GIZ Data Lab, UNDP Niger Accelerator Lab, GIZ PromAP Niger, University of Manchester
Positive Deviance, Agriculture, Climate Change
Humanitarian Data Exchange, Copernicus Global Land Cover Layers
SDG #2, SDG #12, SDG #13, SDG #15
Niger is facing a severe food insecurity crisis, with nearly 20% of its population affected in 2022. This crisis is exacerbated by the impact of climate change and a reduction of rainfall affecting crop cycles. Niger’s principal food crops – pearl millet and sorghum – are threatened by these developments. There is growing pressure to act to mitigate the effects of climate change and promote sustainable farming practices that provide relief from current poverty levels.
The project applied a Data Powered Positive Deviance (DPPD) method, which helped to identify and understand good practices in agriculture. To this end, the team leveraged new data sources, specifically Earth observation data that was further analyzed using a combination of remote sensing and biophysical data (such as soil types, water resources and rock structures). The analysis identified that certain farmers growing pearl millet and sorghum in Niger have consistently achieved higher yields than their counterparts under similar conditions. Closer geospatial analysis and field visits to these areas revealed valuable insights into enabling factors that contributed to improved crop production. These solutions could support the struggling agricultural sector and thus vulnerable communities in Niger.
By analyzing already existing successful local practices, policymakers and practitioners gained insights into the factors that drive success. Interventions and policies can then be tailored accordingly. The method revealed that structural drivers of performance such as precipitation or soil characteristics vary across different contexts and target groups, but identified interventions which could improve yields independently from these factors. The use case contributes to SDG 2 (Zero hunger) by improving the yields of crops which play an important role in mitigating food insecurity in Niger. The project further contributes to SDG 12 (Responsible consumption and production) by promoting sustainable management and efficient use of natural resources. Our study showed how “zaï holes” (a West African farming technique where small pits are dug to catch water and nutrient runoff during heavy rain) or stone strands (incorporating lines of stone into fields to slow stormwater runoff) help mitigate the environmental impacts of agriculture while ensuring food security.
people in Niger are expected to be facing hunger in 2023.
Niger is currently facing its worst food security crisis of the decade. Climate change and the reduction of rainfall are affecting crop cycles, putting agriculture under tremendous pressure. Pearl millet and sorghum, the main food crops in Niger, are rainfed subsistence crops that are increasingly negatively affected by the changing climate. In response to this crisis, Nigerien authorities are seeking data-driven insights to inform policies and interventions. Through the Data Powered Positive Deviance (DPPD) initiative, the GIZ Data Lab, the UNDP Niger Accelerator Lab, GIZ PromAP Niger and the University of Manchester conducted a pilot to find farmers who are particularly successful in cultivating rainfed cereal crops and understand why those farms succeeded.
The project team targeted the southern region of Niger, where sorghum and pearl millet are most cultivated. A two-step approach was followed:
Accounting for diversity in data
Analyzing the southern region of Niger, the diversity of agricultural zones with specific structural characteristics was accommodated by grouping together different zones with similar agricultural yields of rainfed crops. This approach helped reduce the likelihood of higher performance being driven by structural factors rather than effective farming practices. To facilitate the grouping, Hauswirthet al.'s (2020) classification of agricultural zones has been applied.
Soil-Adjusted Vegetation Index (SAVI) as a Performance Measure
Existing open data sources have been leveraged as proxy measures of performance, being particularly valuable in contexts where data is scarce. Crop biomass has been used as a proxy for the agricultural yield of sorghum and pearlmillet, in this case taking advantage of the Soil-Adjusted Vegetation Index (SAVI). This proved particularly suitable for arid regions with little vegetation, such as Niger. To compute the SAVI Sentinel-2 data was accessed.
Good to know: Sentinel-2 is an Earth observation mission that systematically acquires high spatial resolution visual imagery over land and coastal waters to monitor variability in surface conditions. All data from Sentinel-2 are freely available through the Copernicus Open Access Hub.
The maximum SAVI value was used as a measure of performance, indicating when the crops' biomass and thus yield was highest. To increase accuracy, SAVI values were calculated only for areas classified as "managed or cultivated vegetation" by the Copernicus Global Land Cover Layers. Moreover, SAVI values were calculated only for the rainy season (June to September) when sorghum and pearl millet can be grown as rainfed crops. Limiting the period to the rainy season reduced the likelihood of accidentally sampling other crops grown on the same plot, such as cowpea. Finally, rainy seasons from multiple years (2018-2020) were studied to reduce the impact of individual events that may have had short-term effects on yield but are not representative of long-term performance.
Comparing against expectations
Having identified the communities to study and calculated SAVI values for them, three more steps remained:
1. Predicting the average maximum SAVI for each community:
The project team predicted the maximum SAVI value for the 2018-2020 rainy seasons of each agricultural zone using variables such as land cover, soil properties, temperature, rainfall, and soil moisture. Three statistical models were combined for this estimation: (i) a regression model to individually estimate the maximum SAVI for each agricultural zone, (ii) a boosting tree algorithm to capture nonlinear effects on maximum SAVI, and (iii) a neural network.
The results were integrated into a joint prediction to reduce model-specific biases and reduce the susceptibility of the estimates to outliers.
2. Calculating each community's deviation from this estimate:
The project team then calculated the difference between the maximum SAVI value in each municipality and the expected value from step 1. To standardise these measures the mean maximum SAVI value and the standard deviation of each agricultural zone was determined. This figure, “the residual”, therefore corresponds to higher performance which cannot be explained through environmental factors.
3. Identifying the communities which exceeded the estimate by the largest amount within their respective agricultural zones:
The residuals of all the communities were compared. Following this approach, 180 communities were identified that performed particularly well. These were then validated and further investigated, identifying those properties that are likely to have contributed to the above-average performance.
Once identified, satellite imagery was used to manually examine each community with local experts to exclude false positives. Incorrect identification may occur as a result of missing or inaccurate data. As an example, some communities have been located where pastures, shrubs, and trees (which score high on SAVI) were misclassified as agricultural land.
Finally, a two-stage semi-qualitative field study was conducted:
The agricultural expert interviewed 179 local stakeholders growing pearl millet and sorghum in four regions (Tahoua, Dosso, Maradi, and Zinder). 35 producers were identified as positive deviants due to their combination of above-average yields and their use of various behaviours and techniques that likely explain them. In particular, the following approaches correlated to particularly strong crop returns:
By identifying groups that have outperformed others, policymakers can replicate successful interventions and adjust ineffective ones.
More specifically, several opportunities exist for policymakers to leverage new data relating to the rainfed economy in Niger and thus address the population's food insecurity. Among these are the following:
Evidence from this study on positive deviations in Niger's rainfed agriculture provides policymakers with a valuable evidence-based foundation for combating a worsening hunger crisis. Through dissemination of best practices to other farmers in the same or similar regions, scalable and sustainable solutions can be created that have the potential to address malnutrition and, by boosting the agricultural economy, severe poverty. As most of the data used in this study is open and readily available, policymakers in neighbouring countries facing similar challenges such as Mali, Chad and Burkina Faso can similarly benefit from the approach. Leveraging the power of data and evidence-based solutions, a brighter future may be shaped for agriculture across the wider region, grounded in resilience, innovation and success.