Evaluation of soil-dependent crop yield outcomes in Nepal using ground and satellite-based approaches
Abstract/Contents
- Abstract
- Smallholder farmers face many constraints to achieving food security. Optimal soil management is often limited by a lack of accessible and accurate soil characterizations, 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 satellite-based methods to estimate soil and fertilizer impacts on wheat 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 crop cuts from 147 fields. Our ground-data calibrated satellite model predicted yields with good accuracy (R2 = 0.56), while the uncalibrated simulation-based approach had weaker but significant prediction accuracy (R2 = 0.25). Using a large dataset 16 of soil samples collected by the Nepal Agricultural Research Council, we examine the linear and non-linear effects of soil properties on wheat yields. 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% OM and 0.67 ppm Zn. Using digital soil maps of Nepal produced by the International Maize and Wheat Improvement Center (CIMMYT), we map organic matter and zinc deficiency in croplands, and find that 72% of cropland in the Nepal Terai is experiencing some level of deficiency in organic matter or zinc. 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. Yields were significantly more responsive to zinc inputs in higher quality soils but responded similarly to nitrogen inputs across soil quality. Overall, nitrogen and zinc increased yields by 0.8 and 10.4 kg/ha per kg/ha 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.
Description
| Type of resource | text |
|---|---|
| Date created | May 2021 |
Creators/Contributors
| Author | Campolo, Jake |
|---|---|
| Author | Güereña, David |
| Author | Maharjan, Shashish |
| Primary advisor | Lobell, David B. |
| Advisor | Fendorf, Scott |
| Degree granting institution | Stanford University, Earth System Science |
Subjects
| Subject | School of Earth Energy & Environmental Sciences |
|---|---|
| Subject | remote sensing |
| Subject | agriculture |
| Subject | soil |
| Subject | machine learning |
| Genre | Thesis |
Bibliographic information
| Related Publication | Campolo, J., Güereña, 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. |
|---|---|
| Related item | |
| Location | https://purl.stanford.edu/gh395bh9509 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
- Campolo, Jake (2021). Evaluation of soil-dependent crop yield outcomes in Nepal using ground and satellite-based approaches. Stanford Digital Repository. Available at: https://purl.stanford.edu/gh395bh9509
Collection
Master's Theses, Doerr School of Sustainability
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- Contact
- campolo.jake@gmail.com
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