Agriculture

Farmers’ climate perceptions guide adaptation, but previsouly they have been tested against weather stations rather than soil moisture, which matters more for crops. We used surveys of 1,171 Zambian farm households after the 2016 harvest recalled onset dates for recent and earlier seasons. In our analysis each farmer’s own onset rule was compared with local rainfall and modeled soil moisture. Some 88% reported rains arriving 21.9 days later than a decade ago, yet the mean perceived onset for the latest season nearly matched the physically derived mean. Recall bias sets in as early as a year after harvest, and planting rules track maize planting dates, so policy should target current variability over future change.

Governments forecast harvests partly from farmers’ mid-season yield expectations, which may be biased. Data from field surveys are often late and costly. Zambia’s maize forecast and post-harvest surveys were compared by district over six seasons from 2001 to 2012, alongside yield models built from weather and soil data. Forecasts averaged above the harvests later recorded, with a typical district-level error of 780 kilograms per hectare, 56% of mean recorded yield. A machine learning model cut that by 13.3%, beating the survey even on pre-planting weather. A major limitations is that models fitted to past weather-yield relationships, may underperform on unprecendented climate extremes.