Barriers to the adoption of multiple agricultural innovations: Insights from Bt cotton, wheat seeds, herbicides and no-tillage in Pakistan

Bilal, Muhammad; Jamali Jaghdani, Tinoush Article — Published Version Barriers to the adoption of multiple agricultural innovations: Insights from Bt cotton, wheat seeds, herbicides and no-tillage in Pakistan International Journal of Agricultural Sustainability Provided in Cooperation with: Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale) Suggested Citation: Bilal, Muhammad; Jamali Jaghdani, Tinoush (2024) : Barriers to the adoption of multiple agricultural innovations: Insights from Bt cotton, wheat seeds, herbicides and no-tillage in Pakistan, International Journal of Agricultural Sustainability, ISSN 1747-762X, Taylor & Francis, London, Vol. 22, Iss. 1, pp. 1-19, https://doi.org/10.1080/14735903.2024.2318934 , https://www.tandfonline.com/doi/full/10.1080/14735903.2024.2318934 This Version is available at: https://hdl.handle.net/10419/289208 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Barriers to the adoption of multiple agricultural innovations: insights from Bt cotton, wheat seeds, herbicides and no-tillage in Pakistan Muhammad Bilal a and Tinoush Jamali Jaghdani b a School of Business & Economics, Westminster International University in Tashkent, Tashkent, Uzbekistan; b Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Halle (Saale), Germany ABSTRACT The slow pace of the adoption of the latest innovations in agricultural technology innovations impedes sustainable farming practices and sustainable agriculture in developing countries. This study investigates the potential reasons that stop smallholder farming households from adopting genetically engineered seeds (Bt cotton and improved wheat seeds), herbicides resulting from cutting-edge technologies, and no-tillage farming. Utilizing original farm household-level data from 275 smallholder farming households in Pakistan, we employ multivariate probit models. The results show that the adoption of innovative technologies is not an isolated, separate process but a concoction of available technologies and cropping patterns. The estimates of the multivariate probit models show that farm machinery index, off-farm income sources, and farmers’education facilitate technology adoption. The observations and estimates indicate that a lack of agricultural extension service contacts is present, which slows down the farmers’ adoption of agricultural technological inputs. Therefore, promoting the role of agricultural extension services (qualitatively and quantitatively) is likely to play a role in multiple technology adoption. Furthermore, the significant effect of off-farm employment shows that the lack of financial resources is another factor slowing the adoption of innovative technologies, which depends on liquidity for necessary expenditures. ARTICLE HISTORY Received 4 July 2023 Accepted 11 February 2024 KEYWORDS Agricultural extension; cotton-wheat zone; cuttingedge technology; off-farm income sources; smallholder farming households 1. Introduction Innovation has been an integral part of agriculture since its creation and innovation in agriculture has played a crucial role in economic development globally (Alston & Pardey, 2021). As it is defined by Edmund Phelps, a Nobel Prize winner in economics, innovation is any ‘new method or new product that becomes a new practice somewhere in the world’ (Ridley, 2020). Public policies are important for the creation of new innovations in agriculture and their adoption, which seems to fail in many cases (Alston & Pardey, 2021). In spite of these failures, innovation can accelerate the transition toward sustainable food systems (Herrero et al., 2020). The innovation’s adoption does not inclusively consist of the adoption of new materials or methods. It could be the adoption of new institutions, such as the water market (Jaghdani & Brümmer, 2016; Wheeler et al., 2009) or the adoption of new agricultural systems such as pesticide-free practices (Möhring & Finger, 2022). The acceleration in the diffusion and adoption of new innovations in agricultural technologies increases farm productivity and can enhance sustainable development globally. However, in developing countries, the low acceleration of the adoption of the latest innovations in agricultural technologies impedes sustainable farming practices and © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. CONTACT Tinoush Jamali Jaghdani [email protected] Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Theodor-Lieser-Str. 2, 06120 Halle (Saale), Germany INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 2024, VOL. 22, NO. 1, 2318934 https://doi.org/10.1080/14735903.2024.2318934 sustainable agricultural development in general. Hence, the low adoption levels hamper overall economic growth and agricultural transformation. Despite dogged perseverance in fostering rural development in developing countries in recent years, low adoption levels still persist. The issue of lagged adoption of agricultural innovation has been of interest to economists for a long time (Alston & Pardey, 2021), and there are different methods available that try to model the adoption or diffusion of new innovations in agriculture (de Oca Munguia et al., 2021) and different theories in economics to explain them (Gallardo & Sauer, 2018). The recent empirical evidence shows the significant snags that hamper low adoption levels. For example, findings from Mendola (2007), Kassie et al. (2011), Simtowe et al. (2016) and Bilal and Barkmann (2019) all display that there is less contact with extension service providers, that farmers’associations are lacking membership, that there’s fewer means of generating off-farm incomes, and greater distances between farms from primary agricultural input/output markets. These issues, among others, hamper technology adoption. Furthermore, the role of new analogue or digital technologies cannot be neglected. For instance, Silvestri et al. (2021) assessed the impact of radio and short message service (SMS) in expanding smallholder participation in legume-based sustainable agricultural intensification (SAI) practices and technologies in Tanzania. The results show that both awareness and adoption are increased if SMS supports radio campaigns. Nevertheless, radio alone was found to be the most cost-effective approach. Furthermore, Zhong et al. (2023) have shown that internet usage can significantly promote the joint adoption of sustainable agricultural practices (SAPs). A growing body of literature indicates the fundamental role of technology adoption in boosting farm productivity and sustainable development at large. For example, the adoption of crop protection products from multinational brands (CMBs); modern seed technologies; Bt (Bacillus thuringiensis) cotton; integrated pest management (IPM) techniques; soil and water conversation methods; natural resource management technologies; and no-tillage farming show a positive association with farm productivity and sustainable development at large (Abdulai & Huffman, 2014; Asfaw et al., 2012; Barbera et al., 2012; Bilal et al., 2022a; Noltze et al., 2013). Some meta-studies have summarized the convergences and divergences in the literature on the adoption of agricultural technologies. For instance, in a meta-analysis of 96 adoption studies of farmlevel soil conservation practices in developed countries, Wauters and Mathijs (2014) found inconsistencies in the effectiveness of the classic factors that influence the adoption of innovation. In their metaanalysis, Ruzzante et al. (2021) have concluded that there are few universal effective factors of agricultural adoption. They found education, land size, access to credit, land tenure, contact with extension agents, and membership in farmers’organizations to all positively influence the adoption of most of the technological innovations under study. Dessart et al. (2019) reviewed the studies on the behavioural factors that influence farmers’decisions to adopt environmentally sustainable practices for the period after 1999. They address how extraversion, openness to new experiences, risk-seeking, morals and environmental concern, as well as lifestyle farming objectives are associated with a higher adoption of sustainable practices. In contrast, being resistant to change and moved by economic objectives increases the reluctancy of farmers to adopt sustainable practices. They conclude that farmers’behaviours can be indirectly segmented according to sociodemographic and geographic characteristics can lead to necessary policies. In their review of the publications between 2000 and 2021 on the adoption of sustainable agriculture innovations, Rosário et al. (2022) found that the use of behavioural models that select sociopsychological constructs has increased. They suggest building a transand multidisciplinary model for future research. In another review of the adoption studies on developing countries’agriculture, Takahashi et al. (2020) have concluded that there are profitable technologies that are not diffused widely due to weak extension systems. Furthermore, they found there to be a lack of analysis on the profitability of new innovations within adoption studies to date, and also noted how many diffusion studies focus only on a single technology, such as an improved seed variety, improved planting method, or the use of compost. In two studies, de Oca Munguia et al. (2021) and de Oca Munguia and Llewellyn (2020) reviewed different conceptual frameworks surrounding adoption, showing how a diversity of prospective exists among adoption studies. de Oca Munguia and Llewellyn (2020) specifically address how there is a clear tendency for variables related to the technologies or practices to be 2M. BILAL AND T. J. JAGHDANI underrepresented in adoption studies compared to behavioural or socioeconomic variables. Furthermore, they argue that the topic of multiple technology adoption can be considered a gap in the adoption research literature both conceptually and practically (de Oca Munguia et al., 2021). Most previous studies discuss the determinants of single-input adoption or sustainable farming practices (e.g. CMBs, modern seed technologies, Bt cotton, IPM, soil and water conversation methods, natural resource management technologies and no-tillage farming). However, the empirical evidence may be tainted due to methodological challenges related to singleinput adoption decisions, endogeneity and farmers’ adoption decisions for multiple modern inputs, which may produce biased estimates (Abay et al., 2018). The introduction of SAI has increased the importance of multiple technology adoption. SAI refers to a combination of agricultural processes that maintains a level of production or increases it while environmental outcomes are also enhanced. For example, farmers who adopt different SAI practices could increase their yield per hectare by using new seed varieties and employing different agroeconomic-agroecological practices (Pretty, 2018). Irrespective of the empirical evidence regarding the determinants of single-input adoption decisions of agricultural technology products in developing countries, adopting multiple modern inputs remains an empirical puzzle. The adoption of multiple modern inputs presumably complements each other and favours sustainable development. For example, multinational crop protection products and mechanized farming provide higher farm revenues (Bilal et al., 2022a). Similarly, improved seed varieties complement chemical fertilizers and provide better yields (Nyangena & Juma, 2014). Likewise, an efficient soil conservation method, such as no-tillage, helps to avoid ploughing/aggressive mechanized farming (Barbera et al., 2012). Hence, Brookes et al. (2017) and Danne et al. (2019) highlight the pros and cons of the joint adoption of no-tillage systems with modern herbicides (e.g. glyphosate formulations). Tadjiev et al. (2023) have found that no-tillage farming practices among smallholders in Kyrgyzstan to be accompanied with higher herbicide costs and higher labour intensity. Considering the adoption of other technologies in SAI practices, Kotu et al. (2017) have studied multiple technology adoption of cereal-legume intercropping, cereal-legume rotation, organic fertilizers, soil and water conservation practices, chemical fertilizers and improved seeds in Ghana. They found that access to markets, capital and information/knowledge would increase the multiple adoptions in the context of SAI. Furthermore, a higher technology adoption is accompanied by higher productivity. In another study on vegetable production in Tanzania, Ochieng et al. (2022) found that improved varieties, fertilizers and pest management practices are complementarities, as manure and mineral fertilizers show trade-offs between them. Adopting genetically engineered seeds such as Bt cotton and high-quality wheat seeds, herbicides resulting from cutting-edge technology, and notillage farming are regarded as sustainable agriculture innovations (Lee, 2005). Abay et al. (2018) studied input complementarity between chemical fertilizer and improved seeds, while Tadjiev et al. (2023) observed how no-tillage adoption supports higher herbicide costs. Considering the nuances deduced from Pretty (2018), the relevant stakeholders pay more attention to the adaptation process of the SAI model as the introduction of SAI has increased the importance of multiple technology adoption. Based on a careful review of the existing literature, we noticed that the determinants affecting the adaptation processes of multiple sustainable agricultural innovations have not been widely explored. Hence, it is of high interest to contribute to the present knowledge on this issue. Furthermore, we have noticed in particular how understanding an explicit decision to adopt Bt cotton, high-quality wheat seeds, herbicides resulting from cutting-edge technology, and no-tillage farming is lacking. Therefore, this study envisages the complementary (positive correlation) or trade-offs (negative correlation) between the adoption of multiple sustainable agricultural innovations. We intend to contribute to the existing literature by providing empirical evidence regarding the most promising determinants of the adoption of complementary inputs/farming practices by smallholders with a particular focus on policy implications. In this study, we mainly investigate the potential reasons stopping smallholder farming households from adopting genetically engineered seeds (Bt cotton and high-quality wheat seeds), herbicides resulting from cutting-edge technology, and notillage farming in the Punjab Province of Pakistan. The present study is confined exclusively to smallholders because the significant features of South Asian agriculture are smallholders and their smallholdings (Wiggins & Keats, 2013). The farm size in major INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 3 agricultural countries of South Asia ranges from 0.5 to 3.0 ha on average. The average farm size is 0.5 ha in Bangladesh, 0.8 ha for Nepal and Sri Lanka and 1.4 ha in India. The average farm size in Pakistan is relatively higher than in neighbouring countries due to a high concentration of significant land holdings (Thapa & Gaiha, 2014). According to the agricultural census 2010 in Pakistan 1 , farms with an area of less than 5 acres (farm size ≤2.02 ha) constitute small farms, farms with an area more than 5 acres and less than 25 acres (2.02 ha < farm size ≤10.11 ha) comprised the category of average farms, and the large farms’size is 25 acres or more (farm size > 10.11 ha). The total cultivated area of the Punjab Province is 10,941,000 ha, which are unevenly distributed among farmers. Only 23% (2,516,430 ha) of the area is cultivated by small farms and farmers operating these farms are thus considered smallholders, while 37% (4,048,170 ha) of the cultivated area is operated by average farmers, and 40% (4,376,400 ha) by large farmers (considering the definition of Pakistan Bureau of Statistics). Most noteworthy, the average and large farms account for 77% of the total cultivated area (Government of Punjab, 2019). In this study, we have utilized primary data from 275 smallholder farming households in Pakistan, which was acquired by a field survey during 2017. In order to analyze our research questions, we employed a multivariate probit model to test the possible complementary or trade-offsamongdifferent technology adoption practices. This paper is structured as follows: an overview of smallholder constraints in the adoption of multiple agricultural innovations, extension institutions and policies in Pakistan is given in section 2 while the following section 3 details the study site and sampling techniques. The theoretical framework is provided in section 4 and the empirical strategy in section 5, while section 6 covers the results and section 7 contains a discussion. Section 8 concludes this analysis with policy implications and prospects for future research in developing countries. 2. Smallholder constraints in the adoption of multiple agricultural innovations in Pakistan and extension policies The adoption of multiple innovations in Pakistan is the focus of this study. Pakistani farmers face substantial problems in terms of socio-economic constraints that affect their adoption behaviour. Among the plausible reasons are that they have larger household sizes, small landholding sizes, high illiteracy rates, a lack of farm machinery, less interest in technology adoption, a lack of communication between farmers, and less contact with agricultural extension services (Jalal-Ud-Din, 2011). Initial adoption of low-quality inputs is widespread in Pakistan, and it retards agricultural development (Ahmad Khan et al., 2013; Hashmi, 2016). The aggressive and inappropriate use of low-quality herbicides can endanger ecosystem services, including soil microorganisms, birds and other non-targeted organisms (Aktar et al., 2009). Smallholdings in the agricultural heartland of Pakistan are approximately 60% of the total agricultural holdings. The smallholders operating these farms use less multiple agricultural innovations due to socio-economic constraints (Malik et al., 2016; Thapa & Gaiha, 2014). Aslam (2016) highlights how less adoption of technological innovations is the major constraint for Pakistan’s lower average yields compared to average yields obtained internationally. Access to agricultural credit supports the adoption of technological innovations; however, deprived socio-economic factors play a key role in farmers’lesser access to agricultural credit (Bilal et al., 2015). In addition, women farmers in Pakistan face more severe problems in obtaining agricultural credit (Muhammad et al., 2020). Since 2002 Pakistani farmers have been cultivating genetically modified (e.g. Bt cotton), pest-resistant cultivars of cotton seeds (Abdullah, 2010; James, 2012). Also, in the cotton-wheat zone of Punjab in Pakistan, there exists a diversity of wheat seed of varying quality, and high-quality wheat seeds such as sehar-06 and Inqilab-91 varieties that are the most popular varieties in Punjab (Battese et al., 2017). Likewise, farmers adopt herbicides to resist weed growth (Ali et al., 2013); however, the diversity of herbicides of varying quality goes from cuttingedge technology imported from OECD 2 listed countries based upon their successful registration in the source countries to locally manufactured substandard herbicides (Bilal & Barkmann, 2019). The sub-standard herbicides may have low-quality formulations, unpredictably varying concentrations of active agents, and a lack of product information and sufficient labelling (Ahmad Khan et al., 2013; Hashmi, 2016). As was mentioned before, less contact with agricultural extension services is an important constraint on the adoption of multiple innovations in Pakistan. The importance of agricultural extension services 4M. BILAL AND T. J. JAGHDANI has been of interest among researchers and is reflected in the recent and past literature regarding agricultural policy reforms (Ragasa & Mazunda, 2018; Rogers, 1962). However, Khan and Imam (1985) imply firmly that the agricultural reform outcomes mainly benefit large farms and traditionally large farmers in developing countries, such as Pakistan. They assert that the ignorance of small farmers likely increases agricultural inefficiencies. Reform policies that are not coupled with small farming households are constrained and inefficient since the smaller farmers are deprived of certain socio-economic attributes (e.g. illiteracy, less awareness of modern innovations, and accessibility to modern innovations due to financial constraints). Hence, Elahi et al. (2018) suggest that the lack of agricultural advisory services and barriers that restrict small farmers from obtaining agricultural credit are the main reasons behind less agricultural productivity. Likewise, for developing countries such as Pakistan, the adoption of innovation/modern farming practices is generally slower, and farmers’interests vary according to the feasibility of technologies (Ashraf et al., 2015). Government institutions, which are mainly in the form of agricultural extension services, contribute substantially to technology adoption. In disseminating modern technologies and modern ways of farming, the government of Pakistan much relies on agricultural extension services. However, Baloch and Thapa (2019) and Bilal et al. (2015) highlight the main hurdles in the effective outreach and in the implementation of agricultural policies because of institutional constraints that include: .lack of knowledge capital and fewer extension workers .the focus of extension services mainly on large farmers .ignorance of smallholders .financial constraints .lack of communication between agriculture departments Considering the latest digital developments, farmers’awareness of modern digital technologies and electronic media play an essential role in innovation adoption. It expedites famers’existing knowledge of modern farming technologies and keeps them aware of the latest information (Abbas et al., 2003). Among the medium of information transfer to farmers, the information and communication technology (ICT) is now considered a vital tool for the farming community. However, the effectiveness of ICT on disseminating information to farmers’ demands in-service training and capacity building programmes for extension service providers (Aldosari et al., 2019). 3. Study sites and sample selection We administrated a smallholder farming household survey in the Punjab Province of Pakistan, focusing on smallholders because of their substantive majority (64%). The Punjab Province is vital concerning agriculture as it accounts for 58% of the total cultivated area of Pakistan. In 2022, the total population of Pakistan was 224.78 million, of which 141.96 was rural, while the population of Punjab was 110 million, of which 70 million was rural (Government of Pakistan, 2022). Additionally, rural Punjab’s literacy rate is just 57%, out of which 67% of males are literate and 48% of females. The shares of Punjab in the total production of major crops in Pakistan are as follows: cotton 76%, rice 64%, sugarcane 64% and wheat 75% (Government of Punjab, 2019). The Punjab Province has nine divisions (a division is the highest administrative unit), with each division consisting of some districts. Data used for the study was drawn from the three vital agricultural divisions of the Punjab Province, resulting in three randomly selected districts. Among these, the Rahimyar Khan District in the Bahawalpur Division, the Vehari District in the Multan Division and the Pakpattan District in the Sahiwal Division constitute the cotton-wheat zone of Pakistan (see Figure 1). Administratively, each division is further delineated into districts, tehsils (below the district administrative unit), and union councils (the lowest administrative unit with a formal government comprising several villages). Using a multi-stage random sample, households from 18 villages in the cottonwheat zone of the Punjab Province were surveyed from September to December 2017. The final sample size yields N = 275 smallholders who were selected for face-to-face interviews. We use a structured questionnaire and collected information about the household’s socio-demographics, food security, the adoption status of genetically engineered seeds (Bt cotton and high-quality wheat seeds), herbicides resulting from cutting-edge technology, and no-tillage farming. INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 5 Table 1 presents the percentage shares of important farming indicators of the survey area from the entire Punjab Province. The table shows a substantial share of the study area under cotton crops (72%) in comparison to the whole province. Additionally, Table 1 displays that 28% of the province’s total population is living in the study area, which is a bit higher for the rural population alone (33% of the province’s rural population lives in the study area). Furthermore, the literacy ratio of the rural and urban population of the survey area is substantially low and similar to the entire Punjab Province. 4. Theoretical framework In this section, we present a theoretical framework for the adoption of multiple technologies which is built on previous studies on farmers’adoption decisions. A theoretical framework is necessary to model the adoption process numerically. Many frameworks have been developed during the last decades to Figure 1. Map of Pakistan pointing to the study area; source: own elaboration. (This map is developed using shapefiles from https://geodata. lib.utexas.edu and ggplot2 package in R statistical software; Wickham, 2016.) Table 1. Salient indicators of the survey area. Indicators Bahawalpur Division Multan Division Sahiwal Division Entire Punjab Province % Share of survey area from Punjab Total reported area (1000 ha)* 2142 1524 1032 17,518 27 Area sowed under cotton (1000 ha)* 664 712 102 2053 72 Area sowed under wheat (1000 ha)* 930 835 479 6560 34 Total tube well (numbers)* 114,412 63,801 75,243 1,100,841 23 Total tractors (numbers)* 51,554 43,027 32,729 331,905 38 Total threshers (numbers)* 23,449 20,211 14,202 145,155 40 Harvesters/reapers (numbers)* 10,434 4179 2230 40,285 42 Population (1000 persons)* 11,464 12,265 7380 110,012 28 Rural population (1000 persons)** 8630 8851 5731 69,442 33 Literacy of rural areas (ratio)* 0.26 0.30 0.39 0.38 Literacy of urban areas (ratio)* 0.57 0.61 0.66 0.65 Data source:*Government of Punjab (2019) and **Government of Pakistan (2017). 6M. BILAL AND T. J. JAGHDANI model innovation adoption in agriculture. We have employed the utility maximization theoretical framework that was first employed by Rahm and Huffman (1984) for the empirical analysis of adoption and further developed for adoption modelling of farmers by Adesina and Zinnah (1993) and Batz et al. (1999). As was formulated by Ruzzante et al. (2021), the economic constraints paradigm suggests that farmers’ main goal is utility maximization subject to their available uneven resource endowments, and this leads to observed patterns of innovation adoption. This paradigm is widely used by economists to analyze technology adoption. We have used the recent adaptations of the utility maximization model by Möhring and Finger (2022), which allows us to model individual farmers’decisions on multi-technology adoption based on the utility maximization theoretical framework. We assumed the jth smallholder farming household (j=1,…, N), which was determined based on whether or not they opted for the given technologies. Following the conceptual framework defined by Möhring and Finger (2022) for farmers’adoption of a single technology, let p j(Ak j,Xj, e A j) denotes the random profit of farmer j, where Ak jdenotes a farmer’s adoption decision of kth modern inputs technology/farming practice (with Ak=0 jreflection adoption of one of the technologies). Xjdenotes the vector of the farm and farmers’characteristics and e A jreflects the uncertainty affiliated to the adopted technology. We can define the utility maximizing problem of a farmer due to its adoption as: maxAk jE[U( p j(Ak j,Xj, e A j), FEj)] (1) where Uis a von Neumann-Morgenstern utility function of the farmer and FEjdenotes a farmer’s expectations of the technology effects on his production yields, income and costs. A utility maximizing farmer would then choose to adopt the new production technology, ceteris paribus, if: E[Uk( p j(Ak=0 j,Xj, e A=0 j), FEj)] .E[U0( p j(Ak=0 j,Xj, e A=0 j), FEj)] (2) here, let Uorepresent the utility level of the smallholder farming household from traditional farming practices and let Ukrepresent the utility level of the adoption of kth modern inputs technology/farming practice. The smallholder farming household decided to adopt the kth technology if: Y∗ jk =U∗ k−Uo.0 (3) where Y∗ jk represents the expected benefit a smallholder farming household earns from adopting kth technology, and it is a latent variable determined by observed and unobserved characteristics of the farming households, which can be presented as (Cappellari & Jenkins, 2003): Y∗ jk =X′ jk b k+ m jk (4) where Xjk represents the observed characteristics of a farm and farmer; m jk represents unobserved characteristics; kdenotes the type of technology available and b kdenotes the vector of the parameter to be estimated. 5. Empirical strategy Based on our theoretical framework, we employed multivariate probit models as they require capturing the unobserved factors and the extent of association among adoption decisions to avoid biased and inefficient estimates (Abay et al., 2018). According to Kassie et al. (2009) and Mesfin et al. (2016), the significant features of this model are: .allowing for potential correlation between unobserved factors .simultaneously modelling the effect of observed factors on each specific adopted technology/ farming practice Therefore, by applying the multivariate probit models, in addition to finding the influential factors, we can envisage the complementary (positive correlation) and trade-off(negative correlation) between the adoption of genetically engineered seeds such as Bt cotton (referred to as B) and high-quality wheat seeds (referred to as W), herbicides resulting from cutting-edge technology (referred to as H), and no-tillage farming (referred to as T). Using the indicator function, the unobserved preferences in Equation (4) translate to the observed binary outcome equation for each choice as follows: Yjk =1if Y∗ jk .0 0otherwise (5) In the multivariate probit model, the error terms jointly follow a multivariate normal distribution INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 7 (MVN) with zero conditional mean and variance, which is normalized to unity. With this condition, the symmetric covariance matrix Vcan be given by: V= 1 r TB r BT 1 r WT r WB r HT r HB r TW r TH r BW r BH 1 r WH r HW 1 ⎡ ⎢ ⎢ ⎣ ⎤ ⎥ ⎥ ⎦ (6) The off-diagonal elements in the covariance matrix represent the unobserved correlation between the stochastic components of the different types of innovative technologies (Teklewold et al., 2013). 6. Results The descriptive summary of the demographic and socio-economic variables of the smallholder farming households that was acquired from the survey is presented in Table 2. We follow other empirical studies and economic theory to have necessary covariates for this study (Bilal et al., 2022a; Bilal & Barkmann, 2019; de Janvry et al., 1991). Most notably, farm machinery index is remarkably low across the sampled farmers (14%) but a substantive majority (79%) practice laser levelling. Likewise, almost half (46%) of the total farms also generate income other than farming. The average amount of contact made by agricultural extension agents with farmers is only less than one visit/month (0.80), however, almost a quarter seek agricultural information via radio (26%), and a few were members of a local farmers’association (10%). The variable of access to credit does not necessary mean that respondents actually received a credit. It only shows that if the respondents need credit, they can easily fulfil requirements to obtain the agri-credit. 3 Figure 2 shows the visual picture of the causal relationship between the six selected factors and innovation adoption by farm operators. The average area under wheat cultivation (0.93 ha) by farm operators compared to the mean area under cotton cultivation (0.76 ha) is almost the same. Interestingly, the average of cotton and wheat area significantly differs between exclusive non-adopters’farms (Figure 2(b)/(d)). However, there is a moderate causal relationship between the area under cotton cultivation and technology adoption on average. The farm machinery index and offfarm income sources also tends to be higher for adopters’farms (Figure 2(c)/(e)). Nevertheless, on average there is a more consistent causal pattern between farm machinery index and technology Table 2. Descriptive statistics. Variable Mean Std. Dev. Min Max Dependant variables No-tillage (yes = 1; no = 0) 0.46 0.50 0.00 1.00 Herbicides of cutting-edge technology (yes = 1; no = 0) 0.52 0.50 0.00 1.00 Bt cotton seed (yes = 1; no = 0) 0.65 0.48 0.00 1.00 High-quality wheat seeds (yes = 1; no = 0) 0.61 0.49 0.00 1.00 Explanatory variables Age of the respondents (years) 43.53 12.92 17.00 73.00 Household size (numbers) 7.62 3.40 2.00 20.00 No of male (numbers) 3.40 1.98 1.00 13.00 Male above18 years 2.43 1.57 0.00 10.00 Education in years 5.71 4.41 0.00 16.00 Area sowed under cotton (ha) 0.76 0.58 0.00 2.02 Area sowed under wheat (ha) 0.93 0.52 0.00 2.02 Offfarm income (yes = 1; no = 0) 0.46 0.50 0.00 1.00 No of visits by agri-extension (numbers/month) 0.80 0.95 0.00 4.00 Access to credit (yes = 1; no = 0) 0.41 0.49 0.00 1.00 Laser level (yes = 1; no = 0) 0.79 0.41 0.00 1.00 Ownership of farm machinery (yes = 1; no = 0) 0.23 0.42 0.00 1.00 Farm machinery index a (weighted by present value) 0.14 0.30 0.00 1.00 No of milch animals (numbers) 1.26 1.60 0.00 10.00 Farm distance to output market (km) 13.19 8.83 0.00 100.00 Farm distance to input market (km) 11.52 7.88 0.00 60.00 Farm distance to the main road (km) 1.11 1.08 0.00 6.00 Membership in a farmers’union (yes = 1; no = 0) 0.11 0.31 0.00 1.00 Agri-extension-info via radio (yes = 1; no = 0) 0.26 0.44 0.00 1.00 Experts hired for spray (yes = 1; no = 0) 0.33 0.47 0.00 1.00 Source: Field survey results. a Total present value in Pakistani rupees of agricultural fixed assets owned by the small farming household, including any tractors, threshers, rotavators, trolleys, ploughs and spraying machines weighted by the average percentage share. 8M. BILAL AND T. J. JAGHDANI Notes on contributors Muhammad Bilal is currently a Senior Lecturer at the School of Business & Economics, Westminster International University in Tashkent, Uzbekistan. He got Ph.D. from Georg-August-University Göttingen, Germany. His research interests include different aspects of food systems, sustainable development goals, innovation in agricultural technologies, smallholders, and multinational corporations in the agriculture sector. Tinoush Jamali Jaghdani is a Research Associate at the Leibniz Institute of Agricultural Development in Transition Economies (IAMO) in Germany since October 2016. He received his PhD in agricultural economics (Faculty of Agriculture) with a minor in applied statistics (Faculty of Mathematics) from the University of Göttingen (Germany) in 2012. He acquired his B.Sc. (2001) and M.Sc. (2007) in agricultural economics from Iran and Germany respectively. His research focus has been on water economics, food price volatility, market power, trade duration and food supply chain governance. ORCID Muhammad Bilal http://orcid.org/0000-0003-2241-4610 Tinoush Jamali Jaghdani http://orcid.org/0000-0003-22040452 References Abay, K. A., Berhane, G., Taffesse, A. S., Abay, K., & Koru, B. (2018). Estimating input complementarities with unobserved heterogeneity: Evidence from Ethiopia. Journal of Agricultural Economics,69(2), 495–517. https://doi.org/10.1111/14779552.12244 Abbas, M., Sheikh, D., Muhammad, S., & Ashfaq, M. (2003). Role of electronic media in the adoption of agricultural technologies by farmers in the central Punjab-Pakistan. International Journal of Agriculture and Biology,5(1), 417– 419. https://www.researchgate.net/publication/237304797_ Role_of_Electronic_Media_in_the_Adoption_of_ Agricultural_Technologies_by_Farmers_in_the_Central_ Punjab-Pakistan Abdulai, A., & Huffman, W. (2014). The adoption and impact of soil and water conservation technology: An endogenous switching regression application. Land Economics,90(1), 26– 43. https://doi.org/10.3368/le.90.1.26 Abdullah, A. (2010). An analysis of Bt cotton cultivation in Punjab, Pakistan using the agriculture decision support system (ADSS). AgBioForum,13(3), 274–287. https:// mospace.umsystem.edu/xmlui/handle/10355/8975 Adesina, A. A., & Zinnah, M. M. (1993). Technology characteristics, farmers’perceptions and adoption decisions: A Tobit model application in Sierra Leone. Agricultural Economics,9(4), 297–311. https://doi.org/10.1111/j.1574-0862.1993.tb00276.x Ahmad Khan, F. Z., Sagheer, M., Hasan, M. u., Tahira Gul, H., Hassan, F., Manzoor, S. A., & Wahid, A. (2013). Agricultural dynamics in Pakistan: Current issues and solutions. Russian Journal of Agricultural and Socio-Economic Sciences,20(8), 20–26. https://doi.org/10.18551/rjoas.2013-08.03 Aktar, W., Sengupta, D., & Chowdhury, A. (2009). Impact of pesticides use in agriculture: Their benefits and hazards. Interdisciplinary Toxicology,2(1), 1. https://doi.org/10.2478/ v10102-009-0001-7 Aldosari, F., Al Shunaifi, M. S., Ullah, M. A., Muddassir, M., & Noor, M. A. (2019). Farmers’perceptions regarding the use of information and communication technology (ICT) in Khyber Pakhtunkhwa, Northern Pakistan. Journal of the Saudi Society of Agricultural Sciences,18(2), 211–217. https://doi. org/10.1016/j.jssas.2017.05.004 Ali, H., Shoukat, A., Ahmad, S., Sarwar, N., Arooj, M., Mahmood, A., & Shahzad, A. N. (2013). Impact of integrated weed management on flat-sown cotton (Gossypium hirsutum L.). Journal of Animal and Plant Sciences,23(4), 1185–1192. https://thejaps.org.pk/Volume/2013/23-4/abstracts/36.php Alston, J. M., & Pardey, P. G. (2021). Chapter 75 - The economics of agricultural innovation. In C. B. Barrett , & D. R. Just (Eds.), Handbook of agricultural economics (Vol. 5, pp. 3895–3980). Elsevier B.V. https://doi.org/10.1016/bs.hesagr.2021.10.001 Asfaw, S., Kassie, M., Simtowe, F., & Lipper, L. (2012). Poverty reduction effects of agricultural technology adoption: A micro-evidence from rural Tanzania. Journal of Development Studies,48(9), 1288–1305. https://doi.org/10.1080/00220388. 2012.671475 Ashraf, S., Khan, G. A., Ali, S., & Iftikhar, M. (2015). Socioeconomic determinants of the awareness and adoption of citrus production practices in Pakistan. Ciência Rural,45(9), 1701–1706. https://doi.org/10.1590/0103-8478cr20131227 Aslam, M. (2016). Agricultural productivity current scenario, constraints and future prospects in Pakistan. Sarhad Journal of Agriculture,32(4), 289–303. https://doi.org/10.17582/journal. sja/2016.32.4.289.303 Baloch, M. A., & Thapa, G. B. (2019). Review of the agricultural extension modes and services with the focus to Balochistan, Pakistan. Journal of the Saudi Society of Agricultural Sciences,18(2), 188–194. https://doi.org/10. 1016/j.jssas.2017.05.001 Barbera, V., Poma, I., Gristina, L., Novara, A., & Egli, M. (2012). Long-term cropping systems and tillage management effects on soil organic carbon stock and steady state level of C sequestration rates in a semiarid environment. Land Degradation & Development,23(1), 82–91. https://doi.org/10. 1002/ldr.1055 Barrett, C. B., Islam, A., Malek, A., Pakrashi, D., & Ruthbah, U. (2022). Experimental evidence on adoption and impact of the system of rice intensification. American Journal of Agricultural Economics,104(1), 4–32. https://doi.org/10.1111/ ajae.12245 Battese, G. E., Nazli, H., & Smale, M. (2017). Factors influencing the productivity and efficiency of wheat farmers in Punjab, Pakistan. Journal of Agribusiness in Developing and Emerging Economies,7(2), 82–98. https://doi.org/10.1108/JADEE-122013-0042 Batz, F. J., Peters, K. J., & Janssen, W. (1999). The influence of technology characteristics on the rate and speed of adoption. Agricultural Economics,21(2), 121–130. https://doi.org/10. 1111/j.1574-0862.1999.tb00588.x Bilal, M., & Barkmann, J. (2019). Farm and farmer capital foster adoption of improved quality agrochemical inputs in the cotton-wheat zone of the Punjab, Pakistan. Journal of Agriculture and Rural Development in the Tropics and Subtropics (JARTS),120(2), 229–240. https://doi.org/10. 17170/KOBRA-20191217881 INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 15 Bilal, M., Barkmann, J., & Brümmer, B. (2022a). The role of crop protection products of multinational brands for agricultural sustainability in the cotton-growing zone in Pakistan. Journal of Agriculture and Rural Development in the Tropics and Subtropics,123(1), 1–11. https://doi.org/10.17170/ KOBRA-202201195568 Bilal, M., Brümmer, B., & Barkmann, J. (2022b). A discussion on the outcomes of adopted agricultural technological products and specific sustainable development goals: Evidence from Pakistan. Cogent Economics & Finance,10(1), 1–21. https:// doi.org/10.1080/23322039.2022.2132644 Bilal, M., Shakoor, U., Nasir, A., & Raza, Q.(2015). Revisiting snags for obtaining agricultural credit in Pakistan: The case of small farmers. International Journal of ScientificFootprints,3(4), 41–51. Brookes, G., Taheripour, F., & Tyner, W. E. (2017). The contribution of glyphosate to agriculture and potential impact of restrictions on use at the global level. GM Crops & Food,8 (4), 216–228. https://doi.org/10.1080/21645698.2017. 1390637 Cappellari, L., & Jenkins, S. P. (2003). Multivariate probit regression using simulated maximum likelihood. The STATA Journal,3(3), 278–294. https://doi.org/10.1177/1536867X0 300300305 Danne, M., Musshoff, O., & Schulte, M. (2019). Analysing the importance of glyphosate as part of agricultural strategies: A discrete choice experiment. Land Use Policy,86, 189–207. https://doi.org/10.1016/j.landusepol.2019.04.023 de Janvry, A., Fafchamps, M., & Sadoulet, E. (1991). Peasant household behaviour with missing markets: Some paradoxes explained. The Economic Journal,101(409), 1400. https://doi. org/10.2307/2234892 D’Emden, F. H., Llewellyn, R. S., & Burton, M. P. (2008). Factors influencing adoption of conservation tillage in Australian cropping regions*. Australian Journal of Agricultural and Resource Economics,52(2), 169–182. https://doi.org/10.1111/ j.1467-8489.2008.00409.x de Oca Munguia, O. M., & Llewellyn, R. (2020). The adopters versus the technology: Which matters more when predicting or explaining adoption? Applied Economic Perspectives and Policy,42(1), 80–91. https://doi.org/10.1002/aepp.13007 de Oca Munguia, O. M., Pannell, D. J., & Llewellyn, R. (2021). Understanding the adoption of innovations in agriculture: A review of selected conceptual models. Agronomy,11(1), 139. https://doi.org/10.3390/agronomy11010139 Dessart, F. J., Barreiro-Hurlé, J., & Van Bavel, R. (2019). Behavioural factors affecting the adoption of sustainable farming practices: A policy-oriented review. European Review of Agricultural Economics,46(3), 417–471. https://doi. org/10.1093/erae/jbz019 Diao, X., Reardon, T., Kennedy, A., DeFries, R. S., Koo, J., Minten, B., Takeshima, H., & Thornton, P. (2023). The future of small farms: Innovations for inclusive transformation. In J. von Braun, , K. Afsana, L. O. Fresco, & M. H. A. Hassan (Eds.), Science and innovations for food systems transformation (pp. 191–205). Springer International Publishing. https://doi.org/ 10.1007/978-3-031-15703-5_10 Dorfman, J. H. (1996). Modeling multiple adoption decisions in a joint framework. American Journal of Agricultural Economics, 78(3), 547–557. https://doi.org/10.2307/1243273 Elahi, E., Abid, M., Zhang, L., ul Haq, S., & Sahito, J. G. M. (2018). Agricultural advisory and financial services; farm level access, outreach and impact in a mixed cropping district of Punjab, Pakistan. Land Use Policy,71, 249–260. https://doi.org/10. 1016/j.landusepol.2017.12.006 Fagerland, M. W., & Hosmer, D. W. (2012). A generalized Hosmer–Lemeshow goodness-of-fit test for multinomial logistic regression models. The Stata Journal,12(3), 447– 453. https://doi.org/10.1177/1536867X1201200307 Gallardo, R. K., & Sauer, J. (2018). Adoption of labor-saving technologies in agriculture. Annual Review of Resource Economics, 10(1), 185–206. https://doi.org/10.1146/annurev-resource100517-023018 Giordano, M., Turral, H., Scheierling, S. M., Tréguer, D. O., & Mccornick, P. G. (2017). Beyond “More Crop per Drop”: evolving thinking on agricultural water productivity. In IWMI Research Report 169. International Water Management Institute (IWMI). https://doi.org/10.5337/2017.202 Government of Pakistan. (2017). Punjab Population 2017. Pakistan Bureau of Statistics (PBS). https://www.pbs.gov.pk/ sites/default/files/population/2017/punjab_tehsil.pdf Government of Pakistan. (2022). Pakistan Economic Survey 202122. Ministry of Finance. https://www.finance.gov.pk/survey_ 2022.html Government of Punjab. (2019). Bureau of Statistics. Government of Punjab. https://www.bos.gop.pk/developmentstat Hashmi, M. S. (2016). Agrochemical and agricultural sustainability: A case study of Pakistan. Journal of Environmental and Agricultural Sciences,9,1–9. http://www.agropublishers. com/files/JEAS 9 1.PDF.http://jeas.agropublishers.com/ Herrero, M., Thornton, P. K., Mason-D’Croz, D., Palmer, J., Benton, T. G., Bodirsky, B. L., Bogard, J. R., Hall, A., Lee, B., Nyborg, K., Pradhan, P., Bonnett, G. D., Bryan, B. A., Campbell, B. M., Christensen, S., Clark, M., Cook, M. T., de Boer, I. J. M., Downs, C., …West, P. C. (2020). Innovation can accelerate the transition towards a sustainable food system. Nature Food,1(5), 266–272. https://doi.org/10.1038/s43016-0200074-1 Hosmer, D. W., & Lemeshow, S. (1980). Goodness of fit tests for the multiple logistic regression model. Communications in Statistics - Theory and Methods,9(10), 1043–1069. https:// doi.org/10.1080/03610928008827941 Jaghdani, T. J., & Brümmer, B. (2016). Determinants of water purchases by pistachio producers in an informal groundwater market: A case study from Iran. Water Policy,18(3), 599– 618. https://doi.org/10.2166/wp.2015.093 Jalal-Ud-Din, M. (2011). The socio-economic problems of small farmers in adopting new agricultural technology: A case study of three villages in district Mardan. Sarhad Journal of Agriculture,27(2), 299–304. https://www.aup.edu.pk/sj_pdf/ 23-THE SOCIO-ECONOMIC PROBLEMS OF SMALL FARMERS IN.pdf James, C. (2012). Global Status of Commercialized Biotech/GM Crops: 2012. In ISAAA Brief No. 44. ISAAA. International Service for the Acquisition of Agri-biotech Applications (ISAAA). https://ifs.org.uk/publications/15065 Kassie, M., Shiferaw, B., & Muricho, G. (2011). Agricultural technology, crop income, and poverty alleviation in Uganda. World Development,39(10), 1784–1795. https://doi.org/10. 1016/j.worlddev.2011.04.023 Kassie, M., Zikhali, P., Manjur, K., & Edwards, S. (2009). Adoption of sustainable agriculture practices: Evidence from a semi-arid region of Ethiopia. Natural Resources 16 M. BILAL AND T. J. JAGHDANI Forum,33(3), 189–198. https://doi.org/10.1111/j.1477-8947. 2009.01224.x Khan, M. H., & Imam, S. F. (1985). Public policy and agricultural transformation in Pakistan. The Pakistan Development Review, 24(3/4), 305–332. https://www.jstor.org/stable/41258714 Kotu, B. H., Alene, A., Manyong, V., Hoeschle-Zeledon, I., & Larbi, A. (2017). Adoption and impacts of sustainable intensification practices in Ghana. International Journal of Agricultural Sustainability,15(5), 539–554. https://doi.org/10.1080/ 14735903.2017.1369619 Lee, D. (2005). Agricultural sustainability and technology adoption: Issues and policies for developing countries. American Journal of Agricultural Economics,87(5), 1325–1334. https:// doi.org/10.1111/j.1467-8276.2005.00826.x Malik, S. J., Sheikh, A. T., & Jilani, A. H. (2016). Inclusive agricultural growth in Pakistan-understanding some basic constraints. Pakistan Development Review,55(4), 889–903. https://doi.org/10.30541/v55i4I-IIpp.889-903 Marenya, P. P., & Barrett, C. B. (2009). State-conditional fertilizer yield response on Western Kenyan farms. American Journal of Agricultural Economics,91(4), 991–1006. https://doi.org/10. 1111/j.1467-8276.2009.01313.x Mendola, M. (2007). Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh. Food Policy,32(3), 372–393. https://doi. org/10.1016/j.foodpol.2006.07.003 Mesfin, H. M., Ahmed, M. H., & Abady, S. (2016). Determinants of multiple groundnut technology adoption in eastern Ethiopia. Review of Agricultural and Applied Economics,19(02), 51–60. https://doi.org/10.15414/raae/2016.19.02.51-60 Möhring, N., & Finger, R. (2022). Pesticide-free but not organic: Adoption of a large-scale wheat production standard in Switzerland. Food Policy,106, 102188. https://doi.org/10. 1016/j.foodpol.2021.102188 Montt, G., & Luu, T. (2020). Does conservation agriculture change labour requirements? Evidence of Sustainable Intensification in Sub-Saharan Africa. Journal of Agricultural Economics,71(2), 556–580. https://doi.org/10.1111/1477-9552.12353 Muhammad, F., Hassan, M. A., & Mehmood, T. (2020). An overview of the existing problems faced by Pakistani women in agriculture: Conclusion and recommendation. Ecofeminism and Climate Change,1(2), 97–104. https://doi.org/10.1108/ EFCC-04-2020-0010 Nabuuma, D., Reimers, C., Hoang, K. T., Stomph, T. J., Swaans, K., & Raneri, J. E. (2022). Impact of seed system interventions on food and nutrition security in lowand middle-income countries: A scoping review. Global Food Security,33, 100638. https://doi.org/10.1016/j.gfs.2022.100638 Noltze, M., Schwarze, S., & Qaim, M. (2013). Impacts of natural resource management technologies on agricultural yield and household income: The system of rice intensification in Timor Leste. Ecological Economics,85,59–68. https://doi. org/10.1016/j.ecolecon.2012.10.009 Nyangena, W., & Juma, O. M. (2014). Impact of improved farm technologies on yields: The case of improved maize varieties and inorganic fertilizer in Kenya. In RFF Working Paper Series, dp14-02-efd. Resources for the Future. https://www.efdinitiative. org/publications/impact-improved-farm-technologies-yieldscase-improved-maize-varieties-and-inorganic Ochieng, J., Afari-Sefa, V., Muthoni, F., Kansiime, M., HoeschleZeledon, I., Bekunda, M., & Thomas, D. (2022). Adoption of sustainable agricultural technologies for vegetable production in rural Tanzania: Trade-offs, complementarities and diffusion. International Journal of Agricultural Sustainability, 20(4), 478–496. https://doi.org/10.1080/14735903.2021. 1943235 Pregibon,D.(1980). Goodness of link tests for generalized linear models. Journal of the Royal Statistical Society Series C: Applied Statistics,29(1), 15–24. https://doi.org/10.2307/ 2346405 Pretty, J. (2018). Intensification for redesigned and sustainable agricultural systems. Science,362(6417), 1–7. https://doi.org/ 10.1126/science.aav0294 Puech, T., & Stark, F. (2023). Diversification of an integrated crop-livestock system: Agroecological and food production assessment at farm scale. Agriculture, Ecosystems & Environment,344, 108300. https://doi.org/10.1016/j.agee. 2022.108300 Ragasa, C., & Mazunda, J. (2018). The impact of agricultural extension services in the context of a heavily subsidized input system: The case of Malawi. World Development,105, 25–47. https://doi.org/10.1016/j.worlddev.2017.12.004 Rahm, M. R., & Huffman, W. E. (1984). The adoption of reduced tillage: The role of human capital and other variables. American Journal of Agricultural Economics,66(4), 405–413. https://doi.org/10.2307/1240918 Ridley, M. (2020). How Innovation Works (Vol. 1, 1st ed.). 4th Estate. https://www.4thestate.co.uk/book/how-innovationworks-9780008334819/ Rogers, E. M. (1962). Diffusion of innovations (Vol. 1, 1st ed.). Free Press of Glencoe. Rosário, J., Madureira, L., Marques, C., & Silva, R. (2022). Understanding farmers’adoption of sustainable agriculture innovations: A systematic literature review. Agronomy,12 (11), 2879. https://doi.org/10.3390/agronomy12112879 Ruzzante, S., Labarta, R., & Bilton, A. (2021). Adoption of agricultural technology in the developing world: A meta-analysis of the empirical literature. World Development,146, 105599. https://doi.org/10.1016/j.worlddev.2021.105599 Shrivastava, B., Jain, K. K., Kalra, A., & Kuhad, R. C. (2014). Bioprocessing of wheat straw into nutritionally rich and digested cattle feed. Scientific Reports,4(1), 1–9. https://doi. org/10.1038/srep06360 Silvestri, S., Richard, M., Edward, B., Dharmesh, G., & Dannie, R. (2021). Going digital in agriculture: How radio and SMS can scale-up smallholder participation in legume-based sustainable agricultural intensification practices and technologies in Tanzania. International Journal of Agricultural Sustainability,19(5–6), 583–594. https://doi.org/10.1080/ 14735903.2020.1750796 Simtowe, F., Asfaw, S., & Abate, T. (2016). Determinants of agricultural technology adoption under partial population awareness: The case of pigeonpea in Malawi. Agricultural and Food Economics,4(1), 1–21. https://doi.org/10.1186/ s40100-016-0051-z Tadjiev, A., Djanibekov, N., & Herzfeld, T. (2023). Does zero tillage save or increase production costs? Evidence from smallholders in Kyrgyzstan. International Journal of Agricultural Sustainability,21(1), 1–16. https://doi.org/10.1080/14735903. 2023.2270191 Takahashi, K., Muraoka, R., & Otsuka, K. (2020). Technology adoption, impact, and extension in developing countries’ INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 17 agriculture: A review of the recent literature. Agricultural Economics,51(1), 31–45. https://doi.org/10.1111/agec.12539 Teklewold, H., Kassie, M., & Shiferaw, B. (2013). Adoption of multiple sustainable agricultural practices in rural Ethiopia. Journal of Agricultural Economics,64(3), 597–623. https:// doi.org/10.1111/1477-9552.12011 Thapa, G., & Gaiha, R. (2014). Smallholder farming in Asia and the pacific: Challenges and opportunities. In P. B. R. Hazell, & A. Rahman (Eds.), New directions for smallholder agriculture (1st ed., pp. 69–114). Oxford University Press. https://doi.org/10.1093/ACPROF:OSO/ 9780199689347.003.0004 Wauters, E., & Mathijs, E. (2014). The adoption of farm level soil conservation practices in developed countries: A meta-analytic review. International Journal of Agricultural Resources, Governance and Ecology,10(1), 78–102. https://doi.org/10. 1504/IJARGE.2014.061058 Wheeler, S., Bjornlund, H., Shanahan, M., & Zuo, A. (2009). Who trades water allocations? Evidence of the characteristics of early adopters in the Goulburn-Murray Irrigation District, Australia 1998-1999*. Agricultural Economics,40(6), 631–643. https://doi.org/10.1111/j.1574-0862.2009.00404.x Wickham, H. (2016). Ggplot2: Elegant graphics for data analysis. Springer Nature. https://doi.org/10.1007/978-3-319-24277-4 Wiggins, S., & Keats, S. (2013). Smallholder agriculture’s contribution to better nutrition. In Report commissioned by the Hunger Alliance. Overseas Development Institute (ODI). https://odi.org/en/publications/smallholder-agriculturescontribution-to-better-nutrition/ Zhong, W., Chen, Y., & Xie, L. (2023). How does internet use promote joint adoption of sustainable agricultural practices? Evidence from rice farmers in China. International Journal of Agricultural Sustainability,21(1), 1–16. https://doi.org/10. 1080/14735903.2023.2270244 Appendix Robustness test for four probit models that consist the multivariate probit model are provided below. Table A1. Estimates of the probit models. No-tillage Coefficient Robust SE Education in years 0.020 0.020 No of agri-extension visits (numbers) 0.148 0.090 Access to credit (yes = 1; no = 0) −0.056 0.174 Laser level (yes = 1; no = 0) 0.829*** 0.222 Farm machinery index 0.347 0.289 Off-farm income (yes = 1; no = 0) −0.233 0.168 Area sowed under wheat (ha) −0.613*** 0.205 Area sowed under cotton (ha) 0.928*** 0.173 No of milch animals 0.042 0.053 Number of observations 275 Bayesian crit. (BIC) 382.383 Prob > x 2(9) 49.438*** Akaike crit. (AIC) 346.215 Link test hatsq p-value 0.417 Hosmer–Lemeshow test x 2(8) = 9.68 Prob > x 2= 0.2885 Notes: *** p< .01, ** p< .05, * p< .1. Table A2. Estimates of the probit models. Bt cotton seed Coefficient Robust SE Education in years 0.065*** 0.024 No of agri-extension visits (numbers) 0.164* 0.098 Access to credit (yes = 1; no = 0) 0.074 0.196 Farm distance to input market (km) −0.024 0.015 Farm distance to output market (km) −0.003 0.011 Farm distance to the main road (km) −0.004 0.103 Laser level (yes = 1; no = 0) 0.481** 0.249 Farm machinery index 0.502 0.362 Offfarm income (yes = 1; no = 0) 0.410** 0.194 Area sowed under wheat (ha) −1.250*** 0.292 Area sowed under cotton (ha) 2.018*** 0.280 Membership in a farmers’union (yes = 1; no = 0) −0.090 0.346 No of milch animals 0.0350 0.061 Number of observations 275 Bayesian crit. (BIC) 315.777 Prob > x 2(13) 80.265*** Akaike crit. (AIC) 265.142 Hosmer–Lemeshow test x 2(8) = 10.56 Prob > x 2= 0.22 *** p< .01, ** p< .05, * p< .1. 18 M. BILAL AND T. J. JAGHDANI Table A3. Estimates of the probit models. High-quality wheat seed Coefficient Robust SE Education in years 0.031 0.021 No of agri-extension visits (numbers) 0.113 0.099 Access to credit (yes = 1; no = 0) 0.011 0.176 Farm distance to input market (km) 0.001 0.013 Farm distance to output market (km) −0.019* 0.011 Farm distance to the main road (km) −0.086 0.083 Laser level (yes = 1; no = 0) 0.314 0.216 Farm machinery index 0.567* 0.327 Offfarm income (yes = 1; no = 0) 0.467*** 0.177 Area sowed under wheat (ha) 0.252 0.210 Area sowed under cotton (ha) 0.558*** 0.187 Membership in a farmers’union (yes = 1; no = 0) −0.122 0.301 No of milch animals 0.109** 0.055 Number of observations 275 Bayesian crit. (BIC) 393.182 Prob > x 2(13) 56.371*** Akaike crit. (AIC) 342.547 Link test hatsq p-value 0.954 Hosmer–Lemeshow test x 2(8) = 8.21 Prob > x 2= 0.41 *** p< .01, ** p< .05, * p< .1. Table A4. Estimates of the probit models. Herbicides of cutting-edge technology Coefficient Robust SE Education in years 0.070*** 0.021 No of agri-extension visits (numbers) 0.307*** 0.099 Access to credit (yes = 1; no = 0) −0.144 0.181 Farm distance to input market (km) 0.001 0.013 Farm distance to output market (km) −0.003 0.011 Farm distance to the main road (km) −0.125 0.082 Laser level (yes = 1; no = 0) 0.400* 0.218 Farm machinery index 0.578* 0.321 Offfarm income (yes = 1; no = 0) 0.352** 0.179 Area sowed under wheat (ha) −0.372* 0.208 Area sowed under cotton (ha) 0.673*** 0.190 Membership in a farmers’union (yes = 1; no = 0) −0.058 0.296 No of milch animals −0.016 0.052 Number of observations 275 Bayesian crit. (BIC) 392.128 Prob > x 2(13) 51.683*** Akaike crit. (AIC) 341.493 Link test hatsq p-value 0.245 Hosmer–Lemeshow test x 2(8) = 5.32 Prob > x 2= 0.72 Notes: *** p< .01, ** p< .05, * p< .1. INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 19