ABSTRACT

The Alliance for a Green Revolution in Africa (AGRA) is the latest in a series of initiatives aimed at modernizing small-scale farming to address food insecurity and poverty, yet AGRA has fallen short of its goals. This study explores whether these shortcomings might stem from flawed assumptions in AGRA's theory of change—assumptions long embedded in top-down agricultural modernization efforts. We situate AGRA within broader debates on the agrarian question, especially the Chayanov–Lenin debate, and draw historical parallels with United States agricultural industrialization, the Green Revolution, and Soviet collectivization, as well as Tanzania's villagization program. Tanzania is an instructive case, having undergone both collectivist and market-based modernization. Using Chayanov's theory of peasant household decision-making, we analyze panel survey data from the Tanzania National Panel Survey (TNPS), part of the World Bank Living Standards Measurement Study—Integrated Surveys on Agriculture (LSMS-ISA) program, to examine how household demographic factors relate to labor and land use decisions. Our findings show that household composition is significantly associated with agricultural labor allocation choices and land use. We also address Chayanov's gender blind spot, finding that men and women plot managers and men- and women-headed households often pursue different labor allocation and land use strategies. These results suggest that AGRA's model may make questionable assumptions about the decision-making of small-scale farmers. We conclude by considering the implications of this modernization logic and argue that a pragmatic approach to agricultural development, one rooted in the actual priorities and preferences of small-scale farmers, offers an alternative.

A study published in Insect Science reports that reducing fertilizer input can improve natural pest control without compromising crop yields, challenging the assumption that higher nutrient use is always beneficial in agriculture.

New Study shows: What crop advisors really want from AI tech and how precision ag producers will decide on AI adoption.

Research Highlights:

Discrete choice experiments quantify trade-offs in crop advisors’ preferred AI-DSS features.

Advisors favor simplicity and satellite inputs over ultra-accurate or precision-heavy AI-DSS.

AI attitudes moderate acceptance: techno-optimists are more open to data-intensive AI-DSS.

Implications: built trust, ensure cost transparency, and align AI-DSS with user autonomy to boost adoption.