Taking digital soil mapping to the field: Lessons learned from the Water Smart Agriculture soil mapping project in Central America

Owens, Phillip R.; Dorantes, Minerva J.; Fuentes, Bryan A.; Libohova, Zamir; Schmidt, Axel

doi:10.1016/j.geodrs.2020.e00285

Cited by 14 publications

(12 citation statements)

References 40 publications

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“…It was found to be potentially a useful approach for irrigation management for improving crop yield in an onion field in the humid environment of England (González Perea et al, 2018). In another study with irrigated corn crop, Bausch and Diker (2001) reported that VRI technology reduced N fertilizer application by 39 kg N ha -1 with no reduction in corn grain yield  Water Smart Agriculture project in Central America helped small farmers develop long-term digital soil mapping system which enables wise use of natural resources in the present context of growing global challenges for food security under changing climate (Owens et al, 2020)  DSSAT for Conservation, Optimization and Precision Agriculture: A real-time control and optimization in irrigation management system has been presented as a potential option for water savings by Koech and Langat (2018). Decision Support System in Agricultural Technologies (DSSAT) can provide fundamental support to the growers to decide and optimize their practices for precise nutrient, water, energy and pest management.…”

Section: Resource Use Efficiency For Sustainable and Regenerative Agriculturementioning

confidence: 99%

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

et al. 2021

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Intensive agriculture can cause a loss in biodiversity and concerns for global food security. Regenerative agriculture and integrative permaculture are keys to address food security. Digital agricultural tools can aid in attaining agricultural and environmental sustainability. Precision agriculture should be implemented as an agricultural decision support system. Artificial intelligence and machine learning can guide integrated agricultural input management.

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Section: Resource Use Efficiency For Sustainable and Regenerative Agriculturementioning

confidence: 99%

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

et al. 2021

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“…The aggregation of PMs was performed to reduce the number of PM classes (Figure 4). Presumably, the aggregation of PM classes would reduce the number of soil samples required to capture a more complete feature space (2).…”

Section: Environmental Covariatesmentioning

confidence: 99%

“…For the present study, the application of the environmental similarity model for the continuous mapping of soil properties followed a data mining approach, similar to that proposed by Owens et al (2). The data mining approach included the following steps: (i) cluster analysis of terrain attributes, (ii) construction of soil generic classes, (iii) construction of a ruleset and fuzzy inference of the SSV, and (iv) modeling of a soil property.…”

Section: Digital Soil Mapping Approachmentioning

confidence: 99%

“…Information about soils is integral to sustainable soil management. Overall, there is a high level of spatially explicit soil information in the U.S. compared to emerging countries (2). Nonetheless, not all of the U.S. has detailed, spatially resolved soil information, such is the case with Native American Tribal Lands (3,4).…”

Section: Introductionmentioning

confidence: 99%

“…In the current study, a DSM approach based on SoLIM is implemented for the spatially continuous modeling of Quapaw Tribal Lands soil properties. This approach allows for combining fuzzy logic-based SoLIM with data-mining techniques, a combination that has proven useful for DSM when expert knowledge is not available and soil point data are limited (2,11,15,16). The objective of this study is therefore to produce first ever high-resolution soil property maps of the Quapaw Tribal Lands following a data-driven, fuzzy logic framework that accommodates limited data scenarios and compare this approach with gSSURGO.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Soil Properties to Advance the State of Spatial Soil Information for Greater Food Security on US Tribal Lands

et al. 2021

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Knowledge, data, and understanding of soils is key for advancing agriculture and society. There is currently a critical need for sustainable soil management tools for enhanced food security on Native American Tribal Lands. Tribal Reservations have basic soil information and limited access to conservation programs provided to other U.S producers. The objective of this study was to create first ever high-resolution digital soil property maps of Quapaw Tribal Lands with limited data for sustainable soil resource management. We used a digital soil mapping (DSM) approach based on fuzzy logic to model the spatial distribution of 24 soil properties at 0–15 and 15–30 cm depths. A digital elevation model with 3 m resolution was used to derive terrain variables and a total of 28 samples were collected at 0–30 cm over the 22,880-ha reservation. Additionally, soil property maps were derived from Gridded Soil Survey Geographic Database (gSSURGO) for comparison. When comparing properties modeled by DSM to those derived from gSSURGO, DSM resulted in lower root mean squared error (RMSE) for percent clay and sand at 0–15 cm, and cation exchange capacity, percent clay, and pH at 15–30 cm. Conversely, gSSURGO-derived maps resulted in lower RMSE for cation exchange capacity, pH, and percent silt at the 0–15 cm depth, and percent sand and silt at the 15–30 cm depth. Although, some of the soil properties derived from gSSURGO had lower RMSE, spatial soil property patterns modeled by DSM were in better agreement with the topographic complexity and expected soil-landscape relationships. The proposed DSM approach developed property maps at high-resolution, which sets the baseline for production of new spatial soil information for Quapaw Tribal soils. It is expected that these maps and future versions will be useful for soil, crop, and land-use decisions at the farm and Tribal-level for increased agricultural productivity and economic development. Overall, this study provides a framework for developing DSM on Tribal Lands for improving the accuracy and detail of soil property maps (relative to off the shelf products such as SSURGO) that better represents soil-forming environments and the spatial variability of soil properties on Tribal Lands.

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Predicting spatiotemporal patterns of productivity and grazing from multispectral data using neural network analysis based on system complexity

Ashworth,

Avila,

Smith

et al. 2024

Agrosystems Geosci & Env

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Remote sensing tools, along with Global Navigation Satellite System cattle collars and digital soil maps, may help elucidate spatiotemporal relationships among soils, terrain, forages, and animals. However, standard computational procedures preclude systems‐level evaluations across this continuum due to data inoperability and processing limitations. Deep learning, a subset of neural network, may elucidate efficiency of livestock production and linkages within the livestock‐grazing environment. Consequently, we applied deep learning to environmental remote sensing data to (1) develop predictive models for yield and forage nutrition based on vegetation indices and (2) at a pixel‐level (per 55 m2), identify how grazing is linked to soil properties, forage growth and nutrition, and terrain attributes in silvopasture and pasture‐only systems. Remotely sensed data rapidly and non‐destructively estimated herbage mass and nutritive value for enhanced net and primary productivity management in livestock and grazing systems. Cattle grazed big bluestem (Andropogon gerardii ‘Vitman’) with 182% greater frequency than orchardgrass (Dactylis glomerata L.) in the pasture‐only system. Real‐time estimates of vegetative bands may assist in predicting grazing pressure for more efficient pasture resource management. Cattle grazing followed distinct soil‐landscape patterns, namely reduced cattle grazing preference occurred in areas of water accumulation, which highlights linkages among terrain, soil‐water movement, soil properties, forage nutrition, and animal grazing response spatially and temporally. Results from this study could be scaled up to improve grazing management among the largest land‐use category in the United States, that is, grasslands, which would allow for sustainable intensification of forage‐based livestock production to meet growing demands for environmentally responsible protein.

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Taking digital soil mapping to the field: Lessons learned from the Water Smart Agriculture soil mapping project in Central America

Cited by 14 publications

References 40 publications

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

Regenerative agriculture and integrative permaculture for sustainable and technology driven global food production and security

Mapping Soil Properties to Advance the State of Spatial Soil Information for Greater Food Security on US Tribal Lands

Predicting spatiotemporal patterns of productivity and grazing from multispectral data using neural network analysis based on system complexity

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