Selection of the study area

This study was conducted in Khyber Pakhtunkhwa province of Pakistan. The total length of the province is 408 miles between the parallels and the total width between the meridians is 279 miles. Geotropically, it is located between 31^o 15’ and 36^o 57’ North latitude and 69^o 5’ and 74^o 7’ East longitude (^{KHAN, 2012}). The total province area is 10.17 million hectares which as 12.8% of the total area of the country and situated at the junction of Himalaya, Karakorum, and Hindukush mountain ranges (^{KHAN, 2012}). Figure 1 shows the selected study districts in Khyber Pakhtunkhwa, Pakistan. It is important to note that the peach crop and study area selection in this research was due to two main factors. Firstly, as mentioned before in introduction section that Khyber Pakhtunkhwa province is blessed with a wide range of fruits and is the largest producer of delicious fruits in Pakistan; secondly, peach is the leading fruit crop among the fruit crops/orchards of all categories in this province.

Figure 1 Selected study districts in Khyber Pakhtunkhwa, Pakistan. 

Sampling technique and data samples

The data was collected using a multi-stage sampling design. In the first stage, the northern region of Khyber Pakhtunkhwa province (traditional peach producing belt) was selected. In the second stage, three districts namely Peshawar, Nowshera, and Swat were selected purposely according to their relative importance in fruit production. In the third stage, main towns and union councils were selected from purposively-selected districts. Finally, a total number of 21 villages were selected based on more peach fruit plants, a large number of practicing peach growers and orchard owners in these villages. Direct elicitation method was employed for data collection to ask the ICs technology adoption status of peach farm households. The interviewer applied a structured and comprehensive questionnaire. The data collection was carried out from 270 respondents using proportional sampling allocation technique.

Estimation of binary choice logit models

The use of dichotomous choice data model is the most common method to investigate whether technology is adopted or not adopted. Binary logistic regression is typically used when the dependent variable is dichotomous, and the independent variables are either continuous or categorical variable. Therefore, this study uses a binary logistic regression model. In deciding whether or not to adopt a given ICs technology, it may assume that the producer weighs up the marginal advantages and disadvantages of adoption. Moreover, it is important to know the individual factor influencing the farmer’s choice. Hence, dummy variables can be used to describe the observed pattern of adoption:

The farmer’s adoption and non-adoption of ICs technology probabilities is described as P=P [Y_i=1] and, 1- P = [Y _i = 0] respectively. The probability function of this binary adoption variable can express as f(Y)=P^y (-P^1-y) where Y=0,1. The econometric techniques used in this manuscript obtained from ^{GREENE (2005}) and ^{HILL et al. (2008}).

To investigate the factors influencing the adoption of ICs, this study constructs a binary choice model to specify the factors influencing farmer’s decision making. Farmers often consider the benefits from adoption of new technologies such as ICs, which can be denoted by U^* (<) where (<) is net farm income. If the expected utility from adoption is greater than non-adoption, the technology is considered to be adopted and can be mathematically expressed as U _i ^* (<) > U _N ^* (<). The farmer’s decision-making parameters are often un observed and can be measured by a latent variable U _i ^* that might be due to socio-economic characteristics, institutional, farm resources and environmental factors variables (x _i ):

Where β denotes the adoption parameters and e _i is a random error term. After the determination of farmer’s technology choice, dummy variables can be used to express the observed pattern of ICs adoption (y _i ) while the observed values of y _i are related to latent variable y^*; that is otherwise.

y_i= 0

Thus, the technology adoption probability is given below:

Where β is the parameters used in the maximum likelihood procedure and F is the cumulative distribution function (CDF). Binary choice models differ only in the assumption about the functional form of F. To determine the farmers’ adoption probability of ICs technology; the logit model can be used and expressed as:

After the estimation of the maximum likelihood of the logit model, the following procedure was employed. Given the specific farmers characteristics (x _i ), initially, the probability of ICs adoption was predicted. Next, for a change in farmer-specific characteristics (x _i ) the change in probability (∆P) was estimated. For example, if a farm size (x ₁ ) has x _a =1 acre and x _b = 2 acres of two specific values, the change in probability (∆P) as a result of the change in farm size from 1 to 2 acres is determined as under:

Third, if other factors being constant (cetrisperibus), the marginal effect on the probability that farmer chooses to adopt ICs can be computed as:

Finally, calculating the odds ratio of technology adoption, which is the frequency that a farmer will use ICs rather than local cultivar (^{HILL et al., 2008}):

Description of the variables

Explanatory variables included in the binary choice model were classified into four sections: socio-economic, farm resources, institutional and climatic factors (Table 1). The inclusion of the various explanatory variables and their prior expected influence in the models was based on theoretical foundations and from past research literature. For example, research studies reveal that age (^{MARIANO et al., 2012}; ^{EMMANUEL et al., 2016}; ^{MARTEY et al., 2014}), education (^{ABDOULAYE & SANDERS, 2005}), farm size (^{ABDULAI & HUFFMAN, 2014}; ^{MARTEY et al., 2014}), credit (^{MARTEY et al., 2014}; (^{ABDULAI & HUFFMAN, 2014}; ^{ABDULAI et al., 2011}) variables significantly influence the adoption of agricultural technologies. A recent study reveals that farm size had a positive relationship with the adoption of modern agricultural technology. This means that farmers with larger farm size are more likely to adopt irrigation equipment, tractors, and modern variable inputs (^{AKUDUGU et al., 2012}). Moreover, previous research studies suggested socio-economic factors (age, education, gender, farm size, location) and institutional factors (extension contact, leasehold, credit access, market) are hypothesized to influence the adoption of agricultural technologies (^{ABDOULAYE & SANDERS, 2005}; ^{ABDULAI et al., 2011}; MARIANO et al., 2012; ^{ABDULAI & HUFFMAN, 2014}). Furthermore, the choice of access to extension services variable have been incorporated in modeling, because it plays a vital role in the dissemination of useful and practical information related to agricultural technology adoption (^{NAMBIRO et al., 2006}; ^{ADEBAYO, 2012}; ^{PAN, 2014}). In addition to the input variables, three variables are specified to capture the effects of climatic factors on ICs technology adoption of peach production, i.e., natural disaster risk perception, soil quality perception, and weather shocks awareness. Natural disaster risk perception was measured based on low, medium and high ranking when a farmer perceived natural event risk such as flood, drought, windstorm, disease/insect’s outbreak within cropping season. The soil quality perception was measured based on low, medium and high ranking when a farmer perceived the capacity of a specific kind of soil to function within the specific natural environment. Similarly, weather shocks awareness was measured based on low, medium and high ranking when a farmer was aware of the fluctuation in the temperature and rainfall within the cropping season.

Table 2 shows the descriptive statistics of the variables for adopters and non-adopters of ICs technology. Socio-economic characteristics of farmers such as age, education, experience household size and off-farm income show slight variation across groupings. On an average, peach farmers have 5 years of schooling, 8 years of peach farming experience and 6 household members. The average age of the peach farmer was 42 years. On average, only 48.9% and 38.9% of respondents could access credit and extension services, respectively in the selected districts. More farmers who own farm resources such as land and machinery belonging to the technology adopter group, and they also have higher off-farm income. Institutional factors play a vital role in the daily operation of the peach farm. These factors are biased towards technology adopters.

To check the multicollinearity of the data diagnostic tests were carried out. Variation Inflation Factor (VIF) and Condition Indices (CI) were examined with no evidence of multicollinearity reported. Marginal effects are reported because coefficients can be difficult to interpret for the model, given the need to compare to a base outcome.

The estimates of the binary logit model contain 17 explanatory variables and provide insightful results. Table 3 presents the binary logit estimated coefficients of the parameters and the marginal effects. Results of the Wald Chi-squared test is statistically significant at 1% level, suggesting that the explanatory variables jointly determine the adoption of ICs among peach producers. According to ^{Hensher and Johnson (1981}) consider pseudo-R² value between the range of 0.20 and 0.40 to be a good fit. In this study, the goodness-of-fit measures at the bottom of Table 3, the pseudo-R² statistics suggested our models fit the data reasonably well. Moreover, pseudo-R² value 40 percent present interesting results. This gives a meaning that variance in adoption is explained by socio-economic, farm resource, institutional and climatic factors are 40 percent. The remaining 60 percent variance may be due to ICs innovation attributes.

The effect of socio-economic characteristics of the farmers is expected to encourage the adoption of ICs. Results revealed that the coefficient of farmer’s experience had a positive and significant effect on adoption of ICs. In accordance with our expectations, farmers who are more experienced have greater ability to process information and search for technologies suitable to their production constraints than those who are less experienced. The marginal effects indicate that each additional year increase in experience of the farmer increases the probability of adopting the periphery technique by 0.8%. Our results are in line with ^{ABDULAI et al. (2011}), who reported that farmer experience plays a significant role in the adoption of agricultural technologies and innovations. To measure the impact of the availability of more financial resources of the farmer to adopt new technology, off-farm income variable was used. The coefficient of off-farm income had a significant and positive relationship with the adoption of ICs. The estimated marginal effect of this variable was also found very high; i.e. 9.1%. Off-farm income generated from off-farm activities. For instance, employment, small business enterprises, and raising livestock. In general, alternative income resources can help farmers to make necessary and timely investments, such as optimal inputs allocation and utilization (^{OSENI &WINTERS, 2009}; ^{CHAVAS et al., 2016}). The coefficient of farmer’s age had a positive relationship with the adoption of technology, and the estimated marginal effect indicates that the probability of adopting the technology increases by 0.3%. These results of the study are consistent with the results of ^{GRAZHDANI (2013}), and ^{Adesina & Baidu-Forson (1995}). Moreover, ^{PAUDEL et al. (2008}) argued that young farmer comparatively would be in favour of new technology. In contrast, older farmers are conservative to adopt new technology. This statement is endorsed by ^{ABDOULAYE & SANDERS (2005}), ABDULAI & HUFFMAN (2014) and ^{EMMANUEL et al. (2016}) in their respective studies. Education plays an important role in judging the behaviours and attitude of the farmers (^{AYDOGDU & YENIGÜN, 2016}) and creates opportunities to improve the managerial ability of farmers (^{NYUOR et al., 2016}). In this study education of the farmer had a positive effect on ICs adoption. It is interesting that with the increase in years of schooling the probability of adopting ICs would be enhanced by 0.4%. This result reveals that technology adoption rate increases if farmers are more educated (^{MARIANO et al., 2012}). Household size had a positive relationship with the adoption of ICs. This result also strengthens our initial hypothesis. The positive sign indicates that with increasing size of the household, the probability of farmers’ adoption of ICs increases. The reason is large family size can provide more labor during the production process and reduce the pressure of paying wages to extra labor. Cell phone usage of the farmer exerts a positive influence on the adoption of ICs. This finding is in agreement with the general belief that the use of cell phone increases extension agent contact and increases farmers’ production capacity. Accordingly, those farmers who use the cell phone frequently, receive more information and probability of adopting new technology increases (^{BOLARINWA & OYEYINKA, 2011}). Estimated marginal effects suggested that the probability of adopting the ICs increases by 4% with the increase in usage of cell phone.

Farm resources had significant influences on the adoption of ICs technology except for farm size. However, the estimated coefficient of farm size had a positive impact on ICs. Farm size determines the intensity of resources used by farmers. This result implies that farmers who cultivate large orchards have a higher probability of adopting ICs. Apparently, such farmers are business oriented and sell fruits in the market. Therefore, to obtain higher productivity, they tend to create new ways to maximize their profit by adopting ICs technology. Conversely, the fact that farmers who cultivate small orchards are highly risk-averse concerning new technology due to the limited size and uncertain outcomes from the technology. This finding is consistent with the previous study of ^{GRAZHDANI (2013}). Livestock ownership is positively correlated with the adoption of peach ICs technology. Livestock number is an important economic factor that can not only determine ability to adopt technology but also can affect the kind of technology choice (mainly organic versus inorganic). The use of organic fertilizer is a major component of a sustainable agricultural system and a commonly suggested method of improving soil fertility while capturing economies of scope in crop-livestock systems (^{ASFAW et al., 2016}). In addition, livestock contributes to ICs technology within the cropping system in various ways but directly through dung used as farmyard manure. Farmers with livestock could thus apply farmyard manure with inorganic fertilizer and residue incorporation. The coefficient of machinery ownership confirmed the positive and significant effect on ICs adoption. The estimated marginal effect shows that the probability of adopting the technology increases by 10.1% if a farmer has own farm machinery in use. The productivity of the technology increases when complemented with mechanization and naturally ICs are adopted for higher production.

The next set of explanatory variables is composed of institutional determinants such as access to credit, extension services, NGOs, and input price. The estimated coefficients of the investigated variables confirm to a priori expectations about their influence on the adoption of technology. The effect of credit was significant at 10% level of significance, and the estimated marginal effects suggested that the availability of credit increases the likelihood of ICs adoption by 8.2%. The use of ICs requires more expenditure than local cultivar due to their higher price. In addition, they also require more inputs of fertilizer and pesticide to boost their production. Hence, as most farmers have not enough money to buy all inputs to spend at a time on all production operation of peach. Therefore, with the availability of necessary credit, farmers are able to purchase productive farm inputs and invest in the technology. Our result coincided with the finding of ^{ABDULAI et al. (2011}). Fluctuations in prices in agricultural crops combined with the continuity of high production input prices resulted in many changes in the agriculture sector such as farmers’ confusion and unclear decision making regarding the allocation of areas to be cultivated with various crops. Here in our case, we reported that inputs price had a positive relationship with the adoption of ICs. When the inputs price is increasing, the adoption of ICs increases. Peach production is heavily dependent on inputs, such as fertilizers, pesticides, and other nutrients. If the price of these inputs is high in the market, then the farmer gives priority to adopt ICs for higher production. The access to extension services was found to be an appropriate factor encouraging the adoption of technology. ^{Ali & Rahut (2013}) indicated that agricultural extension services play a significant role in the adoption of improved agricultural technologies. Extension agents are expected to provide farmers with useful information on production technologies, efficient input combinations and market information, all aimed at enhancing farm productivity and incomes (^{NYUOR et al., 2016}). The results display those farmers who have adequate access to extension services have a higher probability of adopting ICs. The estimated marginal effects indicated that the availability of extension services increase the probability of ICs adoption by 2.1%. The results confirm the usefulness of extension services in promoting agricultural technology in developing countries (ABDULAI & HUFFMAN, 2014; ^{EMMANUEL et al., 2016}). The importance of NGOs role has been highly recognized by the farmer’s perception of the adoption of modern technologies. There is a positive relationship between the role of NGOs and adoption of ICs of peach farmers. The estimated marginal effects indicated that the role of NGOs increases the probability of ICs adoption by 0.9%. It means that NGOs are contributing to farmers in disseminating the information and knowledge about the adoption of modern technology.

The last sets of variables of our interest are climatic factors such as natural disasters risk perception, soil quality perception, and weather shocks awareness. The estimated coefficients of the investigated variables confirmed the positive sign and are highly significant at the 1% level of significance. Furthermore, the estimated marginal effects showed that appropriate information about climatic factors increases the probability of ICs by 7%, 10.3%, and 13.2% respectively. These results suggested that farmers who have more information and knowledge about a climatic aspect of their farms are more likely to adopt ICs. These findings are consistent with ^{TAMBO & ABDOULAYE (2012}) that farmers who are aware of the changing climate have a higher probability of adopting new technology. To minimize the impacts of climatic factors, horticulture scientists breed new peach cultivar which is more resilient to climatic condition and favorable to the production environment of farmers. It is crucial that horticulturist should find more scientific techniques such as a mutation in genes of peach varieties for improved resistance to biotic and abiotic stresses. These scientists need to increase their attention to breed varieties that have a higher tolerance to local abiotic stresses such as drought, flooding, and extreme temperatures as well as continuing to breed for resistance to pests and diseases. Priorities for breeding should consider the magnitude of the predicted impacts on productivity of the crop, the number of people who depend on the crop and their level of poverty, and the opportunities for significant gains through breeding. Local knowledge of ecological interactions, traditional varieties, and the genetic diversity in the farm crops provide abundant critical resources, to build priority breeding programs for climatic tolerant varieties.