What happens between technico-institutional support and adoption of organic farming? A case study from Benin

Moumouni, Ismaïl; Baco, Mohamed Nasser; Tovignan, Silvère; Gbèdo, Florent; Nouatin, Guy Sourou; Vodouhè, Simplice D.; Liebe, Ulf

doi:10.1007/s13165-013-0039-x

Cited by 17 publications

(24 citation statements)

References 23 publications

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“…There are several models or paradigms employed in the literature to explain the decision of smallholder farmers to adopt new technologies (Negatu and Parikh 1999;Moumouni et al 2013). We identified here four models: (1) the technology diffusion model that considers the characteristics of technology as determinants of adoption (e.g., Sarker and Itohara 2008); (2) the economic constraints model, which emphasizes the importance of economic and institutional factors in the adoption of technology (e.g., Bolwig et al 2009); (3) the technology user's context model that posits that farmers' characteristics and subjective perceptions of technology influence adoption (e.g., Sarker and Itohara 2008) and (4) the integrated model that combines the features of the three above models.…”

Section: Hypothesized Factors Influencing Organic Cotton Adoptionmentioning

confidence: 99%

“…Thus, beyond the poverty level, there are other factors that lead farmers to become organic cotton producer. For example, Moumouni et al (2013) argued that beyond profitability, attitudinal aspects and social dynamics (in the household and the society) also influence producers' decisions to adopt organic farming methods. Although poor and rich farmers have statistically the same likelihood to adopt the Table 7 Determinants of organic cotton adoption: results of the probit model for women and men…”

Section: Farmers' Characteristics and Organic Cotton Adoptionmentioning

confidence: 99%

“…Basically, organic farming is 'knowledge intensive' and requires the mobilization and building of effective socio-technical networks allowing interactive and social learning among peer organic farmers, extension and advisory services, and even research organizations (van Elzakker 1999;Glin et al 2012;Moumouni et al 2013;Nicolay and Baker 2012). Besides, the social control and internal control systems that are at the heart of the certification mechanism of organic farming require collective action to control the traceability of materials and resources used (sprays equipments, organic inputs, etc.)…”

Section: Effect Of Institutional and Economic Factors On Organic Cottmentioning

confidence: 99%

“…subsidies, poverty alleviation, and sustainable development (Moseley and Gray 2008). In particular, Benin is among the countries most dependent on cotton (Gergely 2009).…”

mentioning

confidence: 99%

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Socioeconomic determinants of organic cotton adoption in Benin, West Africa

Sodjinou

Glin²,

Nicolay

et al. 2015

Agric Econ

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Organic cotton relies on ecological processes and the use of natural resources to sustain the production system, unlike conventional cotton, mainly characterized by massive utilization of synthesis chemicals. In West Africa, where rural livelihoods are particularly vulnerable, organic cotton is expected to contribute not only to poverty reduction but also to strengthen households' resilience. The objective of this study was to assess institutional and socioeconomic factors determining farmers' decisions to adopt organic cotton. For this purpose, we applied a probit model on empirical data collected from producers of the Centre and the Northern parts of Benin. Overall, we found that organic cotton adoption is mainly determined by farmers' socioeconomic characteristics, the physical distance between farm and house, and contact with extension and advisory services. Organic farming is more attractive to women compared to conventional farming. This because such type of cotton farming enables women to hold a separate cotton farm and thus increase their economic independence, whereas with the conventional system they depend mainly on the farm of the (male) head of the household. Older, less educated and low-income farmers who express environmental concern are more likely to adopt organic cotton. Subsequently, organic cotton should be considered as a prospective policy option to reach the poor and strengthen their livelihoods conditions while contributing to preserve the environment and natural resources. Furthermore, farmers who have their farm near home are more likely to adopt organic farming than those who have the farm far from their home. It also came out that organic farmers have more contacts with advisory and extension services. Finally, the study noted that there is still a need to enhance the extension system by: (1) exploring, designing, and upgrading innovative pedagogic tools such as videos and mobile phone technology to foster learning; and (2) strengthening organic farmer's organizations and the linkage with agricultural research organizations for technology development.

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Section: Hypothesized Factors Influencing Organic Cotton Adoptionmentioning

confidence: 99%

Section: Farmers' Characteristics and Organic Cotton Adoptionmentioning

confidence: 99%

Section: Effect Of Institutional and Economic Factors On Organic Cottmentioning

confidence: 99%

“…subsidies, poverty alleviation, and sustainable development (Moseley and Gray 2008). In particular, Benin is among the countries most dependent on cotton (Gergely 2009).…”

mentioning

confidence: 99%

See 2 more Smart Citations

Socioeconomic determinants of organic cotton adoption in Benin, West Africa

Sodjinou

Glin²,

Nicolay

et al. 2015

Agric Econ

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“…Una serie de tiempo es un conjunto de números que mide el estado de alguna actividad a lo largo del tiempo. Según [21] es el registro histórico de alguna actividad, con mediciones tomadas a intervalos igualmente espaciados con consistencia en la actividad y el método de medición.…”

Section: Cprocesos Autorregresivosunclassified

Análisis univariante para describir y pronosticar la producción de plátano en la región de piura

Choque¹,

Yarleque²,

Castro

2021

uct

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La actividad agrícola en la región de Piura, es una actividad fundamental para su desarrollo, la implementación de pronósticos es una herramienta útil para los agentes económicos para una planificación y toma de decisiones acertadas. En el estudio interesan dos resultados, el primero identificar, estimar y validar un modelo ajustado para pronosticar la producción de plátano y el segundo realizar el pronóstico de la producción de plátano para el periodo de octubre de 2020 hasta octubre de 2022. Para concretizar los objetivos se realizó el análisis univariante con la metodología de Box y Jenkins. Los datos provienen del Banco Central de Reserva del Perú, se consideraron datos mensuales desde julio de 2000 hasta septiembre de 2020. Luego del cumplimiento de los supuestos, el mejor modelo ajustado para representar la producción del plátano y realizar pronósticos es un modelo autorregresivo integrado de promedio móvil o ARIMA. El pronóstico de la producción del plátano tiene una tendencia decreciente para los próximos años. Palabras Clave: Pronostico, Series de tiempo, Modelos ARIMA, Producción agrícola. Referencias [1]A. A. S. Syed, A. Sajad, y U. J. Arshad, “Growth, Variability and Forecasting of Wheat and Sugarcane Production in Khyber Pakhtunkhwa, Pakistan,” Agric. Res. Technol. Open Access J., 2018. [2]Instituo Nacional de Estadistica e Informatica, “Producción Nacional - INEI,” 2019. [3]M. Laberry, “III Foro Nacional del Cultivo de Arroz,” 2016. [4]L. Torres, “Análisis Económico del Cambio Climático en la Agricultura de la Región Piura. Caso: Principales Productos Agroexportables,” Consorc. Investig. Econ. y Soc. - CIES, 2010. [5]Instituto Nacional de Estadistica e Informatica, “Producto Bruto Interno Por Departamentos,” 2019. [6]D. Llico, “La minería, pesca y agricultura de Piura,” monografias.com, 2013. [7]H. Moyazzem, A. Faruq, y K. Ajit, “Forecasting of Banana Production in Bangladesh,” Am. J. Agric. Biol. Sci., 2016. [8]J. Ruiz, G. Hernández, y R. Zulueta, “Análisis de series de tiempo en el pronóstico de la producción de caña de azúcar,” Fac. Econ. - Univ. Veracruzana - Mex., 2010. [9]V. Erossa, Proyectos de inversión en ingeniería: su metodología. 2004. [10]A. Contreras, C. Atziry, M. José, y S. Diana, “Análisis de series de tiempo en el pronóstico de la demanda de almacenamiento de productos perecederos,” Estud. Gerenciales 32 p.387-396 - Mex., 2016. [11]G. Mendoza, “Pronosticar y métodos de pronóstico.,” 2003. [12]A. Muñoz y F. Parra, Econometria aplicada, Ediciones. 2007. [13]M. A. Hamjah, “Forecasting major fruit crops productions in Bangladesh using Box-Jenkins ARIMA model.,” J. Econ. Sustain., vol. Dev., 5: 9, 2014. [14]M. Casinillo y I. Manching, “Modeling the monthly production of banana using the box and Jenkins analysis.,” Am. J. Agric. Biol. Sci., 2016. [15]N. Suleman y S. Sarpong, “Forecasting Milled Rice Production in Ghana Using Box- Jenkins Approach,” Int. J. Agric. Manag. Dev. (IJAMAD)., 2011. [16]W. Merlin, “Modelo univariante de pronóstico del número de unidades de transfusión de sangre en el hospital regional Manuel Nuñez Butrón - Puno periodo 2006- 2015-I,” Universidad Nacional del Altiplano - Puno, 2015. [17]L. Laurente, “Proyección de la producción de papa en puno. una aplicación de la metodología de Box-Jenkins,” Semest. Econ. - FIE - UNA Puno, 2018.[18]Banco Central de Reserva del Perú, “Gerencia Central de Estudios Económicos,” 2019. [Online]. Available: https://estadisticas.bcrp.gob.pe/estadisticas/series/mensuales/resultados/PN01784AM/html. [19]R. Hernández, C. Fernández, y M. del P. Baptista, Metodologia de la Investigación, vol. 6ta Ed. 2014. [20]Banco Central de Reserva del Perú, “PIURA: Síntesis de Actividad Económica.” 2020, [Online]. Available: https://www.bcrp.gob.pe/estadisticas/informacion-regional/piura/piura.html. [21]I. Moumouni et al., “What happens between technico-institutional support and adoption of organic farming? A case study from Benin,” Org. Agric., p. DOI 10.1007/s13165-013-0039-x., 2013. [22]U. Yule, “On a Method of Investigating Periodicities in Disturbed Series, with Special Reference to Wolfer’s Sunspot Numbers,” Philos. Trans. R. Soc. London, 1926. [23]E. Slutsky, “The Summation of Random Causes as the Source of Cyclical Processes,” Econom. 4 105-46, 1937., 1927. [24]H. Wold, “A Study of the Analysis of Stationary Time Serie,” Uppsala: Almqvist and Wiksells., vol. 2nd ed.-19, 1938. [25]G. Box y G. M. Jenkins, “Time Series Analysis, Forecasting and Control,” San Fr. Holden- Day, California, USA., 1976. [26]D. Gujarati y D. Porter, Econometría. 2010. [27]G. Box y D. Pierce, “Distribution of Residual Autocorrelations in Autoregressive Integrated Moving Average Time Series Models,” J. Am. Stat. Assoc., vol. 65, p, 1970. [28]G. Ljung y G. Box, “On a measure of lack of fit in time series models.,” Biometrika, vol. V65: 297-3, 1978. [29]C. Jarque y A. Bera, “A Test for Normality of Observations and Regression Residuals,” Int. Stat. Inst., vol. Vol. 55, N, 1978. [30]D. A. Dickey y W. A. Fuller, “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” J. Am. Stat. Assoc., vol. 74, p, 1979. [31]P. C. B. Phillips y P. Perron, “Testing for a Unit Root in Time Series Regression,” Biometrika, vol. 75, pp. 335–346,1988.

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Participatory Causal Loop Mapping of the Adoption of Organic Farming in Nigeria

Adebiyi

Olabisi

2022

Environmental Management

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What happens between technico-institutional support and adoption of organic farming? A case study from Benin

Cited by 17 publications

References 23 publications

Socioeconomic determinants of organic cotton adoption in Benin, West Africa

Socioeconomic determinants of organic cotton adoption in Benin, West Africa

Análisis univariante para describir y pronosticar la producción de plátano en la región de piura

Participatory Causal Loop Mapping of the Adoption of Organic Farming in Nigeria

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