Impact, Maintenance, and Sustainability of Irrigation Impact Evaluation Survey 2015-2018, Baseline, First, Second, Third Follow-up - Rwanda

Abstract
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The irrigation study context consists of 4 LWH hillside irrigation schemes and their surrounding terraced land across 5 districts of Rwanda. We use Spatial Regression Discontinuity analysis to capture the effects of irrigation. In the first 3 irrigation schemes (in Karongi and Nyanza), we use randomized control trials to document the impact of complementary interventions that have the potential to increase the returns and sustainability of irrigation. The complementary interventions included in the study are 1) providing demonstration minikits to a random subset of farmers, 2) providing irrigation subsidies to randomly selected farmers, and 3) empowering monitors in a randomly selected number of water user groups to keep operations and maintenance checklist and irrigation schedule. The baseline and 3 follow up surveys for the first 3 schemes (in Karongi and Nyanza) and the baseline and 1 follow up survey for the 4th site (Rwamagana) are documented here.

Geographic coverage
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The study covers 5 districts in Rwanda.
- Karongi & Nyanza schemes: Districts of Karongi, Nyanza, Rutsiro, and Huye;
- Rwamagana scheme: District of Rwamagana

Analysis unit
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Households, Plots of land

Kind of data
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Sample survey data [ssd]

Sampling procedure
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For three of the sites that are being used for the spatial regression discontinuity analysis (K12, N23, and R34), we divided the site into 3 areas - CA buffer (BCA), CA Catchment buffer (BCAC), and CA terraces (TCA). BCA is the area inside of the CA (CA, below main canal) within 50m of the boundary of the CA. BCAC is the area in the CA Catchment (CAC, above main canal) within 50m of the boundary of the CA. TCA is the terraced farmland that is in the CA, but more than 50m from the boundary of the CA. The third site will be used for the within-CA experimental designs only, and as a result we focused our sampling in one area - the CA terraces (TCA).

We constructed our household sampling by dropping a uniform grid of points across the full site at 2-meter resolution, and then sampling points within the grid. After each point was sampled, we excluded any points within 10m of that point (to keep from selecting multiple points too close together).
Enumerators were then given GPS devices with the locations of the points, and sent to each point, with a key informant (often the village leader). For each point, they were asked to identify if the point was on cultivable land (this was to discard forest, swamps, thick bushes, bodies of water, or other terrain which would make cultivation impossible). They were asked to record, for points in cultivable land, in SurveyCTO, the following:
1. The name of the point visited (which was displayed on the GPS);
2. The name of the cultivator, the location of their residence, and their phone number;
3. A description of the plot detailed enough that the cultivator would be able to identify the exact plot described

Additionally, they were asked to save their GPS track at the end of the day, as a way of tracking the number of hours they spent checking points and to verify that they visited each point. We used the data from this listing to construct a roster of all the unique names of cultivators, clustering points together when the names seemed identical. This roster (which contained the name of the individual, their village and phone number, the descriptions of the plots, and the villages in which the plots were located (identified using village shapefiles) and were organized by village) were then used to contact village leaders and verify that the listed individuals in fact existed. Multiple follow-ups were sometimes needed when village leaders suggested that one individual lived in a different village, or multiple village leaders said an individual lived in their village.

Finally, a sample plot was selected for each verified 2689 households. To select this sample plot, one point was randomly selected for each household. The probability of selecting a particular point was weighted - a weight of 1 was assigned to points in the BCA and BCAC, and a different weight was assigned for points in the TCA, to balance the number of sample plots in these areas.

Mode of data collection
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Face-to-face [f2f]

{'Abstract': '本灌溉研究背景涵盖了卢旺达5个地区的4个LWH梯田灌溉方案及其周边梯田。本研究采用空间回归间断性分析方法，以捕捉灌溉的影响。在前三个灌溉方案（位于卡荣吉和尼亚萨）中，我们运用随机对照试验来记录可能提高灌溉回报率和可持续性的辅助干预措施的影响。研究中的辅助干预措施包括：1）向随机选择的农民提供示范型迷你套件；2）向随机选取的农民提供灌溉补贴；3）授权随机选择的水用户群体中的监控员执行操作和维护清单以及灌溉时间表。本描述中记录了前三个方案（位于卡荣吉和尼亚萨）的基线和3次后续调查，以及第四个地点（鲁瓦马甘加）的基线和1次后续调查。', 'Geographic coverage': '本研究覆盖卢旺达的5个地区。
- 卡荣吉与尼亚萨灌溉方案：卡荣吉、尼亚萨、鲁齐罗和胡耶地区；
- 鲁瓦马甘加灌溉方案：鲁瓦马甘加地区', 'Analysis unit': '家庭、土地地块', 'Kind of data': '样本调查数据 [ssd]', 'Sampling procedure': '对于用于空间回归间断性分析的三个地点（K12、N23和R34），我们将地点划分为三个区域——CA缓冲区（BCA）、CA集水区缓冲区（BCAC）和CA梯田（TCA）。BCA是CA（CA，位于主渠下方）内距离CA边界50米范围内的区域。BCAC是CA集水区（CAC，位于主渠上方）内距离CA边界50米范围内的区域。TCA是位于CA内的梯田农田，但距离CA边界超过50米。第三个地点仅用于CA内部实验设计，因此我们专注于一个区域——CA梯田（TCA）。', 'Mode of data collection': '面对面 [f2f]'}