Agriculture

Purpose and Perspective

Agriculture production in iSDG includes crops production, fishery production (separating fish catch and harvest - aquaculture), livestock production and forestry production.

Crops production is influenced by the harvested area, and soil nutrition (with the availability of macro-nutrients N, P and K represented), precipitation, irrigation, which along with total factor productivity affects the actual yield [1]. Production factors are combined as in a Cobb-Douglas production function. Factor productivity depends on several other drivers, including: education (average years of schooling used as proxy); health (life expectancy used as proxy); infrastructure (including roads and irrigation infrastructure); access to electricity; level of governance; macroeconomic stability (inflation rate used as proxy); openness to trade; and public agriculture expenditure. More specifically, an increase in the production factors or their productivity reduces the difference between actual and attainable yield.

Livestock production is affected by the same factors described above for crops. However, in the case of livestock, production per unit of land does not converge to a maximum attainable yield, but its growth is determined directly by growth in driving factors and the corresponding elasticity parameters.

Fish capture is also affected by the same factors described above for crops. However, in the case of fishery, we do not consider a maximum yield based on soil and climate factors, but production is limited by the availability of fish resources (stocks). Fish harvest (farming), separately represented in the model, does not depend on the availability of fish resources.

In the production functions described above, growth in production is driven by the increase in availability of the necessary production factors or by the increase in their productivity. This implies that demand factors are not considered in the calculation of production, that the quantities produced are fully consumed, and prices are exogenous to the model. Such production function can adequately represent the long-term pattern of production growth, and is therefore well suited to calculate production in iSDG. On the other hand, the production functions used are not suitable to represent short-term fluctuations in production caused by the accumulation of inventories of finished goods. Since iSDG is geared toward the analysis of long-term trends and not short-term fluctuations, these limitations do not affect the validity of the model.

Model Structure and Major Assumptions

• Attainable yield depends on potential yield and availability of water and macro-nutrients [2]

• Production factors include land, capital, and labor [3]

• Capital can be damaged by extreme events [4]

• Total factor productivity (TFP) depends on the level of: infrastructure [5]; education [6]; health [7]; governance [8]; access to electricity [9]; macroeconomic stability [10]; female participation in the workforce [11]; openness to trade [12]; climate change [13]; energy prices [14]; and public expenditure in agriculture [15].

• As production factors and their productivity increases, the gap between actual yield and attainable yield is reduced [16]

Exogenous Input Variables

• Crop Intensity Index - Units: Dmnl

• Crop Production Value Per Ton - Units: RLCU [17]/Ton

• Livestock value added per ton – Units: RLCU/Ton

• Fish Catch Production - Units: RLCU/Year

• Fish Harvest Production - Units: RLCU/Year

• Forestry Production - Units: RLCU/Year

• Other agriculture input cost per ton of production – Units: RLCU/ton/Year

• Effect of change in type of crop on yield – Units: Dimensionless

Initialization Variables

• Initial crops production – Units: RLCU/Year

• Initial yield - Units: Ton/(Ha*Year)

• Potential yield - Units: Ton/(Ha*Year)

• Initial livestock production – Units: RLCU/Year

• Initial livestock production per hectare – Units: RLCU/Year/Ha

• Initial agriculture capital output ratio – Units: Year

Modeling Details

Agriculture production calculated in this sector includes crops production (further separated to cereals and other crops by way of the subscript [crop]) and livestock production. Further separation among crops can be introduced as needed by expanding the [crop] subscript (and the underlying database).

In iSDG the production factors are used in unit-consistent form, using the values for capital, labor, and land relative to their initial values, or normalized. A similar approach is used to normalize the drivers of productivity. Thanks to such normalization, the effects of production factors and drivers of productivity are effectively and consistently combined. More specifically all such effects are combined in a multiplicative form, assuming Hicks-neutral technological change.

Finally, the yield gap ratio (i.e. attainable yield minus actual yield over attainable yield) is calculated as:

\(Y = \frac{Y^{crop}_{init}}{TFP_{crop}*K_{crop}^{k}* L^{l_{crop}}}\)

Where

• Y is the yield gap ratio

• TFP is total factor productivity

• K is the relative capital per hectare

• k is the capital elasticity

• L is the relative employment per hectare

• l is the labor elasticity on yield

• Different crop types are represented in the model (in the core model, cereals and other), and the initial value is the model start year.

Related Projects and Publications

• Impact of agroecology on the achievement of the Sustainable Development Goals - An integrated scenario analysis

• Changing Course in Global Agriculture: Senegal

• Changing Course in Global Agriculture: Kenya

• A holistic lens on rice value chain pathways in Senegal: Application of the “Economics and Biodiversity for Agriculture and Food” Framework

• Biosphere smart agriculture in a true cost economy

• Agriculture, Food and Nutrition Security and Rural Poverty Scenarios in Kenya

• New models for sustainable agriculture

• Food Provision and Environmental Goals in the Swiss Agri-food System: System Dyanmics and the Social-ecological Systems Framework

• Sustainability in global agriculture driven by organic farming

Footnotes and References

[1] The yield structure is not explicity captured in the simplified diagram, but is simply the crops production divided by the harvested area.

[2] Steduto, P. Hsiao, T.C., Fereres, E., & Raes, D. (2012). Crop yield response to water (Vol. 1028). Rome: Food and Agriculture Organization of the United Nations.

Tan, Z.X., Lai, R., & Wiebe, K.D. (2005) Global soil nutrient deplention and yield reduction. Journal of Sustainable Agriculture, 26, 1: 123-146.

[3] Bosworth, B., Collins, S.M. et al., (1995). Accounting for Differences in Economic Growth. Discussion Papers Series, 115. Cambridge, MA: Brookings Institution.

Senhadji, A. (1999). Sources of Economic Growth: An Extensive Growth Accounting Exercise. IMF Working Paper Series, (WP/99/77). Washington, DC: International Monetary Fund.

[4] Intergovernmental Panal on Climate Change (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change [Field, C.B., Barros, V., Stocker, T.F., Qin, D., Dokken, D.F., Ebi, K.L., Mastrandrea, M.D. et al.(Eds.)]. Cambridge, UK, and New York, NY: Cambridge University Press.

[5] Calderón, C. & Servén, L. (2004). The Effects of Infrastructure Development on Growth and Income Distribution. World Bank Working Paper, (WPS3400). Washington, DC: World Bank.

Canning, D. (1999). The Contribution of Infrastructure to Aggregate Output. World Bank Policy Research working Paper Series, (2246). Washington, DC: World Bank.

[6] Barro, R.J. (2001). Human Capital and Growth. American Economic Review, AEA P&P, 91(2): 12-17.

Nelson, R.R., Phelps, E.S. (1966). Investment in Humans, Technological Diffusion, and Economic Growth. American Economic Review, 56, 1/2: 69-75.

Romer, P. (1990). Endogenous Technological Change. Journal of Political Economy, 98(5): s71-s102.

[7] Bloom, D.E., Canning, D. et al. (2001). The Effect of Health on Economic Growth: Theory and Evidence. NBER Working Paper Series, (WP8587). Cambridge, MA: National Bureau of Economic Research.

Howitt, P., (2005). Health, Human Capital and Economic Growth: A Schumpeterian Perspective. In López-Casanovas, G., Rivera, B., & Currais, L. (Eds.) Health and Economic Growth: Findings and Policy Implications. Cambridge, MA: MIT Press.

López-Casasnovas, G., B. Rivera, et al., (2005). Introduction: The Role Health Plays in Economic Growth. In López-Casanovas, G., Rivera, B., & Currais, L. (Eds.) Health and Economic Growth: Findings and Policy Implications. Cambridge, MA: MIT Press.

[8] Kaufmann, D., Kraay, A. et al. (2002). Governance Matters II, Updated Indicators for 2000/01. Policy Research Working Paper, (2772). Washington, DC: World Bank.

[9] Calderón, C. & Servén, L. (2004). The Effects of Infrastructure Development on Growth and Income Distribution. World Bank Working Paper, (WPS3400). Washington, DC: World Bank.

[10] Bruno, M., Easterly, W. (1998). Inflation crises and long-run growth. Journal of Monetary Economics 41: 3-26.

Fischer, S. (1993). The Role of Macroeconomic Factors in Growth. Journal of Monetary Economics, 32: 485-512.

[11] Boileau L., Diouf, M.A. (2009). Revisiting the Determinants of Productivity Growth: What’s New? IMF working paper WP/09/225. Washington, DC: International Monetary Fund.

Cuberes, D., & Teignier, M. (2012). Gender gaps in the labor market and aggregate productivity, Sheffield Economic Research Paper Series. Sheffield, UK: University of Sheffield.

Food and Agriculture Organization (2011). The state of food and agriculture 2012-11. Rome: Food and Agriculture Organization of the United Nations.

[12] Edwards, S. (1998). Openness, productivity and growth: What do we really know? The Economic Journal, 108(447):383-398.

Yanikkaya, H. (2003). Trade Openness and Economic Growth: A Cross-Country Empirical Investigation. Journal of Development Economics, 72(1): 57-89.

[13] Burke, M., Hsiang, S.M., & Miguel, E. (2015). Global non-linear effect of temperature on economic production. Nature, 527: 235-239.

[14] Arezki, R., Blanchard, O. (2014). Seven Questions about the Recent Oil Price Slump. IMFdirect - The IMF Blog, December 22, 2014.

Jimenez-Rodriguez, R., & Sanchez, M. (2005). Oil Price Shocks and Real GDP Growth: Empirical Evidence for Some OECD Countries. Applied Economics, 37(2): 201-228.

Peersman, G. & Van Robays, I. (2012). Cross-country differences in the effects of oil shocks. Energy Economics, 34(5):* 1532-1547.

[15] Mogues T., Yu, B., Fan, S., & McBride, L. (2012). The impacts of public investment in and for agriculture: Synthesis of the existing evidence. ESA Working paper No. 12-07, October 2012. Agricultural Development Economics Division, Food and Agriculture Organization of the United Nations

[16] Pedercini, M., Dianati, K. & Baumgärtner, J. (2015). Development and evaluation of a biophysical crop production function for regional policy support in the tropics and subtropics.

[17] Real local currency unit, with base year chosen based on the country’s most commonly used base year.