Agricultural productivity, comparative advantage, and economic growth

The role of agricultural productivity in economic development is addressed during a two-sector model of endogenous growth during which (a) preferences are non-homothetic and therefore the income elasticity of demand for the agricultural good is a smaller amount than unitary, and (b) the engine of growth is learning-by-doing within the manufacturing sector. For the closed economy case, the model predicts a positive link between agricultural productivity and economic process , while, for the tiny open economy case, it predicts a negative link. this means that the openness of an economy should be a crucial factor when planning development strategy and predicting growth performance. [1]

The Agricultural Productivity Gap

According to national accounts data, value added per worker is far higher within the nonagricultural sector than in agriculture within the typical country, particularly in developing countries. Taken at face value, this “agricultural productivity gap” suggests that labor is greatly misallocated across sectors. during this article, we draw on new micro evidence to ask to what extent the gap remains present when better measures of sector labor inputs and value added are taken into consideration. we discover that even after considering sector differences in hours worked and human capital per worker, also as alternative measures of sector output constructed from household survey data, a puzzlingly large gap remains. [2]

Agricultural Productivity Growth and Poverty Alleviation

How important is agricultural growth to poverty reduction? this text first sets out the theoretical reasons for expecting agricultural growth to scale back poverty. Several plausible and powerful arguments apply ‐ including the creation of jobs on the land, linkages from farming to the remainder of the agricultural economy, and a decline within the real cost of food for the entire economy ‐ but the degree of impact is altogether cases qualified by particular circumstances. Hence, the article deploys a cross‐country estimation of the links between agricultural yield per unit area and measures of poverty. This produces strong confirmation of the hypothesised linkages. it’s unlikely that there are many other development interventions capable of reducing the numbers in poverty so effectively. [3]

Key determinants of global land-use projections

Land use is at the core of varied sustainable development goals. Long-term climate foresight studies have structured their recent analyses around five socio-economic pathways (SSPs), with consistent storylines of future macroeconomic and societal developments; however, model quantification of those scenarios shows substantial heterogeneity in land-use projections. Here we repose on a recently developed sensitivity approach to spot how future land use depends on six distinct socio-economic drivers (population, wealth, consumption preferences, agricultural productivity, land-use regulation, and trade) and their interactions. Spread across models arises mostly from diverging sensitivities to long-term drivers and from various representations of land-use regulation and trade, calling for reconciliation efforts and more inquiry. [4]

Constraints to Agricultural Productivity in Kainji Lake National Park, Nigeria

The aim of this research article was to guage the constraints to agricultural productivity in Kainji Lake park (KLNP). This research was administered between the months of January to December, 2011. there have been five districts in Kainji Lake park during which three communities were selected from each district using simple randomised sampling technique. a complete of 600 structured questionnaires were used for this study of which 40 copies were administered in each of the 15 villages sampled within the study area. The retrieved data were analysed using both descriptive and inferential statistics, stepwise multiple correlation was also adopted to spot the contribution of agricultural constraints to the entire food production within the study area. The results showed that the year class of 11-15 years had the very best duration of farmland cultivation of 35.8%, while the smallest amount farmland cultivation duration was class 20 years and above with 4.6% (Table 2). [5]


[1] Matsuyama, K., 1992. Agricultural productivity, comparative advantage, and economic growth. Journal of economic theory, 58(2), (Web Link)

[2] Gollin, D., Lagakos, D. and Waugh, M.E., 2014. The agricultural productivity gap. The Quarterly Journal of Economics, 129(2), (Web Link)

[3] Irz, X., Lin, L., Thirtle, C. and Wiggins, S., 2001. Agricultural productivity growth and poverty alleviation. Development policy review, 19(4), (Web Link)

[4] Key determinants of global land-use projections
Elke Stehfest, Willem-Jan van Zeist, Hugo Valin, Petr Havlik, Alexander Popp, Page Kyle, Andrzej Tabeau, Daniel Mason-D’Croz, Tomoko Hasegawa, Benjamin L. Bodirsky, Katherine Calvin, Jonathan C. Doelman, Shinichiro Fujimori, Florian Humpenöder, Hermann Lotze-Campen, Hans van Meijl & Keith Wiebe
Nature Communications volume 10, (Web Link)

[5] Jeminiwa, O. R., Okanlawon, T. F., Taiwo, D. M., Olaoti-Laaro, S. O. and Jeminiwa, M. S. (2018) “Constraints to Agricultural Productivity in Kainji Lake National Park, Nigeria”, Asian Journal of Research in Agriculture and Forestry, 2(1), (Web Link)

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