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Impact assessment of push-pull pest management on incomes, productivity and poverty among smallholder households in Eastern Uganda

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Abstract

The paper evaluates the impact of adoption of push-pull technology (PPT) on household welfare in terms of productivity, incomes and poverty status measured through per-capita food consumption in eastern Uganda. Push-pull is a habitat management strategy for the integrated management of stemborers, striga weeds and poor soil fertility involving the use of a natural repellent (push) and an attractant (pull). This biological technology simultaneously reduces the impact of three major production constraints to cereal-livestock farming in Africa − pests, weeds and poor soil. Cross sectional survey data were collected from 560 households in four districts in the region (Busia, Tororo, Bugiri and Pallisa), in November and December 2014. Generalized propensity scoring (GPS) was used to determine the intensity of adoption of the technology (i.e., land area allocated to PPT) and also to estimate the dose-response function (DRF) relating intensity of adoption and household welfare. Results revealed that with increased intensity of reported adoption of PPT, the probability of being poor declined through increased maize yield per unit area, incomes, and per capita food consumption. However, its impact varied with the intensity of adoption. With an increase in the area allocated to PPT from 0.025 to 1 acre, average maize yield per unit area increased from 27 kg to 1400 kg, average household income increased from 135 US$ (Uganda Shilling (USh) 370,000) to 273 US$ (USh 750,000) and per capita food consumption increased from 15 US$ (USh 40,000) to 27 US$ (USh 75,000). The average probability of a household being poor (below a rural poverty line of US$ 12.71) declined from 48% to 28%. These findings imply that increased investment in the dissemination and expansion of PPT is essential for poverty reduction among smallholder farmers in Uganda.

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Notes

  1. Within the context of this research, adopters are farmers who have decided to exploit the full potential of the push-pull technology for management of striga weeds and stemborer pests, and also to boost soil fertility

  2. A relative poverty approach is based on the cost of basic needs (CBN) approach in which some minimum nutritional requirement is defined and converted into minimum food expenses. To this is added some considered minimum non-food expenditure such as for clothing and shelter (Ravallion and Bidani 1994).

  3. The average exchange rate during the survey was 1 US$ = USh 2748

  4. Agricultural productivity is defined and measured in a number of ways including land productivity or yield. Productivity is output per unit area cultivated, commonly expressed in tonnes per hectare (t/ha) or kilograms per acre (kg/acre) (Wiebe et al. 2001). In our study, productivity was defined as maize output per acre (kg/acre)

  5. Total livestock unit was calculated as (1 for a bull +0.7 for a cow +0.5 for a heifer +0.5 for a young bull +0.3 for a calf +0.1 for a goat +0.1 for a sheep +0.05 for a duck +0.05 for a turkey +0.01 for a chicken +0.2 for a pig) (Otte and Chilonda 2002).

References

  • Adkins, L. C., Campbell, R. C., Chmelarova, V., & Carter Hill, R. (2012). The Hausman Test, and Some Alternatives, with Heteroskedastic Data. In Essays in Honor of Jerry Hausman (pp. 515-546). United Kingdom: Emerald Group Publishing Limited.

    Chapter  Google Scholar 

  • AGRA. (2013). Africa agriculture status report: Focus on staple crops. Kenya: Alliance for a Green Revolution in Africa Nairobi.

    Google Scholar 

  • Amare, M., Asfaw, S., & Shiferaw, B. (2012). Welfare impacts of maize–pigeon pea intensification in Tanzania. Agricultural Economics, 43(1), 27–43.

    Article  Google Scholar 

  • Amudavi, D. M., Khan, Z. R., Wanyama, J. M., Midega, C. A., Pittchar, J., Hassanali, A., & Pickett, J. A. (2009). Evaluation of farmers' field days as a dissemination tool for push–pull technology in Western Kenya. Crop Protection, 28(3), 225–235.

    Article  Google Scholar 

  • Appleton, S. (2009). Uganda's poverty line - a review, DFID Technical Report: Uganda on Poverty Line Methodology and Trends.

  • Bahiigwa, G. (1999). Household food security in Uganda: An empirical analysis (No. 25). Kampala: Economic Policy Research Center.

    Google Scholar 

  • Bahiigwa, G. B. (2004). Rural household food security in Uganda: An empirical analysis. Eastern Africa Journal of Rural Development, 18(1), 8–22.

    Article  Google Scholar 

  • Bia, M., & Mattei, A. (2008). A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score. The Stata Journal, 8(3), 354–373.

    Google Scholar 

  • Chmelarova, V. (2007). The Hausman Test, and Some Alternatives, with Heteroskedastic Data. USA: Louisiana State University Agricultural & Mechanical College.

    Google Scholar 

  • Conley, T. G., & Taber, C. R. (2011). Inference with “difference in differences” with a small number of policy changes. The Review of Economics and Statistics, 93(1), 113–125.

    Article  Google Scholar 

  • De Groote, H. (2002). Maize yield losses from stemborers in Kenya. International Journal of Tropical Insect Science, 22(2), 89–96.

    Article  Google Scholar 

  • Fischler, M. (2010). Impact assessment of push–pull technology developed and promoted by icipe and partners in eastern Africa. Nairobi: Icipe Science Press.

    Google Scholar 

  • FAO. (2012). Food security statistics. Rome. https://www.fao.org/economic/ess/food-securitystatistics/en/. Accessed December 2015.

  • FAO. (2015). The impact of natural hazards and disasters on agriculture and food and nutrition security: A call for action to build resilient livelihoods. Rome.

  • Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica: Journal of the Econometric Society, 52(3), 761–766.

    Article  Google Scholar 

  • Gatsby Charitable Foundation. (2005). The quiet revolution: Push-pull technology and African farmer. Gatsby Occasional Paper. London: The Gatsby Charitable Foundation.

  • Gatsby Charitable Foundation. (2011). Planting for prosperity. Push–pull: A model for Africa’s green revolution. Gatsby Occasional Paper. India: The Gatsby Charitable Foundation, Pragati Offset Pvt. Ltd.

  • Guardabascio, B., & Ventura, M. (2013). Estimating the dose-response function through the GLM approach. (No. 45013). Germany: University Library of Munich.

    Google Scholar 

  • Guido, W. I. (2004). Nonparametric estimation of average treatment effects under exogeneity: a review. Review of Economics and Statistics, 86(1), 4–29.

    Article  Google Scholar 

  • Hassan, R. M., Onyango, R., & Rutto, J. K. (1994). Adoption Patterns and Performance of Improved Maize in Kenya. In R. M. Hassan (Ed.), Maize Technology Development and Transfer: A GIS Approach to Research Planning in Kenya (pp. 21–54). London: CAB International.

    Google Scholar 

  • Heckman, J. J., & Vytlacil, E. (2005). Structural equations, treatment effects, and econometric policy evaluation. Econometrica, 73(3), 669–738.

    Article  Google Scholar 

  • Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. The Review of Economic Studies, 65(2), 261–294.

    Article  Google Scholar 

  • Hirano, K., & Imbens, G. W. (2004). The propensity score with continuous treatments. Applied Bayesian modeling and causal inference from incomplete-data perspectives, 226164, 73-84.

  • Hooper, A. M., Hassanali, A., Chamberlain, K., Khan, Z., & Pickett, J. A. (2009). New genetic opportunities from legume intercrops for controlling Striga spp. parasitic weeds. Pest Management Science, 65(5), 546–552.

    Article  CAS  Google Scholar 

  • Hooper, A. M., Tsanuo, M. K., Chamberlain, K., Tittcomb, K., Scholes, J., Hassanali, A., & Pickett, J. A. (2010). Isoschaftoside, a C-glycosylflavonoid from Desmodium uncinatum root exudate, is an allelochemical against the development of Striga. Phytochemistry, 71(8), 904–908.

    Article  CAS  Google Scholar 

  • Imai, K., & Van Dyk, D. A. (2004). Causal inference with general treatment regimes. Journal of the American Statistical Association, 99(467), 854–866.

    Article  Google Scholar 

  • Imbens, G. W., & Wooldridge, J. M. (2009). Recent developments in the econometrics of program evaluation. Journal of Economic Literature, 47(1), 5–86.

    Article  Google Scholar 

  • Imbens, G. W., & Angrist, J. D. (1994). Identification and estimation of Local Average Treatment Effects. Econometrica, 62(2), 467–476.

    Article  Google Scholar 

  • Kassie, M., Shiferaw, B., & Muricho, G. (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development, 39(10), 1784–1795.

    Article  Google Scholar 

  • Kassie, M., Jaleta, M., Shiferaw, B. A., Mmbando, F., & De Groote, H. (2012). Improved maize technologies and welfare outcomes in smallholder systems: Evidence from application of parametric and non-parametric approaches. In 2012 Conference, August 18–24, 2012, Foz do Iguacu, Brazil (No. 128004). International Association of Agricultural Economists.

  • Kassie, M., Jaleta, M., & Mattei, A. (2014). Evaluating the impact of improved maize varieties on food security in Rural Tanzania: Evidence from a continuous treatment approach. Food Security, 6(2), 217–230.

    Article  Google Scholar 

  • Kfir, R., Overholt, W. A., Khan, Z. R., & Polaszek, A. (2002). Biology and management of economically important lepidopteran cereal stemborers in Africa. Annual Review of Entomology, 47, 701–731.

    Article  CAS  Google Scholar 

  • Khan, Z. R., Overholt, W. A., & Hassana, A. (1997). Utilization Of Agricultural Biodiversity For Management Of Cereal Stemborers And Striga Weed In Maize-Based Cropping Systems In Africa—A Case Study. UK and USA: CABI Publishing.

    Google Scholar 

  • Khan, Z. R., Pickett, J. A., Wadhams, L. J., & Muyekho, F. (2001). Habitat management strategies for the control of cereal stemborers and Striga weed in maize in Kenya. Insect Science Applications, 21(4), 375–380.

    Google Scholar 

  • Khan, Z., Amudavi, D., & Pickett, J. (2008a). Push-pull technology transforms small farms in Kenya. Pesticide Action Network North America Magazine, Spring. 

  • Khan, Z. R., Midega, C. A. O., Amudavi, D. M., Hassanali, A., & Pickett, J. A. (2008b). On- farm evaluation of the ‘Push–Pull’ Technology for the control of stemborers and Striga weed on maize in Western Kenya. Field Crops Research, 106(3), 224–233.

    Article  Google Scholar 

  • Khan, Z. R., Midega, C. A. O., Njuguna, E. M., Amudavi, D. M., Wanyama, J. M., & Pickett, J. A. (2008c). Economic performance of the Push-Pull Technology for stemborer and Striga control in smallholder farming systems in Western Kenya. Crop Protection, 27(7), 1084–1097.

    Article  Google Scholar 

  • Khan, Z., Midega, C., Pittchar, J., Pickett, J., & Bruce, T. (2011). Push—pull technology: A conservation agriculture approach for integrated management of insect pests, weeds and soil health in Africa: UK government's Foresight Food and Farming Futures project. International Journal of Agricultural Sustainability, 9(1), 162–170.

    Article  Google Scholar 

  • Khan, Z. R., Midega, C. A. O., Pittchar, J. O., Murage, A. W., Birkett, M. A., Toby, J. A., Bruce, T. J. A., & Pickett, J. A. (2014). Achieving food security for one million Sub-Saharan African poor through Push-Pull innovation by 2020. Philosophical Transactions of the Royal Society, 369(1639), 20120284.

    Article  Google Scholar 

  • Kim, S. K. (1991). Combating striga in Africa. Ibadan: International Institute of Tropical Agriculture.

    Google Scholar 

  • Kluve, J., Schneider, H., Uhlendorff, A., & Zhao, Z. (2012). Evaluating continuous training programmes by using the generalized propensity score. Journal of the Royal Statistical Society: Series A (Statistics in Society), 175(2), 587–617.

    Article  Google Scholar 

  • Kreif, N., Grieve, R., Díaz, I., & Harrison, D. (2015). Evaluation of the effect of a continuous treatment: A machine learning approach with an application to treatment for traumatic brain injury. Health Economics, 24(9), 1213–1228.

    Article  Google Scholar 

  • Liu, J., & Florax, R. (2014). The effectiveness of international aid: A generalized propensity score analysis. In 2014 Annual Meeting, July 27–29, 2014, Minneapolis, Minnesota, USA (No. 169804). Agricultural and Applied Economics Association.

  • Mellor, J. W. (1966). The economics of agricultural development. The economics of agricultural development. Ithaca: Cornell University Press.

    Google Scholar 

  • Midega, C. A., Khan, Z. R., Amudavi, D. M., Pittchar, J., & Pickett, J. A. (2010). Integrated management of Striga hermonthica and cereal stemborers in finger millet (Eleusine coracana (L.) Gaertn.) through intercropping with Desmodium intortum. International Journal of Pest Management, 56(2), 145–151.

    Article  Google Scholar 

  • Midega, C. A. O., Bruce, T. J. A., Pickett, J. A., Pittchar, J. O., Murage, A., & Khan, Z. R. (2015). Climate-adapted companion cropping increases agricultural productivity in East Africa. Field Crops Research, 180, 118–125.

    Article  Google Scholar 

  • Ministry of Agriculture, Animal Industry and Fisheries. (2004). Uganda food and nutrition strategy and investment plan. Kampala: The Republic of Uganda.

    Google Scholar 

  • Mukhebi, A., Mbogoh, S., & Matungulu, K. (2011). An overview of the food security situation in eastern Africa. Kigali: Economic commission for Africa sub-regional office for eastern Africa.

    Google Scholar 

  • Murage, A. W., Amudavi, D. M., Obare, G., Chianu, J., Midega, C. A. O., Pickett, J. A., & Khan, Z. R. (2011). Determining smallholder farmers' preferences for technology dissemination pathways: the case of 'Push–Pull' technology in the control of stemborer and Striga weeds in Kenya. International Journal of Pest Management, 57(2), 133–145.

    Article  Google Scholar 

  • Murage, A. W., Obare, G., Chianu, J., Amudavi, D. M., Midega, C. A. O., Pickett, J. A., & Khan, Z. R. (2012). The effectiveness of dissemination pathways on adoption of "Push-Pull" technology in Western Kenya. Quarterly Journal of International Agriculture, 51(1), 51.

    Google Scholar 

  • Murage, A. W., Midega, C. A. O., Pittchar, J. O., Pickett, J. A., & Khan, Z. R. (2015a). Determinants of adoption of climate-smart push-pull technology for enhanced food security through integrated pest management in eastern Africa. Food Security, 7(3), 709–724.

    Article  Google Scholar 

  • Murage, A. W., Pittchar, J. O., Midega, C. A. O., Onyango, C. O., & Khan, Z. R. (2015b). Gender specific perceptions and adoption of the climate-smart push–pull technology in eastern Africa. Crop Protection, 76, 83–91.

    Article  Google Scholar 

  • Musselman, L. J., Safa, S. B., Knepper, D. A., Mohamed, K. I., White, C. L., & Kim, S. K. (1991). Recent research on the biology of Striga asiatica, S. gesnerioides and S. hermonthica. In Combating striga in Africa: Proceedings of the international workshop held in Ibadan, Nigeria, 22–24 August 1988 (pp. 31–41). Nigeria: International Institute of Tropical Agriculture.

  • Nabasirye, M., Kiiza, B., & Omiat, G. (2012). Evaluating the impact of adoption of improved maize varieties on yield in Uganda: A Propensity Score matching approach. Journal of Agricultural Science and Technology B, 2, 368–377.

    Google Scholar 

  • Nambafu, G. N., Onwonga, R. N., Karuku, G. N., Ariga, E. S., Vanlauwe, B., & da Nowina, K. R. (2014). Knowledge, attitude and practices used in the control of Striga in maize by smallholder farmers of Western Kenya. Journal of Agricultural Science and Technology B, p.237 

  • Nguezet, P. D., Diagne, A., Okoruwa, V. O., Ojehomon, V., & Manyong, V. (2011). Impact of improved rice technology (NERICA varieties) on income and poverty among rice farming households in Nigeria: a local average treatment effect (LATE) approach. Quarterly Journal of International Agriculture, 50(3), 267–292.

  • Otte, M. J., & Chilonda, P. (2002). Cattle and small ruminant production systems in sub-Saharan Africa. A systematic review. Rome: Food and Agriculture Organization of the United Nations.

    Google Scholar 

  • Ouma, J., Bett, E., & Mbataru, P. (2014). Does adoption of improved maize varieties enhance household food security in maize growing zones of eastern Kenya. Developing Country Studies, 4(23), 157–165.

    Google Scholar 

  • Ravallion, M., & Bidani, B. (1994). How robust is a poverty profile? The world bank economic review, 8(1), 75–102.

    Article  Google Scholar 

  • Romney, D. L., Thorne, P., Lukuyu, B., & Thornton, P. K. (2003). Maize as food and feed in intensive smallholder systems: management options for improved integration in mixed farming systems of east and southern Africa. Field Crops Research, 84, 159–168.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  • Rosenbaum, P. R. (2002). Observational Studies. New York: Springer.

    Book  Google Scholar 

  • Rubin, D. B. (2005). Causal inference using potential outcomes. Journal of the American Statistical Association, 100(469), 322–331.

    Article  CAS  Google Scholar 

  • Salami, A., Kamara, A. B., & Brixiova, Z. (2010). Smallholder agriculture in East Africa: trends, constraints and opportunities. Tunis: African Development Bank.

    Google Scholar 

  • Salmen, L. F. (1995). Beneficiary assessment: an approach described (No. 23). Washington: Environment Department, World Bank.

    Google Scholar 

  • Simtowe, F., Kassie, M., Asfaw, S., Shiferaw, B., Monyo, E., & Siambi, M. (2012). Welfare effects of agricultural technology adoption: The case of improved groundnut varieties in rural Malawi. In Selected Paper prepared for presentation at the International Association of Agricultural Economists (IAAE) Triennial Conference, Foz do Iguaçu, Brazil (pp. 18–24).

  • Smil, V. (2000). Feeding the World: A Challenge for the Twenty-First Century. Cambridge: MIT Press.

    Google Scholar 

  • Ssewanyana, S., & Kasirye, I. (2010). Food insecurity in Uganda: A dilemma to achieving the hunger millennium development goal. Economic Policy Research Series No. 67.

  • Ssewanyana, S. N., & Kasirye, I. (2013). The dynamics of income poverty in Uganda: Insights from the Uganda National Panel Surveys of 2009/10 and 2010/11 (No. 206188). Kampala: Economic Policy Research Centre (EPRC).

    Google Scholar 

  • Tsanuo, M. K., Hassanali, A., Hooper, A. M., Khan, Z., Kaberia, F., Pickett, J. A., & Wadhams, L. J. (2003). Isoflavanones from the allelopathic aqueous root exudate of Desmodium uncinatum. Phytochemistry, 64(1), 265–273.

    Article  CAS  Google Scholar 

  • Turyahabwe, N., Tumusiime, D. M., Kakuru, W., & Barasa, B. (2013). Wetland use/cover changes and local perceptions in Uganda. Sustainable Agriculture Research, 2(4), 95.

    Article  Google Scholar 

  • Wiebe, K.D., Soule, M.J. & Schimmelpfennig, D., (2001). Agricultural productivity for sustainable food security in Sub-Saharan Africa. In L. Zepeda (Ed.), Agricultural Investment and Productivity in Developing Countries. Rome: FAO.

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Correspondence to Ruth T. Chepchirchir.

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Chepchirchir, R.T., Macharia, I., Murage, A.W. et al. Impact assessment of push-pull pest management on incomes, productivity and poverty among smallholder households in Eastern Uganda. Food Sec. 9, 1359–1372 (2017). https://doi.org/10.1007/s12571-017-0730-y

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