Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

12. Enhancing Productivity and Market Access for Key Staples in the EAC Region: An Economic Analysis of Biophysical and Market Potential

verfasst von : Siwa Msangi, Kennedy Were, Bernard Musana, Joseph Mudiope, Leonidas Dusengemungu, Lucas Tanui, Jean-Claude Muhutu, George Ayaga, Geophrey Kajiru, Birungi Korutaro

Erschienen in: Applied Methods for Agriculture and Natural Resource Management

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In this chapter, we show how the current crop areas under three key staples—rice, maize, and beans—could be better aligned with the crop suitabilities that are inherent in the East African Community (EAC) region, through some key policy interventions. We take a multi-market model that was constructed for the 5 main countries in the EAC and use it to demonstrate how reducing transport costs, and increasing crop productivities can lead to market-level welfare improvements, as well as a closer alignment between the areas where the crops are cultivated, and the areas with the best agronomic suitability for those crops. At present, a significant proportion of those staples are grown in areas with limited growth potential, but opening up markets in combination with productivity-focused investments can allow countries to make better use of the crop potential they already have, and take advantage of regional market opportunities.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Climate Policies as Water Policies
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
The East African Community consists of five key countries—Burundi, Kenya, Rwanda, Tanzania, and Uganda. South Sudan is now also an official part of the EAC; however, a lack of reliable data and links to research experts in that area has led to its omission from this study.
 
2
Like the Bill and Melinda Gates Foundation’s efforts to support the World Bank in adding details relevant to agricultural in their Living-Standards and Measurement Surveys (LSMS)—resulting in the LSMS-ISA (i.e., ‘Integrated Surveys on Agriculture’) project.
 
3
See the data products available at: https://​harvestchoice.​org/​.
 
4
Non-food demand also contains categories such as ‘seed use’ and ‘waste’ which are tracked in the FAO food balances, but which we do not have sufficient information for to model explicitly.
 
5
The GIS-based analysis of crop suitability is described in a separate technical document of Were et al. (2016).
 
6
In this function, the parameter \(c_{A}^{R,r}\) is a calibrating constant, and the ‘elasticity’ \(\gamma\) gives the response of area to a change in price.
 
7
In our implementation, we did not model trade between the sub-regions (r) of each country, as that would have imposed an enormous computational burden on the model, and require detailed data beyond what we possess. So, we only model trade at the national level, in this model.
 
8
In our formulation, we treat the ‘rest of the World’ as a homogeneous entity, knowing that it represents something different to each EAC country, in reality. For the inland, landlocked countries, for example, the rest of the World, would be the bordering Congo or the Sudan, whereas the coastal countries receive goods from the ‘rest of the World’ over ocean-based routes at Mombasa, Dar-es-Salaam, Tanga, Mtwara, or from neighboring, Ethiopia, Malawi, Zambia, or Mozambique. By simplifying the representation of RoW to one entity, we cannot (therefore) tie a particular landed price at a given point of entry or exit as the border price—but hypothesize a composite world price which we have to solve for in the calibration process.
 
9
The application of maximum entropy inference methods is explained in further detail in the Technical Annex (2).
 
10
Here, we interpret net trade as net exports (exports minus imports)—which means a positive quantity makes the country a net exporter and a net importer of the good if negative.
 
11
In this scenario, this yield increase is implemented for those crops falling into zones where less than 60% of the maximum potential is currently being realized.
 
12
The GIS-based analysis of crop suitability is described in separate technical documentation of the WaLETS project and has been led by Kennedy Were of KALRO and his colleagues. This can be accessed at: https://​www.​kilimotrust.​org/​documents/​reports/​2017/​walets/​WaLETS_​Final_​Reports/​WaLETS_​GIS_​TechnicalReport.​pdf.
 
13
In this function, the parameter \(c_{A}^{R,r}\) is a calibrating constant, and the ‘elasticity’ \(\gamma\) gives the response of area to a change in price.
 
14
The function does not have to be quadratic—but must be convex in curvature. We have chosen the quadratic form simply for analytical convenience (in implementation and exposition to the reader).
 
15
In cases where a good is ‘inferior’ to other preferred goods, the per-capita consumption could go down with income. An example could be the declining demand for coarse grains (millet/sorghum) as household income increases, in favor of rice- and wheat-based products.
 
16
The ‘no-arbitrage’ condition describes a competitive market equilibrium, where any opportunity for selling a good for a higher price than the original purchase price + transport cost is exhausted. So, at best, an agent can break even by selling a good for exactly the cost at which it was purchased plus the cost of delivering it to the destination, but no more. This assumption could be relaxed in a less competitive market environment—but that is beyond the scope of this study.
 
17
This describes the kind of ‘mixed complementarity’ formulation that is commonly applied to solve trade equilibrium problems and is found in other types of mathematical programming problems. In such a problem, one does not need to maximize or minimize an economic objective function, since these complementary relationships summarize the first-order necessary conditions required to solve the implicit optimization problem. See Paris (2010) for more details.
 
18
In our implementation, we did not model trade between the sub-regions (r) of each country, as that would have imposed an enormous computational burden on the model and require detailed data beyond what we possess. So, we only model trade at the national level, in this model.
 
Literatur
Anyango, G. (1997). Comparative transportation cost analysis in East Africa. Technical Paper no 22, SD Publications Series, Office of Sustainable Development, Bureau for Africa, United States Agency for International Development (USAID), Washington D.C.
Barkaoui, A., & Boutault, J.-P. (2000). Cereal and oil seeds supply with EU under agenda 2000: A positive mathematical programming application. Agricultural Economics Review, 1(2), 7–17.
Doole, G., & Marsh, D. (2014). Use of positive mathematical programming invalidates the application of the NZFARM model. Australian Journal of Agricultural and Resource Economics, 58(2), 291–294.CrossRef
Foster, V., & Briceño-Garmendia, C. (2010). Africa’s infrastructure: A time for transformation. Washington, D.C.: The World Bank.
Gohin, A., & Chantreuil, F. (1999). La Programmation Mathématique Positive dans les Modèles d’Exploitation Agricole. Principes et Importance du Calibrage. Cahiers d’Économie et de Sociologie Rurales, 52, 59–79.
Heckelei, T., & Britz, W. (2000). Positive mathematical programming with multiple data points: A cross-sectional estimation procedure. Cahiers d’Economie et Sociologie Rurales, 57, 28–50.
Heckelei, T., & Wolff, H. (2003). Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy. European Review of Agricultural Economics, 30(1), 27–50.CrossRef
Heckelei, T., & Britz, W. (2005). Models based on positive mathematical programming: State of the art and further extensions. In F. Arfini (Ed.), Modelling agricultural policies: State of the art and new challenges: Proceedings of the 89th European Seminar of the European association of agricultural economists (pp. 48–73). Parma: Monte Università.
Heckelei, T., Britz, W., & Zhang, Y. (2012). Positive mathematical programming approaches—recent developments in literature and applied modelling. Bio-based and Applied Economics, 1(1), 109–124.
Henry de Frahan, B., Buysse, J., Polomé, P., Fernagut, B., Harmignie, O., Lauwers, L., et al. (2007). Positive mathematical programming for agricultural and environmental policy analysis: Review and practice. In A. Weintraub, C. Romero, T. Bjørndal, & R. Epstein (Eds.), Handbook of operations research in natural resources (pp. 129–154). New York: Springer.CrossRef
Howitt, R. E. (1995a). Positive mathematical programming. American Journal of Agricultural Economics, 77(2), 329–342.CrossRef
Howitt, R. E. (1995b). A calibration method for agricultural economic production models. Journal of Agricultural Economics, 46(2), 147–159.CrossRef
Howitt, R. E., Medellín-Azuara, J., MacEwan, D., & Lund, J. R. (2012). Calibrating disaggregate economic models of agricultural production and water management. Environmental Modelling and Software, 38, 244–258.CrossRef
Judez, L., Chaya, C., Martınez, S., & González, A. (2001). Effects of the measures envisaged in “Agenda 2000” on arable crop producers and beef and veal producers: An application of Positive Mathematical Programming to representative farms of a Spanish region. Agricultural Systems, 67(2), 121–138.CrossRef
Kaaya, A. K., Msanya, B. M., & Mrema, J. P. (1994). Soils and land evaluation of part of the Sokoine University of Agriculture farm (Tanzania) for some crops under rain-fed conditions. African Study Monographs, 15(2), 97–117.
Kanellopoulos, A., Berentsen, P., Heckelei, T., Van Ittersum, M., & Oude Lansink, A. (2010). Assessing the forecasting performance of a generic bio-economic farm model calibrated with two different PMP variants. Journal of Agricultural Economics, 61, 274–294.CrossRef
Kullback, S. (1959). Information theory and statistics. New York: John Wiley.
Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics, 22, 79–86.CrossRef
Mérel, P., & Howitt, R. (2014). Theory and application of positive mathematical programming in agriculture and the environment. Annual Review of Resource Economics, 6(1), 451–470.CrossRef
Mérel, P. R., & Bucaram, S. (2010). Exact calibration of programming models of agricultural supply against exogenous sets of supply elasticities. European Review of Agricultural Economics, 37(3), 395–418.CrossRef
Mérel, P. R., Simon, L. K., & Yi, F. (2011). A fully calibrated generalized constant-elasticity-of-substitution programming model of agricultural supply. American Journal of Agricultural Economics, 93, 936–948.CrossRef
Mérel, P. R., Yi, F., Lee, J., & Six, J. (2014). A regional bio-economic model of nitrogen use in cropping. American Journal of Agricultural Economics, 96(1), 67–91.CrossRef
Omamo, S. W., Diao, X., Wood, S., Chamberlin, J., You, L., Benin, S., et al. (2006). Strategic priorities for agricultural development in Eastern and Central Africa. Research Report 150. Washington D.C.: International Food Policy Research Institute. https://​doi.​org/​10.​2499/​9780896291584rr1​50.
Paris, Q. (2010). Economic foundations of symmetric programming. Cambridge: University Press.CrossRef
Robinson, S., Cattaneo, A., & El-Said, M. (2000). Updating and estimating a social accounting matrix using cross entropy methods. Trade and MacroEconomics Division Discussion Paper No. 58. Washington, D.C.: IFPRI.
Rosegrant, M. W., Paisner, M. S., Meijer, S., & Witcover, J. (2001). Global food projections to 2020: Emerging trends and alternative futures. Washington, D.C.: International Food Policy Research Institute.
Rosegrant, M. W., Cai, X., & Cline, S. A. (2002). Water and food to 2025: Dealing with scarcity. Washington, D.C.: International Food Policy Research Institute.
Rosegrant, M. W., et al. (2012). International model for policy analysis of agricultural commodities and trade (IMPACT): Model description. Washington, D.C.: International Food Policy Research Institute.
Sadoulet, E., & de Janvry, A. (1994). Quantitative development policy analysis. Baltimore, MD: Johns Hopkins University Press.
Shannon, C. E. (1948a). A mathematical theory of communication. The Bell System Technical Journal, 27(July), 379–423.CrossRef
Shannon, C. E. (1948b). A mathematical theory of communication. The Bell System Technical Journal, 27(October), 623–656.CrossRef
Teravaninthorn, S., & Raballand, G. (2009). Transport prices and costs in Africa: A review of the main international corridors. Washington D.C.: The World Bank.
You, L., & Wood, S. (2004). Assessing the spatial distribution of crop production using a cross-entropy method. Environment and Production Technology Division Discussion Paper No. 126. Washington, D.C.: IFPRI.
Metadaten
Titel
Enhancing Productivity and Market Access for Key Staples in the EAC Region: An Economic Analysis of Biophysical and Market Potential
verfasst von
Siwa Msangi
Kennedy Were
Bernard Musana
Joseph Mudiope
Leonidas Dusengemungu
Lucas Tanui
Jean-Claude Muhutu
George Ayaga
Geophrey Kajiru
Birungi Korutaro
Copyright-Jahr
2019
Buch
Applied Methods for Agriculture and Natural Resource Management

Print ISBN: 978-3-030-13486-0

Electronic ISBN: 978-3-030-13487-7

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-13487-7_12