH, and phosphorus sorption index. It is not possible to indicate a universal optimal prediction model for all soil variables. The best prediction results are provided by all regression models for total and organic C, total Fe, total Al and bioavailable Al content, and pH. For bulk density, total N and total K content satisfactory results are provided by specific model type. Many other properties, i.e., texture, SWCC, total Ga, Rb, Na, Ca, Cu, Pb, Hg content, and bioavailable Ca and K content, can be predicted with accuracies sufficient for some less demanding tasks.
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Ntalo M, Ravhuhali KE, Moyo B, Mmbi NE, Mokoboki KH. Physical and chemical properties of the soils in selected communal property associations of South Africa. PeerJ 2022; 10:e13960. [PMID: 36530403 PMCID: PMC9753764 DOI: 10.7717/peerj.13960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 08/07/2022] [Indexed: 01/19/2023] Open
Abstract
Communal Property Associations (CPAs) rangeland users need more knowledge on the state of their respective grazing lands and also the interaction of soil properties with grazing management implemented. This study aimed to investigate the effect grazing has on the physical and chemical properties of four different soil types found in selected CPAs of the Bela-Bela municipality, they are as follows: Mawela (Hutton-clay loam: HCL), Bela-Bela (Hutton-clay: HC), Moretele (Hutton-loamy sand: HLS) and Ramorula (Ecca sand-clay loam: ESCL).The macro and micro minerals, pH, nitrate-nitrogen, ammonium-nitrogen, organic carbon, soil particle size distribution, acidity and resistance were all measured. All data were subjected to two-way factorial analysis of variance (SAS, 2010). The topsoil was sampled at a depth of 300 mm at an interval of 100 m (100 and 200 m) from the same transect used for woody species data collection resulting in a total of 18 samples per CPA. In each CPA, three camps were selected. In each camp, three transects 200 m apart at the length of 200 m were set. In each transect, soils were drawn at 0, 100 and 200 m making a total of nine soil samples per each camp. The highest (P < 0.05) pH (7.14) recorded on the sub-soil was in HLS. Nitrate nitrogen (2.4 mg/kg) concentration on the topsoil was high (P < 0.05) in HC soil type. Soil organic carbon for both topsoil (0.66%) and subsoil (0.41%) was significantly lower (P < 0.05) in HLS soil type and ESCL soil type respectively. Phosphorus concentration was significantly high (P < 0.05) in ESCL soil type for both topsoil (12.86 mg/kg) and sub-soil (1.59 mg/kg). Iron concentration was high in both topsoil (11.8 mg/kg) and sub-soil (7.3 mg/kg) in ESCL soil type. Sub-soil manganese concentration was found to be higher (P < 0.05) in ESCL soil type (7.58 mg/kg). Soil resistance (2880 Ω ) measured in topsoil was high (P < 0.05) in HCL soil type compared to other soil types. Moreover, for the sub-soil the highest (P < 0.05) resistance (least salts) (3640 Ω) was recorded in ESCL soil type. For most of the soil types, the mineral concentration was higher in topsoil than in sub-soil, this trend explains that the uptake of these minerals by plants took place due to the inconsistencies of grazing management employed in these selected CPA farms. It is of colossal significance to properly manage rangelands, to allow a fair-to-good herbaceous layer in the presence of minerals in the soils and farmer should prioritize having enclosures and keeping N-fixing tree species in the rangelands to achieve the above mentioned conditions.
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Affiliation(s)
- Malizo Ntalo
- Food Security and Safety Niche Area, North-West University, Mahikeng, North West Province, South Africa,Department of Animal Science, School of Agricultural Sciences, North-West University, Mahikeng, North West Province, South Africa
| | - Khuliso Emmanuel Ravhuhali
- Food Security and Safety Niche Area, North-West University, Mahikeng, North West Province, South Africa,Department of Animal Science, School of Agricultural Sciences, North-West University, Mahikeng, North West Province, South Africa
| | - Bethwell Moyo
- Department of Animal Production, Fort Cox Agriculture and Forestry Training Institute, Middledrift, Eastern Cape, South Africa
| | - Ntuwiseni Emile Mmbi
- Department of Agriculture and Rural Development, Veld and Pasture Component, Tawoomba Agricultural Research Station, Bela-Bela, Limpopo Province, South Africa
| | - Kwena Hilda Mokoboki
- Food Security and Safety Niche Area, North-West University, Mahikeng, North West Province, South Africa,Department of Animal Science, School of Agricultural Sciences, North-West University, Mahikeng, North West Province, South Africa
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Abraham A, Duvall E, Ferraro K, Webster A, Doughty C, le Roux E, Ellis‐Soto D. Understanding anthropogenic impacts on zoogeochemistry is essential for ecological restoration. Restor Ecol 2022. [DOI: 10.1111/rec.13778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Andrew Abraham
- School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff USA
| | - Ethan Duvall
- Department of Ecology and Evolutionary Biology Cornell University Ithaca USA
| | - Kristy Ferraro
- School of the Environment Yale University Connecticut USA
| | - Andrea Webster
- Mammal Research Institute University of Pretoria Pretoria South Africa
| | - Chris Doughty
- School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff USA
| | - Elizabeth le Roux
- Mammal Research Institute University of Pretoria Pretoria South Africa
- Centre for Biodiversity Dynamics in a Changing World (BIOCHANGE), Section of EcoInformatics and Biodiversity, Department of Biology Aarhus University Denmark
- Environmental Change Institute, School of Geography and the Environment University of Oxford Oxford UK
| | - Diego Ellis‐Soto
- Department of Ecology and Evolutionary Biology Yale University Connecticut USA
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Zeyliger A, Chinilin A, Ermolaeva O. Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region). SENSORS (BASEL, SWITZERLAND) 2022; 22:6153. [PMID: 36015913 PMCID: PMC9414959 DOI: 10.3390/s22166153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
The implementation of the sustainable management of the interaction between agriculture and the environment requires an increasingly deep understanding and numerical description of the soil genesis and properties of soils. One of the areas of application of relevant knowledge is digital irrigated agriculture. During the development of such technologies, the traditional methods of soil research can be quite expensive and time consuming. Proximal soil sensing in combination with predictive soil mapping can significantly reduce the complexity of the work. In this study, we used topographic variables and data from the Electromagnetic Induction Meter (EM38-mk) in combination with soil surface hydrological variables to produce cartographic models of the gravimetric soil moisture for a number of depth intervals. For this purpose, in dry steppe zone conditions, a test site was organized. It was located at the border of the parcel containing the irrigated soybean crop, where 50 soil samples were taken at different points alongside electrical conductivity data (ECa) measured in situ in the field. The modeling of the gravimetric soil moisture was carried out with the stepwise inclusion of independent variables, using methods of ensemble machine learning and spatial cross-validation. The obtained cartographic models showed satisfactory results with the best performance R2cv 0.59-0.64. The best combination of predictors that provided the best results of the model characteristics for predicting gravimetric soil moisture were geographical variables (buffer zone distances) in combination with the initial variables converted into the principal components. The cartographic models of the gravimetric soil moisture variability obtained this way can be used to solve the problems of managed irrigated agriculture, applying fertilizers at variable rates, thereby optimizing the use of resources by crop producers, which can ultimately contribute to the sustainable management of natural resources.
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Affiliation(s)
- Anatoly Zeyliger
- Saratov State University of Genetics, Biotechnology and Engineering Named after N.I. Vavilov, 410012 Saratov, Russia
| | - Andrey Chinilin
- Department of Soil Geography, FRC “V.V. Dokuchaev Soil Science Institute”, 119017 Moscow, Russia
| | - Olga Ermolaeva
- Department of Applied Informatics, Russian State Agrarian University—Moscow Timiryazev Agricultural Academy, 127550 Moscow, Russia
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Chivenge P, Zingore S, Ezui K, Njoroge S, Bunquin M, Dobermann A, Saito K. Progress in research on site-specific nutrient management for smallholder farmers in sub-Saharan Africa. FIELD CROPS RESEARCH 2022; 281:108503. [PMID: 35582149 PMCID: PMC8935389 DOI: 10.1016/j.fcr.2022.108503] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/09/2022] [Accepted: 02/22/2022] [Indexed: 05/08/2023]
Abstract
Increasing fertilizer access and use is an essential component for improving crop production and food security in sub-Saharan Africa (SSA). However, given the heterogeneous nature of smallholder farms, fertilizer application needs to be tailored to specific farming conditions to increase yield, profitability, and nutrient use efficiency. The site-specific nutrient management (SSNM) approach initially developed in the 1990 s for generating field-specific fertilizer recommendations for rice in Asia, has also been introduced to rice, maize and cassava cropping systems in SSA. The SSNM approach has been shown to increase yield, profitability, and nutrient use efficiency. Yield gains of rice and maize with SSNM in SSA were on average 24% and 69% when compared to the farmer practice, respectively, or 11% and 4% when compared to local blanket fertilizer recommendations. However, there is need for more extensive field evaluation to quantify the broader benefits of the SSNM approach in diverse farming systems and environments. Especially for rice, the SSNM approach should be expanded to rainfed systems, which are dominant in SSA and further developed to take into account soil texture and soil water availability. Digital decision support tools such as RiceAdvice and Nutrient Expert can enable wider dissemination of locally relevant SSNM recommendations to reach large numbers of farmers at scale. One of the major limitations of the currently available SSNM decision support tools is the requirement of acquiring a significant amount of farm-specific information needed to formulate SSNM recommendations. The scaling potential of SSNM will be greatly enhanced by integration with other agronomic advisory platforms and seamless integration of digital soil, climate and crop information to improve predictions of SSNM recommendations with reduced need for on-farm data collection. Uncertainty should also be included in future solutions, primarily to also better account for varying prices and economic outcomes.
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Affiliation(s)
- P. Chivenge
- African Plant Nutrition Institute, UM6P Experimental Farm, Benguérir 41350, Morocco
- Corresponding author.
| | - S. Zingore
- African Plant Nutrition Institute, UM6P Experimental Farm, Benguérir 41350, Morocco
| | - K.S. Ezui
- African Plant Nutrition Institute, ICIPE Campus, Duduville – Kasarani, Thika Road, Nairobi, Kenya
| | - S. Njoroge
- African Plant Nutrition Institute, ICIPE Campus, Duduville – Kasarani, Thika Road, Nairobi, Kenya
| | - M.A. Bunquin
- Analytical Services Laboratory, Department of Soil Science, Agricultural Systems Institute, College of Agriculture and Food Sciences, University of the Philippines, College, Los Baños, Laguna 4031, Philippines
| | - A. Dobermann
- International Fertilizer Association (IFA), 49, Avenue d′Iena, 75116 Paris, France
| | - K. Saito
- Africa Rice Center (AfricaRice), 01 B.P. 2551, Bouaké 01, Côte d′Ivoire
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The Role of Remote Sensing Data and Methods in a Modern Approach to Fertilization in Precision Agriculture. REMOTE SENSING 2022. [DOI: 10.3390/rs14030778] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The precision fertilization system is the basis for upgrading conventional intensive agricultural production, while achieving both high and quality yields and minimizing the negative impacts on the environment. This research aims to present the application of both conventional and modern prediction methods in precision fertilization by integrating agronomic components with the spatial component of interpolation and machine learning. While conventional methods were a cornerstone of soil prediction in the past decades, new challenges to process larger and more complex data have reduced their viability in the present. Their disadvantages of lower prediction accuracy, lack of robustness regarding the properties of input soil sample values and requirements for extensive cost- and time-expensive soil sampling were addressed. Specific conventional (ordinary kriging, inverse distance weighted) and modern machine learning methods (random forest, support vector machine, artificial neural networks, decision trees) were evaluated according to their popularity in relevant studies indexed in the Web of Science Core Collection over the past decade. As a shift towards increased prediction accuracy and computational efficiency, an overview of state-of-the-art remote sensing methods for improving precise fertilization was completed, with the accent on open-data and global satellite missions. State-of-the-art remote sensing techniques allowed hybrid interpolation to predict the sampled data supported by remote sensing data such as high-resolution multispectral, thermal and radar satellite or unmanned aerial vehicle (UAV)-based imagery in the analyzed studies. The representative overview of conventional and modern approaches to precision fertilization was performed based on 121 samples with phosphorous pentoxide (P2O5) and potassium oxide (K2O) in a common agricultural parcel in Croatia. It visually and quantitatively confirmed the superior prediction accuracy and retained local heterogeneity of the modern approach. The research concludes that remote sensing data and methods have a significant role in improving fertilization in precision agriculture today and will be increasingly important in the future.
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Yang RM, Liu LA, Zhang X, He RX, Zhu CM, Zhang ZQ, Li JG. Exploring the likely relationship between soil carbon change and environmental controls using nonrevisited temporal data sets: Mapping soil carbon dynamics across China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 800:149312. [PMID: 34392206 DOI: 10.1016/j.scitotenv.2021.149312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/23/2021] [Accepted: 07/23/2021] [Indexed: 06/13/2023]
Abstract
The prediction of soil organic carbon (SOC) changes in response to environmental change is often limited by a scarcity of revisited temporal data, which constrains scientific understanding and realistic predictions of soil carbon change. The present study improved the potential of nonrevisited temporal data in the prediction of SOC stocks (SOCS) variations. We proposed a method to develop predictions of SOCS change using two independent temporal data sets (pertaining to the 1980s and 2010s) in China based on the digital soil mapping technique. Changes in SOCS over time at the site level were analyzed via the interpolation of missing SOCS values in each data set. Quantitative SOCS change predictions were generated by modeling the relationship between SOCS change and variables that represent changes in climate, vegetation indices, and land cover. The scale-dependent response of SOCS change to these environmental dynamics was assessed. On average, a slight increase was observed from 3.70 kg m-2 in the 1980s to 4.53 kg m-2 in the 2010s. The proposed approach attained moderate accuracy with an R2 value of 0.32 and a root mean squared error (RMSE) of 1.73 kg m-2. We found that changes in climate factors were dominant controls of SOCS change over time at the country scale. At the regional scale, the controlling factors of SOCS change were distinct and variable. Our case study may be of value in the application of independent temporal data sets to analyze soil carbon change on multiple scales. The method may be used to resolve questions of soil carbon change projections and provide an alternative solution to predict likely changes in soil carbon in response to future environmental change when no temporal data are available.
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Affiliation(s)
- Ren-Min Yang
- School of Geography, Geomatics, and Planning, Jiangsu Normal University, Xuzhou 221116, China.
| | - Li-An Liu
- School of Geography, Geomatics, and Planning, Jiangsu Normal University, Xuzhou 221116, China.
| | - Xin Zhang
- State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China.
| | - Ri-Xing He
- College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China; Key Laboratory of 3D Information Acquisition and Application, MOE, Capital Normal University, Beijing 100048, China.
| | - Chang-Ming Zhu
- School of Geography, Geomatics, and Planning, Jiangsu Normal University, Xuzhou 221116, China.
| | - Zhong-Qi Zhang
- School of Geography, Geomatics, and Planning, Jiangsu Normal University, Xuzhou 221116, China.
| | - Jian-Guo Li
- School of Geography, Geomatics, and Planning, Jiangsu Normal University, Xuzhou 221116, China.
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Monitoring and Analyzing Yield Gap in Africa through Soil Attribute Best Management Using Remote Sensing Approaches: A Review. REMOTE SENSING 2021. [DOI: 10.3390/rs13224602] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Africa has the largest population growth rate in the world and an agricultural system characterized by the predominance of smallholder farmers. Improving food security in Africa will require a good understanding of farming systems yields as well as reducing yield gaps (i.e., the difference between potential yield and actual farmer yield). To this end, crop yield gap practices in African countries need to be understood to fill this gap while decreasing the environmental impacts of agricultural systems. For instance, the variability of yields has been demonstrated to be strongly controlled by soil fertilizer use, irrigation management, soil attribute, and the climate. Consequently, the quantitative assessment and mapping information of soil attributes such as nitrogen (N), phosphorus (P), potassium (K), soil organic carbon (SOC), moisture content (MC), and soil texture (i.e., clay, sand and silt contents) on the ground are essential to potentially reducing the yield gap. However, to assess, measure, and monitor these soil yield-related parameters in the field, there is a need for rapid, accurate, and inexpensive methods. Recent advances in remote sensing technologies and high computational performances offer a unique opportunity to implement cost-effective spatiotemporal methods for estimating crop yield with important levels of scalability. However, researchers and scientists in Africa are not taking advantage of the opportunity of increasingly available geospatial remote sensing technologies and data for yield studies. The objectives of this report are to (i) conduct a review of scientific literature on the current status of African yield gap analysis research and their variation in regard to soil properties management by using remote sensing techniques; (ii) review and describe optimal yield practices in Africa; and (iii) identify gaps and limitations to higher yields in African smallholder farms and propose possible improvements. Our literature reviewed 80 publications and covered a period of 22 years (1998-2020) over many selected African countries with a potential yield improvement. Our results found that (i) the number of agriculture yield-focused remote sensing studies has gradually increased, with the largest proportion of studies published during the last 15 years; (ii) most studies were conducted exclusively using multispectral Landsat and Sentinel sensors; and (iii) over the past decade, hyperspectral imagery has contributed to a better understanding of yield gap analysis compared to multispectral imagery; (iv) soil nutrients (i.e., NPK) are not the main factor influencing the studied crop productivity in Africa, whereas clay, SOC, and soil pH were the most examined soil properties in prior papers.
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Abstract
We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.
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Miller MAE, Shepherd KD, Kisitu B, Collinson J. iSDAsoil: The first continent-scale soil property map at 30 m resolution provides a soil information revolution for Africa. PLoS Biol 2021; 19:e3001441. [PMID: 34723965 PMCID: PMC8584968 DOI: 10.1371/journal.pbio.3001441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Revised: 11/11/2021] [Indexed: 11/18/2022] Open
Abstract
Open access, high-resolution soil property maps have been created for Africa at 30 m resolution, using machine learning trained on over 100,000 analysed soil samples. Combined with other field-level information, iSDAsoil enables the possibility of site-specific agronomy advisory for smallholder farmers.
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Affiliation(s)
- Matthew A. E. Miller
- Innovative Solutions for Decision Agriculture Ltd (iSDA), Harpenden, United Kingdom
- * E-mail:
| | - Keith D. Shepherd
- Innovative Solutions for Decision Agriculture Ltd (iSDA), Harpenden, United Kingdom
- World Agroforestry Centre (CIFOR-ICRAF), Nairobi, Kenya
| | - Bruce Kisitu
- Innovative Solutions for Decision Agriculture Ltd (iSDA), Harpenden, United Kingdom
| | - Jamie Collinson
- Innovative Solutions for Decision Agriculture Ltd (iSDA), Harpenden, United Kingdom
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