In Kimilili, Bungoma County, rains failed in late October and early December. Maize, beans and cassava stopped growing at the flowering stage, so fields went brown and farmers expect a poor harvest. Women farmers often feel the effects most because they do much of the farm work but have limited access to land, credit, technology and advisory services.
Researchers say pests and diseases cause up to 40 per cent more pre-harvest crop loss than climate-related factors. Women make up about 43 per cent of the agricultural labour force in low- and middle-income countries. If women had the same access to productive resources as men, yields could rise by up to 30 per cent, total agricultural output by up to 4 per cent, and global hunger could fall by as much as 17 per cent.
Marital and legal barriers add problems. One farmer could not use family land as loan collateral because her husband opposed it, and she cannot sell produce without his consent. Households use casual labour, small shops and sales of milk, eggs and vegetables to cope. The GBCL project measures losses using field trials, scientific literature, automated text mining, Earth observation and machine learning, and it is funded by UK International Development and the Gates Foundation. Some programmes already target women with input subsidies and loans.
Difficult words
- flowering stage — time when plants make flowers before fruitthe flowering stage
- access — ability to reach or use something neededaccess to land, access to productive resources
- advisory services — professional help and advice for farmers
- pre-harvest — loss of crops before they are harvestedpre-harvest crop loss
- collateral — property used to guarantee repayment of a loanloan collateral
- Earth observation — using satellites to collect data about the Earth
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- How could equal access to productive resources change life for small farmers in your area?
- What other ways might households cope with a poor harvest besides casual labour and small shops?
- In what simple ways could Earth observation and machine learning help farmers detect problems earlier?
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