As communities in the developing world face more frequent and severe environmental risks, many efforts to mitigate damages and build resilience fall prey to collective action challenges. We propose studying the effectiveness of one innovative approach that promotes productive agricultural activities as well as preventing soil erosion and combating severe flooding risks. Over the past decade, Mercy Corps’ Managing Risk through Economic Development (M-RED) program has encouraged farmers to plant water-intensive crops along riverbanks in Western Nepal’s plains and middle hills. Existing evaluation work has shown promising gains among participant communities, but the program’s broader environmental and watershed impacts have not yet been assessed. Our multidisciplinary team will use remote sensing and variation in recent weather shocks to assess M-RED’s impacts on adoption of key crops, as well as on soil erosion and flooding. Our research will assess the magnitude of cross-village spillovers in both geophysical and social diffusion terms, as downstream communities benefit from reduced silt and turbidity from upstream erosion, and nearby untreated communities see take-up of the promoted crops.
Rainwater harvesting (RWH) techniques, which capture rainfall and reduce runoff, present a compelling option in settings where irrigation is technically unfeasible and chemical input use is limited. Aker and Jack’s recent study examines the adoption of RWH using a randomized control trial in Niger, in which the authors test the importance of the time profile of returns, as well as credit and liquidity
constraints, to the adoption of RWH. The authors find that providing farmers with training increases the share of adopters by over 90 percentage points, whereas adding conditional or unconditional cash transfers has no additional effect. Adoption increases agricultural output, reduces land turnover, and leads to adoption spillovers up to three years after treatment. To extend the completed work, we plan to add remotely sensed data that will extend the time period over which adoption is observed, as well as expand the sample to cover new adoption that may occur as the Min. of Environment scales up the intervention nationally. We will build on recent efforts to develop three remote sensing measures, helping to overcome recent challenges. First, detecting adoption outcomes (i.e., demi-lune – half moon) based on visual detection with free or inexpensive imagery is a challenge. Second, the farmer selects where to construct demi-lunes, typically focusing on the most degraded land. How to estimate soil quality is a challenge. Third, the time of year when some visual indications of adoption (such as rainwater collecting in the demi-lunes) are most obvious is also most susceptible to cloud cover. We plan to utilize Sentinel (preferably SAR – Synthetic Aperture Radar) + PlanetScope along with SkySat data to detect demi-lunes, quantify soil properties, and agriculture production.
Emerick and coauthors from Tufts and IRRI designed and implemented a randomized control trial to study the distribution of benefits from subsidizing a water-saving technology known as Alternate Wetting and Drying (AWD). The technology is a perforated plastic pipe planted into the rice field to plan irrigation based on crop-water needs. Using a randomized controlled trial across 360 villages in Bangladesh, authors show that subsidies for adoption of the technology reduce electricity used for pumping by 38 percent, but only when targeted to water sellers. However, the evaluation only looked at a proxy of adoption (i.e., aggregate pump electricity use). Existing measures of AWD adoption do not exist. We plan to utilize Earth Observation imagery and products to examine whether wetting and drying adoption could be feasible with imagery from Sentinel, PlanetScope, or tasked SkySat sensors.
Increasing desertification across Africa driven by climate change, land use, and related factors presents a growing threat to communities who are dependent upon the land for their livelihoods. Initiatives such as the Great Green Wall in the Sahara and Sahel have been created to combat desertification, reduce poverty, and improve resilience to climate change in the region. From 2016 until 2020, the FAO’s Action Against Desertification (AAD) programme supported the ambitions of the GGW in six countries of the Sahel, including in Northern Nigeria. In 2021, following completion of AAD activities, the FAO conducted an ex-post quasi-experimental evaluation of the project’s activities and socio-economic outcomes using a combination of ground data collection, geospatial data, and machine learning. The use of remote sensing and earth observation to evaluate project outcomes is particularly valuable in areas like Northern Nigeria, where conflict and instability can limit ground-based data collection such as household surveys. In this rapid use case, we aim to expand the use of geospatial data, including high resolution imagery, to explore whether we can A) validate and enhance the methods used in the initial analysis, B) expand estimates of socio-economic impacts to areas beyond those in the initial analysis, and C) identify ways that remote sensing can be used to effectively measure the socio-economic impacts of AAD’s successor program - the GCF-funded SURAGGWA, beginning in 2023 - to inform the program’s activities.