Spatial variation in fertilizer prices in Sub-Saharan Africa
Autoři:
Camila Bonilla Cedrez aff001; Jordan Chamberlin aff002; Zhe Guo aff003; Robert J. Hijmans aff001
Působiště autorů:
Department of Environmental Science and Policy, University of California, Davis, California, United States of America
aff001; International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
aff002; International Food Policy Research Institute (IFPRI), Washington, DC, United States of America
aff003
Vyšlo v časopise:
PLoS ONE 15(1)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0227764
Souhrn
Low crop yields in Sub-Saharan Africa are associated with low fertilizer use. To better understand patterns of, and opportunities for, fertilizer use, location specific fertilizer price data may be relevant. We compiled local market price data for urea fertilizer, a source of inorganic nitrogen, in 1729 locations in eighteen countries in two regions (West and East Africa) from 2010–2018 to understand patterns in the spatial variation in fertilizer prices. The average national price was lowest in Ghana (0.80 USD kg-1), Kenya (0.97 USD kg-1), and Nigeria (0.99 USD kg-1). Urea was most expensive in three landlocked countries (Burundi: 1.51, Uganda: 1.49, and Burkina Faso: 1.49 USD kg-1). Our study uncovers considerable spatial variation in fertilizer prices within African countries. We show that in many countries this variation can be predicted for unsampled locations by fitting models of prices as a function of longitude, latitude, and additional predictor variables that capture aspects of market access, demand and environmental conditions. Predicted within-country urea price variation (as a fraction of the median price) was particularly high in Kenya (0.77–1.12), Nigeria (0.83–1.34), Senegal (0.73–1.40), Tanzania (0.90–1.29) and Uganda (0.93–1.30), but much lower in Burkina Faso (0.96–1.04), Burundi (0.95–1.05), and Togo (0.94–1.05). The correlation coefficient of the country level models was between 0.17 to 0.83 (mean 0.52) and the RMSE varies from 0.005 to 0.188 (mean 0.095). In 10 countries, predictions were at least 25% better than a null-model that assumes no spatial variation. Our work indicates new opportunities for incorporating spatial variation in prices into efforts to understand the profitability of agricultural technologies across rural areas in Sub-Saharan Africa.
Klíčová slova:
Africa – Crops – Ethiopia – Fertilizers – Nigeria – Tanzania – Transportation – Urea
Zdroje
1. Ray DK, Ramankutty N, Mueller ND, West PC, Foley JA. Recent patterns of crop yield growth and stagnation. Nature communications. 2012; 3:1293. doi: 10.1038/ncomms2296 23250423
2. Van Ittersum MK, Van Bussel LG, Wolf J, Grassini P, Van Wart J, Guilpart N, et al. Can sub-Saharan Africa feed itself?. Proceedings of the National Academy of Sciences. 2016; 113:14964–9.
3. Bank World, 2018. World Development Indicators. Washington, D.C.: The World Bank Group. [cited 2019, Feb 1]. Available from: https://data.worldbank.org/indicator.
4. Howard J, Crawford E, Kelly V, Demeke M, Jeje JJ. Promoting high-input maize technologies in Africa: the Sasakawa-Global 2000 experience in Ethiopia and Mozambique. Food Policy. 2003; 28(4):335–48.
5. Mafongoya PL, Bationo A, Kihara J, Waswa BS. Appropriate technologies to replenish soil fertility in southern Africa. Nutrient Cycling in Agroecosystems. 2006; 76(2–3):137–51.
6. Vanlauwe B, Wendt J, Giller KE, Corbeels M, Gerard B, Nolte C. A fourth principle is required to define conservation agriculture in sub-Saharan Africa: the appropriate use of fertilizer to enhance crop productivity. Field Crops Research. 2014; 155:10–3.
7. Holden ST. Fertilizer and sustainable intensification in Sub-Saharan Africa. Global food security. 2018; 18:20–6.
8. Drechsel P, Gyiele L, Kunze D, Cofie O. Population density, soil nutrient depletion, and economic growth in sub-Saharan Africa. Ecological economics. 2001; 38:251–8.
9. Place F, Barrett CB, Freeman HA, Ramisch JJ, Vanlauwe B. Prospects for integrated soil fertility management using organic and inorganic inputs: evidence from smallholder African agricultural systems. Food policy. 2003; 28:365–78.
10. Marenya PP, Barrett CB. Soil quality and fertilizer use rates among smallholder farmers in western Kenya. Agricultural Economics. 2009; 40:561–72.
11. Jayne TS, Mather D, Mason NM, Ricker‐Gilbert J, Crawford EW. Rejoinder to the comment by Andrew Dorward and Ephraim Chirwa on Jayne, TS, D. Mather, N. Mason, and J. Ricker‐Gilbert. 2013. How do fertilizer subsidy program affect total fertilizer use in sub‐Saharan Africa? Crowding out, diversion, and benefit/cost assessments. Agricultural Economics, 44 (6), 687–703. Agricultural Economics. 2015; 46:745–55.
12. Barrett CB, Christiaensen L, Sheahan M, Shimeles A. On the structural transformation of rural Africa. The World Bank. 2017.
13. Quansah C, Drechsel P, Yirenkyi BB, Asante-Mensah S. Farmers' perceptions and management of soil organic matter–a case study from West Africa. Nutrient Cycling in Agroecosystems. 2001; 61:205–13.
14. Asfaw A, Admassie A. The role of education on the adoption of chemical fertiliser under different socioeconomic environments in Ethiopia. Agricultural economics. 2004: 215–28.
15. Matsumoto T, Yamano T. Soil fertility, fertilizer, and the maize green revolution in East Africa. The World Bank. 2010.
16. Croppenstedt A, Demeke M, Meschi MM. Technology adoption in the presence of constraints: the case of fertilizer demand in Ethiopia. Review of Development Economics. 2003; 7:58–70.
17. Adjognon SG, Liverpool-Tasie LS, Reardon TA. Agricultural input credit in Sub-Saharan Africa: Telling myth from facts. Food policy. 2017; 67:93–105. doi: 10.1016/j.foodpol.2016.09.014 28413249
18. Jama B, Kimani D, Harawa R, Mavuthu AK, Sileshi GW. Maize yield response, nitrogen use efficiency and financial returns to fertilizer on smallholder farms in southern Africa. Food Security. 2017; 9(3):577–93.
19. Koussoubé E, Nauges C. Returns to fertiliser use: Does it pay enough? Some new evidence from Sub-Saharan Africa. European Review of Agricultural Economics. 2017; 44(2):183–210.
20. Burke WJ, Jayne TS, Black JR. Factors explaining the low and variable profitability of fertilizer application to maize in Zambia. Agricultural economics. 2017; 48(1):115–26.
21. Liverpool-Tasie LS, Omonona BT, Sanou A, Ogunleye WO. Is increasing inorganic fertilizer use for maize production in SSA a profitable proposition? Evidence from Nigeria. Food policy. 2017; 67:41–51. doi: 10.1016/j.foodpol.2016.09.011 28413245
22. Benson T, Kirama SL, Selejio O. The supply of inorganic fertilizers to smallholder farmers in Tanzania: Evidence for fertilizer policy development.
23. Zo Druilhe. Fertilizer subsidies in sub-Saharan Africa. 2017.
24. Chamberlin J, Jayne TS, Headey D. Scarcity amidst abundance? Reassessing the potential for cropland expansion in Africa. Food Policy. 2014; 48:51–65.
25. Ariga J, Jayne TS. Fertilizer in Kenya: Factors driving the increase in usage by smallholder farmers. Yes Africa can: success stories from a dynamic continent, World Bank, Washington. 2011:269–88.
26. You L, Ringler C, Wood-Sichra U, Robertson R, Wood S, Zhu T, et al. What is the irrigation potential for Africa? A combined biophysical and socioeconomic approach. Food Policy. 2011; 36:770–82.
27. Binswanger-Mkhize HP, Savastano S. Agricultural intensification: the status in six African countries. The World Bank; 2014.
28. Ariga J, Jayne TS, Nyoro JK. Factors driving the growth in fertilizer consumption in Kenya, 1990–2005: Sustaining the momentum in Kenya and lessons for broader replicability in Sub-Saharan Africa. 2006.
29. Kihara J, Huising J, Nziguheba G, Waswa BS, Njoroge S, Kabambe V,et al. Maize response to macronutrients and potential for profitability in sub-Saharan Africa. Nutrient cycling in agroecosystems. 2016; 105:171–81.
30. Omamo SW. Fertilizer trade and pricing in Uganda. Agrekon. 2003; 42:310–24.
31. Komarek AM, Koo J, Wood-Sichra U, You L. Spatially-explicit effects of seed and fertilizer intensification for maize in Tanzania. Land use policy. 2018; 78:158–65.
32. Africa Fertilizer. Retail Fertilizer Prices. [cited 2019, Feb 1]. Available from: https://africafertilizer.org/
33. World Bank. Living Standards Measurement Study-Integrated Surveys in Agriculture. Washington, D.C.: The World Bank Group. [cited 2019, Feb 1], Available from: http://surveys.worldbank.org/lsms/programs/integrated-surveys-agriculture-ISA.
34. Nelson A. Estimated travel time to the nearest city of 50,000 or more people in year 2000. Global Environment Monitoring Unit—Joint Research Centre of the European Commission, Ispra Italy: 2008.
35. LandScan LandScan high resolution global population data set. [cited 2019, Feb 1]. Available from: https://landscan.ornl.gov/.
36. Xiong J, Thenkabail PS, Tilton JC, Gumma MK, Teluguntla P, Congalton RG, et al. NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Africa 30 m V001. [cited 2019, Feb 1]. NASA EOSDIS Land Processes DAAC. doi: 10.5067/MEaSUREs/GFSAD/GFSAD30AFCE.001
37. Fick SE, Hijmans RJ. WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International journal of climatology. 2017; 37:4302–15.
38. Hijmans RJ. raster: Geographic data analysis and modeling. R package version 2.8–4. 2018.
39. Breiman L. Random forests. Machine learning. 2001; 45:5–32.
40. Liaw A, Wiener M. Classification and regression by randomForest. R news. 2002; 2(3):18–22.
41. Hellwig E, Hijmans RJ. Spatio-temporal variation in childhood growth in Nigeria: a comparison of aggregation and interpolation. International journal of digital earth. 2017; 10:1166–76.
42. Hutchinson, Michael F. 1995. “Interpolating Mean Rainfall Using Thin Plate Smoothing Splines.” International Journal of Geographical Information Systems 9: 385–403. doi: 10.1080/02693799508902045
43. Nychka D, Furrer R, Paige J, Sain S. fields: Tools for spatial data. R package version 9.6. 2018.
44. Sheahan M, Barrett CB. Ten striking facts about agricultural input use in Sub-Saharan Africa. Food Policy. 2017; 67:12–25. doi: 10.1016/j.foodpol.2016.09.010 28413243
45. Zerfu D, Larson DF. Incomplete markets and fertilizer use: evidence from Ethiopia. World Bank Policy Research Working Paper 5235. The World Bank. 2010.
46. Agwe J, Morris M, Fernandes E. Africa’s growing soil fertility crisis: What role for fertilizer? Agriculture and Rural Development Notes 21. The World Bank. 2007.
47. Morris M, Kelly VA, Kopicki RJ, Byerlee D. Fertilizer use in African agriculture: Lessons learned and good practice guidelines. The World Bank. 2007.
48. Smith J, Weber G, Manyong MV, Fakorede MA. Fostering sustainable increases in maize productivity in Nigeria. Africa’s emerging maize revolution, Lynne Rienner Publishers Inc. 1997:107–24.
49. Eboh EC, Ujah OC, Amaechina EC. Do government fertiliser subsidies benefit rural poor farmers in Nigeria? Making sense out of existing data. PMMA Network Session Paper. Available online at: http://132.203. 2006 May;59.
50. Mustapha AR. Colonialism and environmental perception in Northern Nigeria. Oxford development studies. 2003; 31:405–25.
51. Takeshima H, Liverpool-Tasie LS. Fertilizer subsidies, political influence and local food prices in sub-Saharan Africa: Evidence from Nigeria. Food Policy. 2015; 54:11–24.
52. Chapoto A. The political economy of food price policy: The case of Zambia. WIDER Working Paper; 2012.
53. Chinsinga B. Seeds and subsidies: The political economy of input programmes in Malawi. IDS bulletin. 2011; 42(4):59–68.
54. Mpesi AM, Muriaas RL. Food security as a political issue: the 2009 elections in Malawi. Journal of contemporary African studies. 2012; 30(3):377–93.
55. Banful AB. Old problems in the new solutions? Politically motivated allocation of program benefits and the “new” fertilizer subsidies. World Development. 2011;39(7):1166–76.
56. Ricker-Gilbert J, Jayne TS, Chirwa E. Subsidies and crowding out: A double-hurdle model of fertilizer demand in Malawi. American journal of agricultural economics. 2011; 93: 26–42.
57. Mason NM, Jayne TS. Fertiliser subsidies and smallholder commercial fertiliser purchases: Crowding out, leakage and policy implications for Zambia. Journal of Agricultural Economics. 2013; 64:558–82.
58. Minten B, Koru B, Stifel D. The last mile (s) in modern input distribution: Pricing, profitability, and adoption. Agricultural Economics. 2013; 44(6):629–46.
59. Aggarwal S, Giera B, Jeong D, Robinson J, Spearot A. Market Access, Trade Costs, and Technology Adoption: Evidence from Northern Tanzania. National Bureau of Economic Research. 2018.
60. Komarek AM, Drogue S, Chenoune R, Hawkins J, Msangi S, Belhouchette H, Flichman G. Agricultural household effects of fertilizer price changes for smallholder farmers in central Malawi. Agricultural Systems. 2017; 154:168–78.
61. Golding N, Burstein R, Longbottom J, Browne AJ, Fullman N, Osgood-Zimmerman A, et al. Mapping under-5 and neonatal mortality in Africa, 2000–15: a baseline analysis for the Sustainable Development Goals. The Lancet. 2017; 390:2171–2182.
62. Osgood-Zimmerman A, Millear AI, Stubbs RW, Shields C, Pickering BV, Earl L, et al. Mapping child growth failure in Africa between 2000 and 2015. Nature. 2018; 555, 7694: 41.
63. Hijmans RJ. Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology. 2012; 93: 679–688. doi: 10.1890/11-0826.1 22624221
64. World Bank. World Development Report 2009: Reshaping Economic Geography. Washington, DC: World Bank. 2009.
Článek vyšel v časopise
PLOS One
2020 Číslo 1
- Tisícileté topoly, mokří psi, stárnoucí kočky a ospalé octomilky – „jednohubky“ z výzkumu 2024/41
- Jaké jsou aktuální trendy v léčbě karcinomu slinivky?
- Může hubnutí souviset s vyšším rizikem nádorových onemocnění?
- Menstruační krev má značný diagnostický potenciál, mimo jiné u diabetu
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
Nejčtenější v tomto čísle
- Severity of misophonia symptoms is associated with worse cognitive control when exposed to misophonia trigger sounds
- Chemical analysis of snus products from the United States and northern Europe
- Calcium dobesilate reduces VEGF signaling by interfering with heparan sulfate binding site and protects from vascular complications in diabetic mice
- Effect of Lactobacillus acidophilus D2/CSL (CECT 4529) supplementation in drinking water on chicken crop and caeca microbiome
Zvyšte si kvalifikaci online z pohodlí domova
Všechny kurzy