Geospatial data for measuring vegetation impact on agricultural productivity

Geospatial data for Measuring vegetation impact on agricultural productivity

In August 2021, 3ie and New Light Technologies co-led a series of capacity-building workshops with 10 researchers from the African Population and Health Research Center (APHRC) on the potential to use remotely-sensed geospatial data for impact evaluations. This blog is the second in a series of four in which workshop participants reflect on the uses of remotely-sensed and geospatial data.

Evaluations have become a core aspect of development programs, as they contribute to increasing productivity and effectiveness in programs through learning and tracking of impact. Despite being effective, even the gold standard in evaluation approaches such as randomized control trials (RCTs) are limited in application due to cost considerations. In other contexts, random assignments can be unethical. Academics in the field of Geographic Information Systems (GIS) have proposed Geospatial Impact Evaluations (GIE) as an affordable emerging tool for evaluators. GIEs use spatial data to evaluate the impacts and cost-effectiveness of specific development interventions and large investment portfolios.

Geographic Information Systems and measurement devices, including sensors onboard satellites and data collected by global positioning system (GPS), can be used to acquire data about phenomena on earth which are then used for analysis, modeling, simulations, and visualization. GIE methods leverage readily available data, like satellite observations or household surveys, to establish a reliable counterfactual for measuring impacts — at a fraction of the time and cost of “traditional” RCTs. And unlike RCTs, GIEs use precise geographic data to establish this counterfactual retroactively, eliminating the need to assign program participants into randomized treatment and control groups. These approaches can be particularly effective in the agricultural sector.

The use of GIS technologies and remotely sensed data for agricultural applications are not new. Different vegetation indices like normalized difference vegetation index (NDVI), fraction of photosynthetically active radiation (FPAR), and transformed vegetation index (TVI) are widely used to monitor crop health and productivity, which is directly related to yield. Remote sensing and geospatial analyses and visualizations of agricultural environments have indeed proved to be very beneficial to the farming community. These tools play a great role in agriculture throughout the world by helping farmers to achieve increased productivity and reduced costs by enabling better management of land resources. The risk of marginalization and vulnerability of small and marginal farmers, who constitute about 85 percent of farmers globally, can also be reduced via the proper use of these tools.

Geospatial data and specifically remotely sensed imagery is applied widely and are effective and powerful tools in detecting land use and land cover change over time. They can further be used to identify promising land for any particular crop. Remote sensing and GIS are therefore effective and invaluable in understanding crop health, the extent of insect infestation, potential yield, and soil conditions. GIS and remote sensing can therefore be specifically applied to agricultural applications such as:

  • Assessment of crop damage and crop progress
  • Provision of crop insurance, so actual damage can be assessed and compensations can be given on a fair basis
  • Estimation of biomass and crop yield
  • Analysis of appropriate soil amendments
  • Erosion identification and remediation
  • Agricultural water management
  • Soil moisture and area estimation
  • Vegetation vigor and drought stress monitoring
  • Assessment of crop phenological development
  • Crop acreage estimation
  • Crop yield forecasting
  • Cropland mapping
  • Monitoring of droughts
  • Land cover and land degradation mapping

Additionally, farmers can also use other geospatial technologies such as ground measurements, ground surveys, and satellite imagery (including publicly available imagery such as Landsat) to improve their yields by assessing variations in soil quality for planting crops.

Effects of urbanization on land productivity

Thousands of hectares of agricultural land are lost due to urbanization all over the world every year due to rapid population growth and human production demands. Remote sensing analytical techniques can measure these changes. Vast tracts of forests are also lost globally every year, especially in developing countries of the tropics due to various human activities such as rapid urbanization, logging, farming, bush fires, and surface mining. The extent of land use and land cover change over time can be examined through image differencing techniques applied on Landsat Satellite images. Landsat images, which are publicly available and provide a snapshot of Earth since the mid- 1970s, can further be used to quantify agricultural land consumption by urbanization and monitor and predict the future pattern and land-use trends. As one example, this article examines urbanization in Egypt using remote sensing. Expanding on this theme, our blog tomorrow will investigate the case of Nairobi's expansion.

Food insecurity

Africa’s high level of food insecurity is exacerbated by rapid urbanization. In Kenya, the urban poor exhibit high levels of food and nutrition insecurity, with about 80 percent of slum households being food insecure, about 50 percent of children below 5 years being stunted, and about one-third of their mothers being underweight. Despite this, the right to food is recognized in the Kenyan constitution. Therefore the Right to Food (RTF) Initiative at APHRC, seeks to improve food and nutrition security for the urban poor in Kenya by exploring solutions to their specific food insecurity problems. The project can leverage remote sensing and GIS technologies to help make informed decisions on food security issues by generating regular updates on crop production statistics, early yield prediction, and providing inputs to achieve sustainable agriculture. Additionally, the project could use geospatial technology in demarcating and identifying crops and areas where urban agriculture is practiced.

Who needs GIE and remote sensing for agriculture?

Government authorities, local agencies, and relevant stakeholders can use GIS and remote sensing data as part of their GIEs to make important decisions about the policies they will adopt or measures to tackle national issues regarding agriculture.

Additionally, individual farmers can visualize all their farmlands with their associated information and current situation on a single platform, therefore receiving useful information from remotely-sensed images. This system can help them learn their crops' health status and how to deal with any problems. For instance, tasks like yield estimation and crop damage assessment done by traditional means normally takes more than one month and tons of manpower to complete. On the contrary, by using geospatial technologies the same task can be completed within a much shorter timeframe, using fewer resources while achieving high accuracy.

As APHRC expands its impact evaluation mandate to be a strong regional player in measurement and impact evaluation in sub-Saharan Africa in the upcoming years, it continues to promote the use of classical and emerging impact evaluation methodologies. As an institution, we aim to apply robust impact evaluation tools to generate evidence for decision-making to improve lives and wellbeing in Africa through research.

Call to Action: Sub-national government authorities, in collaboration with development agencies and research institutions, should invest in infrastructure for obtaining GIS and remote sensing data. This data can be used to estimate agricultural productivity in specific regions in order to make important decisions about the best policies  to tackle national issues regarding agriculture.

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Authors

Dennis Akeyo Dennis Akeyo Epidemiologist and a Researcher, APHRC
Henry Owoko Henry OwokoDesignation: Monitoring Evaluation and Research Officer, APHRC
Aayush Malik Aayush MalikData Science Associate
Ran Goldblatt Ran GoldblattGeographic Information System (GIS) and Remote Sensing expert

About

Evidence Matters is 3ie’s blog. It primarily features contributions from staff and board members. Guest blogs are by invitation.

3ie publishes blogs in the form received from the authors. Any errors or omissions are the sole responsibility of the authors. Views expressed are their own and do not represent the opinions of 3ie, its board of commissioners or supporters.

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Authors

Dennis Akeyo Dennis Akeyo Epidemiologist and a Researcher, APHRC
Henry Owoko Henry OwokoDesignation: Monitoring Evaluation and Research Officer, APHRC
Aayush Malik Aayush MalikData Science Associate
Ran Goldblatt Ran GoldblattGeographic Information System (GIS) and Remote Sensing expert

About

Evidence Matters is 3ie’s blog. It primarily features contributions from staff and board members. Guest blogs are by invitation.

3ie publishes blogs in the form received from the authors. Any errors or omissions are the sole responsibility of the authors. Views expressed are their own and do not represent the opinions of 3ie, its board of commissioners or supporters.

Archives