Adoption of soil organic carbon-enhancing practices: A case of two watershed sites in Ethiopia.

This study aimed at identifying the factors that determine the decision to adopt and the intensity of adoption of soil organic carbon (SOC)-enhancing practices using two watershed sites in Ethiopia: Yiser (Amhara region) and Azugashube (Southern region). The study used survey data collected from 379 sample households drawn from four Kebele/village administrations at each watershed site. Multivariate and ordinary least squares regressions were used to identify the factors that determine the decision to adopt the SOC-enhancing practices and the factors that determine the extent of adoption of these practices, respectively. The study classified these various practices into three classes: soil and water conservation, agronomic, and agroforestry SOC-enhancing practices. We find that the decision to adopt soil and water conservation practices is negatively related to both the decision to adopt agronomic and to adopt agroforestry SOC-enhancing practices. On the contrary, we find that the decision to adopt agronomic and agroforestry practices is complementary. The study also identified diverse agroecological, farming system, institutional, and household characteristics that determine the decision to adopt and the intensity of adoption of the three SOCenhancing practices. Among the different variables, the study found location as a strong determinant of the type and intensity of adoption of the SOC practices

Adoption Technology Targets and Knowledge Dynamics: Consequences for Long-Run Prospects

When targeting frontier technologies, less developed economies usually face obstacles to achieve high growth in the long run, because of their low level of knowledge relative to the adoption technology target. If the intensity in which the adoption activity uses knowledge is high, then the less developed economy may end up trapped in a low growth equilibrium. We show that in this case it is beneficial to target less advanced technologies, which helps to compensate the scarcity of knowledge during the transition. Nevertheless, polarization is possible. If knowledge intensity in the adoption activity is low, then possessing a low stock of knowledge allows targeting the technology frontier even in a poor R&D environment. In this case, all economies achieve a high growth equilibrium in which only income level differences persist in the long run.R&D, adoption, innovation, growth, development, transitional dynamics

Soil Fertility Management Choice in the Maize-Based Smallholder Farming System in Malawi

The paper analyses the factors that affect smallholder farmers choice of soil fertility management options in Malawi using a two-stage maximum likelihood estimation procedure. Using results from the Double-Hurdle model, the paper estimates the probabilities and intensities of fertilizer application conditional on choice of inorganic fertilizer. The findings indicate that relative wealthy indicators, human capital, credit and market access, food security index and land pressure are the main factors that greatly influence farmers choice and intensity of input investment. Although there is a high and positive correlation between probability of adoption and intensity of application, factors that influence adoption are not necessarily the same as those that influence the intensity of application, conditional on adoption. The paper concludes with policy and research implications aimed at informing the debate on enhancing sustainable soil fertility management among smallholder farmers in Malawi.soil fertility management, smallholder farmers, Double-Hurdle model, Malawi, Resource /Energy Economics and Policy,

Factors that affect the use of herbicides in Philippine rice farming systems

This study involves the application of a random-effects double-hurdle model to survey data to identify the farm-level factors affecting the adoption and intensity of herbicide use in rice production in the Philippines. Results broadly indicate apparent differences in the degree to which important explanatory variables affect the intensity and adoption decisions. The age of the farmer, household size, and irrigation are the significant predictors influencing the decision of farmers to use herbicides, while economic variables such as the price of herbicides, total income of farmers, and the use of bank loans or credit are the highly significant factors determining the intensity of herbicide use. Significant determinants of both the adoption and intensity decisions are land ownership, farm area, and the method of crop establishment used. Results suggest that all of the identified significant predictors in both herbicide use decisions can be considered by the national government when designing policies to reduce excessive use of herbicides or to encourage the adoption of alternative methods of weed control. This is important because for small rice producers, like the majority of Filipino farmers, improved weed management techniques that build on their traditional practices and that are compatible with their resources will be more easily adopted by farmers, relative to those that require radical change to the entire farming system.Herbicide use, Double-hurdle model, Adoption, Rice farming system., Demand and Price Analysis,

Does “Convenience Agriculture” Affect Off‐farm Labor Allocation Decisions?

The objective of this study is to examine the effect of adoption intensity of GM crops on off‐farm labor supply by farm households. Using ARMS data in 2004, 2005 and 2006, we estimate a two stage simultaneous Tobit model and find that adoption intensity of GM crops has a negative impact on off‐farm labor supply by operators and a positive impact on off‐farm labor supply by spouse. This may be due to the comparative advantage of operators and spouses. Our results find that GM crops adoption has different but significant implications on off‐farm labor supply by operators and spouses and underscores the importance of understanding farm households’ decisions to explain behaviors of farm businesses in the United States.Technology Adoption, Two stage simultaneous Tobit model, GM Crops, Off‐farm labor, Agricultural and Food Policy, Labor and Human Capital, Q10, Q12,

Can Risk-aversion towards fertilizer explain part of the non-adoption puzzle for hybrid maize? Empirical evidence from Malawi

This study investigates the linkage between attitudes towards risk and adoption. We empirically examine the relative risk premium related to fertilizer-use among 404 farmers from Malawi and examine the relationship between risk aversion on fertilizer-use and the adoption of hybrid maize. Results show that Malawian farmers exhibit absolute Arrow-Pratt risk aversion towards the use of fertilizer. The findings also reveal that risk aversion towards the use of fertilizer is strongly associated with low intensity of hybrid maize adoption and that other than the safety net programs, human and financial capital variables such as age, household size, land size and off-farm income can be helpful in explaining the non-adoption puzzle. While safety net programs such as the free input distribution increase the likelihood of adoption, they are associated with low adoption intensity for hybrid maize. A key lesson is that when considering promoting a technology, it is important to assess the profit distribution associated with the use of complementary inputs and its implications for risk preference among technology users in order to avoid formulating misguiding policies.Adoption; hybrid maize; fertilizer; risk-aversion; Malawi

The effect of climate change adaptation strategies on bean yield in central and northern Uganda

This paper analyses the impact of adaptation to climate change on bean productivity on a micro-scale using instrumental variable techniques in a two-stage econometric model, using data collected from farming households in northern and central Uganda. We employed the bivariate probit technique to model simultaneous and interdependent adoption decisions, and the ordered probit to model the intensity of adaptation. We modelled the impact of adaptation using instrumental variables and the control function approach because of the potential endogeneity of the adaptation decision. The driving forces behind adoption of the two selected adaptation strategies were heterogeneous. Location-specific factors influenced the intensity of adaptation between the two study regions. The effect of adaptation was stronger for households that used a higher number of strategies, evidence that the two adaptation strategies need to be used simultaneously by farmers to maximise the positive impact of adaptation

Determinants of Improved Maize Seed and Fertilizer Use in Kenya: Policy Implications

Maize is a key food crop in Kenya. While maize yields increased from 1.25 t ha-1 in early 1960s to over 2 tonnes in 1982, they fell below 1.5 t ha-1 in 2000. Given the limited land area, there is no doubt that Kenya will have to rely more on modern technologies for increased yields .Use of improved maize varieties and fertilizers will therefore continue to be critical inputs for improving productivity. To improve production, it is important to understand factors determining adoption and intensity of use of modern technologies. A stratified 2-stage sampling design was used to select 1800 households, subsequently interviewed by means of structured questionnaire. Econometric models were used to explore factors influencing adoption and intensity of use of the improved varieties and fertilizer. Access to credit was positively related to adoption and intensity of use of the two inputs. Extension contacts positively influenced the likelihood of adoption of improved maize seed, while amount of planting fertilizer used positively influenced both the adoption and intensity of use of improved varieties. Distance to market negatively determined the adoption and intensity of use of fertilizer. In addition gender and access to hired labour had negative impacts on the intensity of use of fertilizer. There is need to think of alternative sources of credit to farmers and also revamp the existing extension service (including privatization in the long term) for efficient delivery of information.Maize, adoption, improved seed, fertilizer, credit, extension, Kenya, Crop Production/Industries,

Accounting for Neighborhood Influence in Estimating Factors Determining the Adoption of Improved Agricultural Technologies

Researchers have traditionally applied censored regression models to estimate factors influencing farmers' decisions to adopt improved technologies for the design of appropriate intervention strategies. The standard Tobit model, commonly used, assumes spatial homogeneity implicitly but the potential for the presence of spatial heterogeneity (spatial autocorrelation or dependence) is high due to neighborhood influence among farmers. Ignoring spatial autocorrelation (if it exists) would result in biased estimates and all inferences based on the model will be incorrect. On the other hand, if spatial dependence is ignored the regression estimates would be inefficient and inferences based on t and F statistics misleading. To account for neighborhood influence, this study applied a spatial Tobit model to assess the factors determining the adoption of improved maize varieties in southern Africa using data collected from 300 randomly selected farm households in the Manica, Sussundenga and Chokwe districts of Mozambique during the 2003/04 crop season. Model diagnosis confirmed the spatial Tobit model as a better fit than the standard Tobit model. The estimated results suggest that farm size, access to credit, yield and cost of seed significantly influence maize variety adoption at less than 1% error probability while age of household head and distance to market influence adoption decisions at 5% error probability. The marginal effect analysis showed that convincing farmers that a given improved maize variety would give a unit more yield than the local one would increase adoption rate by 18% and intensity of use by 10%. Given that improved maize seeds are relatively more expensive than local ones, making credit accessible to farmers would increase adoption and intensity of use of improved maize varieties by 24% (15% being the probability of adoption and 8% the intensity of 2 use of the varieties). On the other hand, increasing seed price by a unit over the local variety would decrease the adoption rate by 12% and area under the improved variety by 6%. Targeting younger farmers with extension messages or making markets accessible to farmers would marginally increase the adoption and use intensity of improved maize varieties by only 0.4%. These results suggest that increasing field demonstrations to show farmers the yield advantage of improved varieties over local ones in Mozambique are essential in improving the uptake of improved varieties, which may be enhanced by making credit available to farmers to address the high improved seed costs. Alternatively, assuring farmers of competitive output markets through marketing innovations would enhance improved maize variety adoptions decisions. It may be concluded that the significance of the paper is its demonstration of the need to include spatial dependency in technology adoption models where neighborhood influences are suspected. Such an approach would give more credence to the results and limit the errors in suggesting areas to emphasize in individual or group targeting. The results thus have implications beyond the study area. Furthermore, the paper contributes to the scanty literature on the application of spatial econometrics in agricultural technology adoption modeling.Farm Management,

BIORATIONAL INSECTICIDE ADOPTION AND CONVENTIONAL INSECTICIDE USE: A SIMULTANEOUS, LIMITED DEPENDENT VARIABLE MODEL

Using data reporting section-level pesticide use for all of Arizona, this study estimates how early-season adoption of new biorational insecticides reduced subsequent broad-spectrum insecticide applications in cotton. The two-stage econometric model accounts for the endogeneity and censoring of the adoption intensity variable. One biorational application substituted for 3.66 broad-spectrum applications.Crop Production/Industries,