Agricultural intensification through multiple-season farming: Effects on resiliency, food security and nutrition
Bibliographic record
Abstract
Agricultural intensification is key to improving food security, nutrition, and resilience. While most approaches focus on increasing productivity using improved inputs like fertilizers, an alternative, relatively understudied method involves multiple-season farming—cultivation across multiple periods in a year, enabled by conditions such as irrigation or alternative water access. Using panel data from the World Bank Living Standards Measurement Study (2010–2019) in Malawi, we examine how multiple-season farming shapes household food security, child nutrition, and resilience, defined as persistent food security over time. To mitigate selection bias, we employ fixed-effects and instrumental variable fixed-effects models. We find that multiple-season farming is statistically significantly associated with improved household food security (at the 1% level), particularly for asset-rich and male-headed households. Multiple season farmers cultivate more diverse crops and engage in market sales, which enhances their food supply. However, we find no significant impact on child nutrition. Using Cissé and Barrett’s (2018) moment-based method to estimate resilience, we provide suggestive evidence that multiple-season farming also positively affects household resilience. These results point to the potential of multiple-season farming to bolster food security and help farmers adapt to climate change. Our results also highlight the need for interventions that support poor and female-headed households to access the inputs needed for multiple season farming. • Multiple season farmers consume one more food group weekly and have a higher Food Consumption Score. • Multiple season farmers cultivate 1–2 more crop types and are more likely to sell crops in markets. • Multiple season farming (MSF) and resiliency are positively associated though this result is not robust to an IV specification. • MSF positively impacts the food security of better-off and male-headed households. • We do not find a statistically significant relationship between MSF and child nutrition.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".