SSCI征稿: 就业与农业食品系统

本期征稿期刊：Food Policy

【主题】就业与农业食品系统

【关键词】农业食品系统，劳动力，就业，工作岗位，农业中小微企业，节约劳动力技术，可持续集约化，技能

征稿期刊

Food Policy

期刊指标

IF 6 (JCR 2024)

SSCI

Q1 (ECONOMICS 37/620)

ABS 3 / ABDC B / FMS B

年发文量126

征稿主题

Jobs and the Agrifood System

细分领域

What do jobs in agrifood look like? Despite much recent work at the global level using ILO data and national accounts, the understanding of the numbers, demographic composition and evolution of agrifood jobs remains incomplete. Systematic information is especially tenuous on the characterization of agrifood jobs in the off-farm segment, on agrifood job quality, on and off the farm (earnings and working conditions), on their location (rural, peri-urban and urban by city size), as well as the extent of underemployment and seasonality, and intra- and intersectoral labor productivity differences, including across farm and firm size. Related questions include whether agriculture is feminizing and whether youth is disproportionately leaving agriculture.

Which support packages to bundle and organizational models to scale? Agrifood expansion requires foundational investments, clear rules and predictable policies, and risk sharing measures to attract private investment in farming and agribusinesses. Much remains to be learnt, however, on whom best to support (farmers or agri-MSMEs ) to expand productive agrifood employment on and off the farm and how best to bundle and sequence interventions. Another challenge is determining which organizational models to scale, especially where financing and capacity are lacking. Possible models include anchor firms that coordinate and invest across the value chain, producer organizations that aggregate smallholders and strengthen their market position, or last mile agents who deliver inputs, services and information directly to farmers. But SME development and employment generation may also emerge organically once foundational investments and an enabling policy environment are in place.

How best to leverage technology? While mechanization and automation may reduce labor needs in some settings, productivity gains tend to improve food security and job quality for farmers, on average. Mechanization can also benefit small farmers through machinery-services and right-sized technologies, like two-wheel tractors. In wealthier countries, tasks like horticulture harvesting, milking, and meat cutting are rapidly getting automated. In lower income countries, however, these subsectors would hold most agrifood job growth potential. Central questions concern how labor saving (mechanization, automation, herbicides) and other technologies (digitalization, AI) affect the different dimensions of the job agenda (their number, quality and inclusion), how impacts vary by the type of technology and agro-ecological and socio-economic context, and how they may vary between the short and the long run, including through spill-over effects on non-adopters.

How to make sustainable intensification good for producers and the environment? Environmental degradation and climate change make it essential to boost output while using less land and water and preserving soil health. Yet sustainable intensification (SI), often comes with upfront costs and a steep learning curve, delayed gains, and uncertain implications for jobs. When increased labor needs are not offset by higher yields, for example, adoption may be sluggish; if offset, the structural transformation may slow. We need a better understanding of how SI affects labor outcomes across crops, settings, and time periods, and how policies can foster adoption and make SI work for producers and the environment.

What skill deficits need to be addressed? Ensuring more people, young and old, can succeed in farming and agribusiness, given the rapidly changing environment, requires that they have the right skills and access to necessary education and training. This starts with a clear picture of which skills are missing, where they’re missing—in the field or beyond—and whether today’s education and training programs and online tools are addressing the right gaps. For extension, for example, the latest evidence emphasizes designing extension systems around the way farmers learn in their local conditions (World Bank, 2025). Less is known about the skill needs and delivery systems for agribusinesses.

重要时间

Submission Deadline: 30 September 2026

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