Your search found 3 records
1 Nauman, T. W.; Ely, C. P.; Miller, M. P.; Duniway, M. C. 2019. Salinity yield modeling of the upper Colorado River Basin using 30-m resolution soil maps and random forests. Water Resources Research, 55(6):4954-4973. [doi: https://doi.org/10.1029/2018WR024054]
Salinity control ; Models ; River basins ; Erosion ; Risk analysis ; Soil maps ; Soil properties ; Flood irrigation ; Land cover ; Land use ; Catchment areas ; Runoff ; Hydrological factors ; Forecasting / USA / Colorado River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049250)
(2.33 MB)
Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upward of $300 million U.S. dollars annually. Salinity source models have generally included coarse spatial data to represent nonagriculture sources. We developed new predictive soil property and cover maps at 30-m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices were also created based on soil properties, remotely sensed bare ground exposure, and topographic factors to examine potential surface soil erosion drivers. These new maps and data from previous SPARROW models were related to recently updated records of salinity at 309 stream gauges in the UCRB using random forest regressions. Resulting salinity yield predictions indicate more diffuse salinity sources, with slightly higher yields in more arid portions of the UCRB, and less overall load coming from irrigated agricultural sources. Model simulations still indicate irrigation to be the major human source of salinity (661,000 Mg or 12%) and also suggest that 75,000 Mg (1.4%) of annual salinity in the UCRB is coming from areas with excessive exposed bare ground in high-elevation mountain areas. Model inputs allow for field-scale screening of locations that could be targeted for salinity control projects. Results confirm recent studies indicating limited surface erosional influence on salinity loading in UCRB surface waters, but impacts of monsoonal runoff events are still not fully understood, particularly in drylands. The study highlights the utility of new predictive soil maps and machine learning for environmental modeling.
2 Campolo, J.; Guerena, D.; Maharjan, S.; Lobell, D. B. 2021. Evaluation of soil-dependent crop yield outcomes in Nepal using ground and satellite-based approaches. Field Crops Research, 260:107987. [doi: https://doi.org/10.1016/j.fcr.2020.107987]
Crop yield ; Soil deficiencies ; Estimation ; Satellite imagery ; Remote sensing ; Soil maps ; Wheat ; Farmland ; Fertilizers ; Soil quality ; Soil properties ; Smallholders ; Weather data ; Models / Nepal / Terai Region
(Location: IWMI HQ Call no: e-copy only Record No: H050190)
(5.14 MB)
Smallholder farmers face many constraints to achieving food security. Optimal soil management is often limited by a lack of accessible and accurate soil characterization, and an associated lack of soil-specific management practice recommendations. Crop yields depend on both soil quality and soil-mediated fertilizer responses. Existing research on soil-fertilizer interactions is primarily based on farm trials and/or survey data, which are resource intensive and typically restricted to local scales. High-resolution (~10-meter) remote sensing data and digital soil maps provide a low cost, scalable alternative. Here, we deploy methods based on the Sentinel satellite constellation to estimate soil and fertilizer impacts on irrigated wheat grain yields in Nepal and to inform precision soil and nutrient management recommendations. We first combine field data with Sentinel-1 and Sentinel-2 imagery to delineate wheat cropping areas for 2016–2019 with 92 % accuracy. We then estimate wheat yields at 10-meter resolution using Sentinel-2 and weather covariates based on yield models parameterized from two different methods: 1) APSIM crop model simulations and 2) ground cropcuts from 147 fields. Using a large dataset of soil samples collected by the Nepal Agricultural Research Council, we examine the linear and non-linear effects of soil properties on wheat yields. Finally, the soil maps were combined with a survey of field-level crop management data and our yield estimates to test the interaction of soil quality with fertilizer effectiveness. Our ground-calibrated satellite model predicted yields with good accuracy (R2 = 0.55), while the uncalibrated simulation-based approach had weaker but significant prediction accuracy (R2 = 0.24). We find statistically significant gains in yield of 0.9–2.4 % are possible by increasing soil organic matter and zinc from highly deficient values to optimal values of 2.2 % organic matter (OM) and 0.67 ppm zinc (Zn). Using digital soil maps of Nepal produced by the International Maize and Wheat Improvement Center (CIMMYT), we map croplands deficient in zinc (Zn < 0.66 ppm) and organic matter (OM < 2.2 %) and find that 72 % of croplands in the Nepal Terai are experiencing less than optimal levels of these nutrients. We examine the effectiveness of nitrogen and zinc fertilizers, applied in amounts ranging from 0 to 150 kg ha-1 and 0 to 15 kg ha-1 respectively, in different soil quality regimes as determined by a soil quality index informed by standard relationships between crop yields and soil properties. Yields were significantly more responsive to zinc fertilizer inputs in soils with a higher than average soil quality but responded similarly to nitrogen inputs across different soils. Effects of soil and fertilizers on the simulation-based yield estimate were generally similar but less significant than effects on ground-calibrated yields. Overall, nitrogen and zinc increased ground-calibrated yields by 0.8 and 10.4 kg ha-1 per kg of nutrient input, respectively. This research demonstrates the potential of satellite data, crop simulation, and machine learning to examine the influence of soils on yields and guide precision fertilizer use in smallholder regions.
3 Ashwini, K.; Verma, R. K.; Sriharsha, S.; Chourasiya, S.; Singh, A. 2023. Delineation of groundwater potential zone for sustainable water resources management using remote sensing-GIS and analytic hierarchy approach in the state of Jharkhand, India. Groundwater for Sustainable Development, 21:100908. [doi: https://doi.org/10.1016/j.gsd.2023.100908]
Groundwater potential ; Groundwater recharge ; Remote sensing ; Geographical information systems ; Thematic mapper ; Soil maps ; Land use ; Land cover ; Drainage ; Sensitivity analysis / India / Jharkhand
(Location: IWMI HQ Call no: e-copy only Record No: H051860)
(8.75 MB)
Groundwater abstraction in many parts of India is highly unregulated which has caused a gradual decrease in groundwater levels over the years. Normally surface water storage structures or recharge zones are developed without knowing if all the groundwater is getting recharged. The issue becomes serious in the plateau region where most of the rainfall goes as surface runoff. Here an effort was made to identify the groundwater potential recharge zones of the entire Jharkhand state using Remote Sensing, GIS, and Analytic Hierarchy Approach techniques. Thematic layers which could contribute to the successful recharge of groundwater were prepared. The whole region was classified into four prospective groundwater potential zones: extremely low, low, moderate, and high. The results showed that approximately 3.28 and 77.34% of the total area falls under the High and average groundwater potential zones and the rest are under Low and Very low. Most of the high potential area is located in eight districts due to the alluvial plain region or a few mountains in that region namely Godda, Sahibganj, Pakur, Dumka, Purbi, Singhbhum, Saraikela-kharsawan and part of the Ranchi. The discharge data of 28 existing bore wells were used to endorse the groundwater potential zones. The further validation was made with an Area Under Curve (AUC) of 83%. According to the validation, the applied method produces a fairly dependable outcome for the state of Jharkhand.
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