Improved cropland management

Climate change exacerbates land degradation and food insecurity, while land is both a source and sink of CO2.

improved cropland

Improved cropland management

Climate change exacerbates land degradation and food insecurity, while land is both a source and sink of CO2. In the Global South smallholder farmers manage approximately 25 percent of farmland and account for close to one-third of the world’s food supply. However, smallholders, who are more dependent on rain-fed agriculture, are particularly vulnerable to extreme weather events caused by changes in climate from flash floods to water scarcity occurring as a result of droughts.

Collective intelligence methods are increasingly helping networks of smallholders to adopt climate resilient practices and behaviors. These initiatives help to fill several data gaps on local weather or growing conditions. Many farmers are already experimenting with adaptation at a small scale, often in isolation from each other. Collective Intelligence approaches, however, enable many farmers to pool knowledge and insights, accelerating their ability to adapt to changes in rainfall, soil quality and other factors and addressing doing gaps of ineffective farming practices. Collaborative action by farmers in turn is also creating new scientific data and knowledge that has wider application and use, helping to narrow the distance gap about effective adaptation interventions. Together these examples offer a glimpse of how scaled-up, smartly targeted and incentivized actions could enable a larger sector-wide shift in cropland management, while improving the economic prospects of individual farmers.

Main collective intelligence methods being used

  • Citizen science and open repositories for climate resilient crops

  • Peer exchange for climate smart agriculture

  • Combining sensor data and citizen-generated data for intelligent networked actions 

Main climate action gaps being addressed

  • Data gaps on local weather or growing conditions
  • Distance gap around experiments that happen at small scale and in isolation
  • Doing gap around persistence of ineffective farming practices ill-suited to changes in climate
  • Diversity gap from failing to tap into and share on-the-ground farmer expertise

Citizen science and open repositories for climate resilient crops

One way to build the resilience of smallholders is through the diversification of crops and seed varieties; however, there is a significant data gap about how different seed varieties will perform across changing climatic conditions and in different ecological zones. Across South America and Africa, collective intelligence initiatives are addressing this gap through large scale crowdsourcing of data on seed varieties and how well they grow in different local conditions. Popular approaches include citizen science – where volunteers (in this case mostly smallholder farmers) work with scientific researchers to generate new scientific data and knowledge, and the creation of open repositories – digital databases where content (data, code, text or DIY designs) can be stored and freely downloaded or used with few restrictions.

An example of this is the Bioleft platform in South America, which acts as an open-source repository of local seed varieties and facilitates collaborative seed-breeding between farmers to help them identify more climate-resilient options. The platform enables georeferencing of seeds and records their transfer under an official Bioleft license. This system helps to retain the benefits of seeds for local communities through open experimentation, reducing their exploitation by international corporations who take out patents for certain seeds and demand payment for their use. The platform also supports farmers to build connections and exchange ideas with peers. Seeds for Needs, is another project that helps farmers test which seeds are most appropriate for their local area through a combination of citizen science, large scale field experiments called n-trials and the digital platform ClimMob.

Peer exchange for climate smart agriculture

Although the concept of climate-smart agriculture has grown in popularity in recent years, its adoption by smallholder farmers faces a number of challenges – including knowledge, finance, technology and infrastructure.

Collective intelligence methods are increasingly helping to bridge distance and doing gaps – by enabling farmer-to-farmer knowledge exchange. This is an effective way to help farmers fast track improvements to their agricultural practices by sharing and learning from each other. For this reason, mechanisms that support peer exchange are increasingly incorporated into “agri-tech” tools for farmers.

An example of farmer-to-farmer peer exchange is Geofarmer, an open source app that supports farmers in adopting climate-smart applications in Africa and Latin America. Farmers use it to exchange advice about crop, animal and farm management practices. It can also be used by funders and researchers to access location-specific data about the effectiveness of agricultural technologies and practices implemented by farmers in a given region. The Agrolly app provides real-time weather monitoring and crop information to help farmers decide which crops to grow and when. It is another example of a digital tool that enables peer exchange through a social forum where farmers can share advice and solutions using either text or images. So far, it’s been tested with smallholders in India, Mongolia and Brazil. In 2022, the team announced that it would open source its annual weather forecasting model under the name OpenTempus to allow others to create new applications.

A pilot project by the NGO Swiss Contact, which worked with smallholders in the Bolivian Andes, also illustrates the value of peer exchange. The project installed low-cost sensor-based weather stations on farmers’ land and crowdsourced detailed information about diseases and pests from farmers. It found that crowdsourcing improved weather forecast accuracy by 25 percent and also increased farmers’ trust and engagement in early warning systems, meaning they were more likely to take action to prevent pest outbreaks.

Combining sensor data and citizen-generated data for intelligent networked actions

Collective intelligence initiatives help to coordinate the activities of smallholder farmers by triangulating between different data sources – including sensor data and crowdsourced observations. This helps to close doing gaps by incentivizing climate-resilient individual and group level behavior through financial rewards.

An important element of these data platforms is the verification of behavior by remote sensing through satellite imagery and/or drones. For example, the BaKhabar Kissan (BKK) app in Pakistan uses satellite imagery and remote soil health sensors to monitor crops and provide personalized recommendations to farmers. The app also helps to close the gaps in the agricultural supply and value chain by providing an online marketplace where farmers can sell directly to consumers.

Open Harvest (nDI Chuma) is a similar system developed by Heifer International. It's an open-source digital platform in the early stages of development that supports agricultural systems in Malawi. Smallholder farmers in Malawi are not always able to maximize their yield or access fair prices for their groundnut crops. Open Harvest visualizes data on farmers’ experience and production history and gives customized recommendations on crop management based on climate modeling. The actions taken by farmers are verified by drones and when confirmed, they gain “reputation” credits. These credits allow them to access better deals on loans, helping to reinforce climate-resilient actions for long-term behavior change. Both of these examples use financial incentives and tailored recommendations to influence the actions of individual farmers and amplify adaptive behaviors when aggregated at the regional level.

These refer to experiments taking place outside of lab settings with large numbers of participants. Instead of a few researchers carrying out complicated field trials, large numbers of farmers or gardeners carry out small, simple trials on their land. Taken together, the many small trials can offer valuable information about the local suitability of agricultural technologies.