Recuperating dynamism in agriculture through adoption of sustainable agricultural technology - Implications for cleaner production

Journal of Cleaner Production 232 (2019) 639e647

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Recuperating dynamism in agriculture through adoption of sustainable agricultural technology - Implications for cleaner production Gershom Endelani Mwalupaso a, b, c, Mariko Korotoumou a, b, Aseres Mamo Eshetie a, b, John-Philippe Essiagnon Alavo a, b, Xu Tian a, b, * a b c

College of Economics and Management, Nanjing Agricultural University, No. 1 Weigang, Nanjing, 210095, PR China China Center for Food Security Studies, Nanjing Agricultural University Nanjing, 210095, PR China Department of Agriculture and Agribusiness, Prince G Academy and Consultancy, Kabwe, 10101, Zambia

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 April 2019 Received in revised form 27 May 2019 Accepted 29 May 2019 Available online 30 May 2019

Sustainable agricultural technologies are being touted as a requirement for a sustainable world in many parts of the globe. Consequently, they have become a critical issue in the development policy agenda for Sub-Saharan Africa. Despite several studies conducted on the adoption of Sustainable agricultural technologies, they remain poorly understood in Mali. Thus, research that could inform policies capable of simultaneously addressing low agricultural productivity and environmental degradation is obstructed. To begin to fill this research gap, we use cross-sectional data from rice farmers in Mali. Stochastic production frontier is adopted for rice production and technical efficiency analysis in a one-step estimation using maximum likelihood method. The results reveal that adoption of the system of rice intensification, a sustainable agricultural technology, is consonant with cleaner production concept. Particularly, adopters are more technically efficient than non-adopters. The policy implication is that, if all farmers adopted system of rice intensification, their efficiency would increase by 17% while waste in production would reduce to 4.8%. Therefore, our study puts forward substantial empirical evidence to encourage the adoption of system of rice intensification as it could eventually enhance agricultural sustainability. © 2019 Elsevier Ltd. All rights reserved.

Keywords: Stochastic production frontier System of rice intensification Technical efficiency Rice production Sustainable agriculture Mali

1. Introduction The diffusion and adoption of sustainable agricultural technologies (SATs) in Sub-Saharan Africa (SSA) has suddenly become a critical issue in the development-policy agenda (Teklewold et al., 2013). Notably, the introduction of SATs is beheld as a win-win strategy given its potential to simultaneously confront cropland degradation, low agricultural productivity, and poverty. As global population burgeons (WB, 2016), this is linked to a sizeable increase in food demand as well as excessive environmental problems on account of rigorous agricultural productions. For this reason, attaining sustainable agriculture and food security remains one of the major challenges worldwide. While the latter aims at ensuring a healthy and constant supply of food over time, sustainable

* Corresponding author. China Center for Food Security Studies, Nanjing Agricultural University Nanjing, 210095, PR China. E-mail address: [email protected] (X. Tian). https://doi.org/10.1016/j.jclepro.2019.05.366 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

agriculture's role is crucial in the maintenance of resilient agroecosystems (Skaf et al., 2019). Therefore, given the current environmental challenges, adoption of SATs is imperative to attain improved food security status via agricultural productivity. Fascinatingly, adoption rates of SATs are still below expected levels (Kassie et al., 2013). One of the most promising SATs extensively used in rice production is the system of rice intensification (SRI) (Styger et al., 2011a,b). It is claimed to be a novel, more productive and sustainable method founded on the principles of using mechanical weed control, compost and intermittent irrigation on single and young transplants with wide spacing (Uphoff et al., 2011). Since its origin in the early 1980s in Madagascar, it has widely and rapidly spread to forty nations (Uphoff, 2008) despite lack of unequivocal and clear endorsement by science (Glover, 2011). Compared to African countries, the adoption of SRI has been more rapid in Asia given that it is the world's major rice-growing region. Nevertheless, in recent years, as rice consumption multiplies, adoption of SRI has been recurrent at policy debates of some African countries. In view

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of the increased rice imports due to the gradual decline of selfsufficiency ratio, SRI approaches have prompted a dramatic shift in the perception and comprehension regarding how to realize a productive and sustainable rice cropping systems (Styger et al., 2011a,b). In Mali, a significant deficit exists between the current rice production and consumption, triggered by the rapid population growth, rising income, dietary diversification, and urbanization (Audibert, 1997). Also, decreasing land fertility and erratic rainfall, further compound the problem leading to increased rice importation yearly. In response, innovative programs were instigated to improve self-sufficiency and increase the productivity of rice farmers through the provision of seed and fertilizer subsidies (Bagayoko, 2012). To the disappointment of many, such programs have resulted in overuse of inputs, and environmental degradation without increasing rice productivity. Rice is considered a staple food in the country and both its production and productivity has serious policy implication. Despite rice production and area cultivated increasing since 1990, productivity has been fluctuating drastically (Fig. 1). This calls the production technology used by rice farmers to question. Consequently, stimulating farmers to initiate a managementbased strategy for raising cropland productivity is the desired state. SRI is a set of principles and biophysical mechanisms (for the most part) as detailed in Stoop et al. (2002), designed to increase rice yields. At present, the validity of its concepts and practices has been proven in more than 20 countries (Satyanarayana et al., 2004). Consistent with its objective, paddy yields were higher using SRI than conventional cultivation in India (Sinha and Talati, 2007), Cambodia (Anthofer, 2004), Sri Lanka (Namara et al., 2003), Philippines (Lazaro, 2004), China (Yuan, 2002), Indonesia (Sato, 2006), Bangladesh (Latif et al., 2005), Madagascar (Uphoff, 1999), Tanzania (Katambara et al., 2013) and Mali (Styger et al., 2011a,b). In support, SRI proponents emphasize that in addition to attaining average increased yields in the range of 20e200%, SRI simultaneously reduces emissions and increases carbon sink activity (Thakur et al., 2016), and improves resistance to environmental stresses (Styger and Uphoff, 2016). Indisputably, this makes SRI a triple-win methodology for climate security, agriculture, and food security (Nyasimi et al., 2014). On the other hand, opponents contend that measurement error and missing supporting information are the reasons behind the reported high yields or so-called ‘fantastic yields’ (Sinha and Talati, 2007). For instance, scientific questions regarding SRI's impacts on resource use remain unanswered. Thus, up till now, the debate regarding the putative benefits of SRI adoption has not convinced adherents of the opposing side because they claim that only

theoretical or speculative analysis is presented. Interestingly, appropriate empirical data is currently available to place SRI adoption in a meaningful context by evaluating SRI production approach with respect to cleaner production concept. In this study, we regard this concept as one that aims at preventing the production of waste (in terms of lower output than expected from a fixed set of resources), while increasing efficiency in the use of agricultural inputs. Taking into consideration the aforementioned and the definition of efficiency by Koopmans (1951) that it is the act of achieving good results with little waste of resources, adoption of SRI could have some ‘cleaner production’ implications. In economic theory, for every given amount of inputs, there is a maximum attainable output, and this is referred to as technical efficiency. Any deviations are an indication of inefficiency or waste in terms of untapped potential to increase output. Mindful of this, efficient resource use could be guaranteed through SRI adoption especially against the discourse by Styger et al. (2011a,b) that adoption significantly reduces seeds, water, and chemical inputs requirements. Particularly, by changing how water, nutrients, soil, and rice plants are managed, adopters are expected to be more technically efficient than their counterparts (Takahashi, 2013). Therefore, if adoption of SRI could lead to relatively less waste of resources than their counterparts, we could conclude that SRI production is ‘cleaner’. However, such analysis has never benefitted from empirical evaluation, and this has occasioned policymakers' sluggishness on promoting the adoption of SRI practices. Therefore, to comprehend the implication of SRI adoption in more depth, the objectives of the study are twofold. First, we establish the association of SRI adoption and waste reduction in production. Second, we examine the factors that influence the adoption probability of SRI. It is not within the scope of the study to fire another salvo in the ‘rice wars’, by either applauding or condemning individuals or organizations for attacking or endorsing SRI. Neither are we using the study to adjudicate on SRI's scientific validity. On the contrary, having recognized SRI as a social fact in rice farming, we focus on the implication of SRI adoption on cleaner production in agriculture. The study contributes to the literature in three aspects. First, through efficiency analysis, we explore the relationship between SRI adoption and cleaner production in rice production. This sits very well with the sustainable development goals (SDGs) which advocate for thoughtful change in food production to ensure global food demands are met. Second, multidisciplinary approach and dynamic econometric framework are applied in the investigation, leading to consistent and unbiased empirical evidence for policymakers and development practitioners. Finally, to the best of the

Fig. 1. Rice production and yield in Mali (1960e2018) Source (FAOSTAT, 2018).

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2. Material and methods

constructing the questionnaire using common layouts for productivity studies, training the team of enumerators and pre-testing the instrument in the local setting. Most of the interrogations related to technical efficiency included the size of land for rice cultivation, hours of labor used and the quantities of fertilizers and seeds, which were easy to answer for farmers. We do not anticipate methodical discrepancies in the precision of the answers among adopters or non-adopters, so that measurement error should not culminate in bias in the estimation.

2.1. Data

2.2. Measurement of key variables

We use data extracted from a household survey conducted in 2018 cropping season in Mali's San west plain commonly named ARPASO (an acronym for the French name Association des Riziculteurs de la Plaine Am
enag
ee de San Ouest). Climatic conditions in the study area are reasonably fair with annual precipitation varying between 400 and 700 mm, and the average temperature is between 16
C and 39
C. Rice is the principal crop grown, and farmers in this region significantly contribute to the national food basket. Since 2007, some farmers in this area have adopted SRI although the majority use the conventional approach. Regarding information acquisition on SRI adoption, the situation in the study area is the same as revealed by Uphoff (2007) - the spread is spontaneous from one farmer to another. Apart from farmers, information about SRI is also disseminated by agricultural extension officers and cooperatives. Some non-governmental organizations (NGOs) also promote this method through on and off-farm training, provision of input subsidies, and credit sourcing after assisting farmers with insurance registration. Training in this community is voluntary. Thus, in most cases, it is not the primary source of information as some farmers may already have adopted SRI even prior to attending the training. Since SRI allows for farmers to take advantage of resources at their disposal, they find it to be beneficial and accessible. Nevertheless, the reasoning is that farmers that choose to participate in SRI training could be more disposed to use SRI effectively in their farming operation. A two-stage sampling procedure was employed for sample se^ , Demba, Tiekelenso, and San) were lection. First, four zones (Bo chosen based on SRI adoption and easy accessibility to the area. Second, 208 farmers (104 adopters and non-adopters) were selected using a list of registered farmer obtained from ARPASO and the regional sector of agriculture. Using a pre-tested and structured questionnaire, household heads were interviewed by well-trained and experienced enumerators (this exercise took about three months to complete). This study received ethical approval from the Ethics Committee of the School of Economics and Management, Nanjing Agricultural University, which bases its foundations on the 1964 Helsinki declaration and also from the local government (sous prevet of San district). All participating farmers orally consented to their involvement witnessed by Ministry of agriculture officers who helped identify the farmers that were randomly selected. Luckily, all recruited farmers agreed to participate and to compensate for their time, without their knowledge, we recommended them to NGOs for consideration for input subsidies and training. The questionnaire focused on agricultural inputs and other socio-demographic details. Adoption of SRI was explicitly asked in order to distinguish the two groups correctly. To ensure farmers understood SRI, we used the local name given to the methodology, ‘Kelenkelen turu’ meaning ‘one hole one plant’. Also, in the interest of quicker and honest response, appointments were made to suit farmers' most convenient time, we designed the instrument in a way that participants remain anonymous and avoided too many open questions. We tried to minimize measurement error by cautiously

Technical efficiency of rice farmers is the outcome variable of interest which is estimated from stochastic production frontier (SPF) analysis while adoption of SRI, a SAT, is the primary independent variable captured through a dummy where 1 is an adopter and 0 otherwise. Interestingly, the latter is also an outcome of interest when estimating factors likely to impact on SRI adoption.

researchers’ knowledge, there is paucity in the literature on this topic, yet it is a critical subject that policymakers need to understand. The results of this study, therefore, provide a useful demonstration of how the adoption of sustainable agricultural methodology can translate to a reduction in agricultural input wastage which could potentially enhance agricultural sustainability.

2.3. Conceptual framework Field-based validation of SRI reveals that the methodology makes good use of resources as it is a labor intensive, low water approach making use of single-spaced younger seedlings and unique hand weeding tool. It is believed to provide optimal growing conditions leading to growth acceleration (Nemoto et al., 1995), tiller mortality reduction and a healthier and stronger root system with potential enhancements for nutrient uptake. Typically, the summarized technical components of SRI concerns the methods of weed control, water management, and crop establishment (Stoop et al., 2002), combined with the use of organic fertilizer and soil aeration (Uphoff, 2007). Implementation of such a procedure is claimed to lead to more efficient resource use as well as remarkable yields. However, these findings have been denounced because of unconfirmed field observations (‘UFOs’) (Sinclair and Cassman, 2004), measurement error (Sheehy et al., 2004), and ‘nonsense’ and ‘nonscience’ (Sheehy et al., 2005). In agricultural production, efficiency is at the core because the output can be expanded and sustained through efficient use of resources. For that reason, efficiency continues to be an essential topic especially in nations where farmers are resource poor. Thus, researchers and policymakers use empirical examination to monitor the progress of any innovation. In this study, we make use of technical efficiency to evaluate SRI adoption. According to economic theory, given the existing technology, a production function is described with reference to the maximum output obtainable from a fixed set of inputs (Koopmans, 1951). When a producer attains this, they are deemed as technically efficient as there is zero waste in production (Lovell, 1993). One of the fundamental aspects of technical efficiency is the adoption of a production technology (SRI adoption in this case) which works as a double-edged sword e improving efficiency in the use of inputs but reduces waste in production. Bearing in mind the three underlying principles that SRI fulfills as highlighted by Uphoff (2003), i.e. (i) optimizing competition for sunlight, soil nutrients, and growing space among rice plants; (ii) capitalize on the vigor of young seedlings and; (iii) fostering aerobic soil conditions to boost healthy root systems and trigger soil microbial activity, technical efficiency analysis of SRI adopters and non-adopters could have useful and insightful repercussions for achieving cleaner production as well as agricultural sustainability. While field evidence suggesting that SRI significantly increases the productivity of capital, labor and land has been questioned, sufficient data is now available to conduct empirical analysis leading to cleaner production implications that transcend

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individual experiment limitations (Fried et al., 1993). The primary question addressed in this study is: Does the adoption of SRI innovation in rice production concur with the concept of cleaner production? In an effort to accurately address the question, we establish a framework (Fig. 2) and postulate that producers have a fixed set of inputs but use different production technologies. In this case, the expected ratio between maximum output and observed output, given a specified set of inputs is 1 (Lovell, 1993). Accordingly, inefficiency occurs when the output produced is less than what is possible given the inputs and available production technology. To derive some useful cleaner production implications, we compare the difference in technical efficiency between the two groups to ascertain the increase in efficiency owing to SRI adoption. Also, the individual difference of each group's technical efficiency from the maximum score is calculated d this aids to elucidate whether SRI adoption averts waste in production while increasing efficiency in resource use. 2.4. Analytical framework and Strategy A one-step stochastic production function using maximum likelihood estimation is adopted for rice production analysis inclusive of technical efficiency determinants while a binary Logit function is employed to model factors influencing SRI adoption. All statistics were implemented in stata (version 14; Stata Corporation, College Station, TX, USA). 2.4.1. Production function Using the stochastic frontier analysis (SFA) in production and productivity evaluation has substantially been extended after its development. SFA is a technique of economic modeling simultaneously introduced by Meeusen and van Den Broeck (1977) and Aigner et al. (1977). In this study, we adopt the production model proposed by Wang and Schmidt (2002). In comparison to the model advanced by Battese and Coelli (1995), this specification is considered flexible because of its ability to relax the identical distribution assumption. The following is the basic specification of the model:

Yi ¼ f ðxi ; bÞ exp ðvi Þ exp ð  ui Þ;

(1)

TEi ¼

Y ¼ expðui Þ; fðxi ; bÞ

(2)

Where Yi is the rice output harvested by the ith farmer, xi is a vector of inputs used in production by the ith farmer, b is the parameter to be estimated, vi is the random errors and ui is the technical inefficiency. In equation (2), technical efficiency is given as the exponential of inefficiency with values ranging 0 to 1. Considering the various production functions used in productivity analysis, we conduct a model specification test using the likelihood ratio (LR) test described as follows:

LR ¼  2½lnfLðHA Þg  lnfLðH0 Þg;

(3)

Where LðHA and LðH0 Þ are the values of the likelihood function under the alternative and null hypotheses respectively. Two popular production functions (Translog and CD) are considered for model specification, and the result (insignificant LR test result; refer to Table 2) is in favor of Cobb-Douglas (CD) production function which is specified as follows;

lnYi ¼ b0 þ bi

4 X

lnXi þ vi  ui ;

(4)

i¼1

Ui ¼ b0 þ b1 SRIi þ

4 X

bi Mi þ zi ;

(5)

i¼1

where Yi is the output of rice harvested by the ith farmer, xi is a vector of four inputs (seed, land, fertilizer and labor) used in production by the ith farmer, bi , b0 and b1 are the parameter to be estimated, vi is the random errors and ui is the technical inefficiency which is influenced by adoption of SRI (SRIi ) and Mi which is a vector of variable such as training, farming experience, education and age of farmers; zi is the error term of the inefficiency model. For robust estimates using the CD production function, we adopted the maximum likelihood estimation procedure (MLE) which allows for the estimation of the parameters in the frontier and the inefficiency model simultaneously. This approach has the advantage of not contradicting the distribution assumption of the inefficiency effects. Also, in the interest for consistent and unbiased

Fig. 2. Conceptual framework.

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Table 1 Descriptive statistics. Variable Type Continuous Variable Age Family Size Farming Experience Dummy Variable Gender Education Training Credit Equipment Water Control

Mean (N ¼ 208)

Std.Dev.

Min

Max

Description

50.726 13.563 22.981

11.653 6.454 11.732

22 4 3

85 35 50

Age of the household head Number of people in a farm household Years of household head's farming experience in rice production Being male or female (1 ¼ male) Household head can read and write (1 ¼ can read and write) Member of a household has attended rice production training (1 ¼ attended) Household head has access to credit (1 ¼ access) Household owns weeding equipment (1 ¼ owns equipment) Household has water control (1 ¼ has control)

87.98 50.96 63.46 96.63 66.83 79.81

Note: The values on dummy variables are percentages for the category in parenthesis.

Table 2 SFA Maximum Likelihood estimates. Stochastic Frontier Production Function

Technical Inefficiency Function

Rice Production Output Labor Organic Fertilizer Chemical Fertilizer Land Seeds Constant

Coefficient (Std.Err.) 0.090 (0.055)* 0.009 (0.010) 0.104 (0.033)*** 0.847 (0.044)*** 0.011 (0.010) 0.127 (0.021)***

Explanatory Variables Age Family Size Farming Experience Gender Education Training Credit SRI adoption Water Control Equipment Constant

Coefficient (Std.Err.) 0.186 (0.089) ** 0.046 (0.133) 0.088 (0.104) 0.001 (0.026) 0.001 (0.003) 0.033 (0.039) 0.923 (0.029)** 0.148 (0.038)*** 0.766 (1.835) 0.807 (0.406) 7.925 (3.126)***

Model Diagnostics Scale elasticity Log-likelihood Wald chi2 Mean Observations

1.017 52.40 1097.69*** 0.867 208

Sigma_u sqr Sigma_v sqr Lambda LR Test Endogeneity (F-value)

1.491 (0.143)*** 0.125 (0.010)*** 11.906 (0.145)*** 0.791 1.32

Notes: Figures in parentheses are standard errors of the coefficient, while *, **, and *** indicate statistical significance levels at 10%, 5%, and 1%, respectively.

estimates, we addressed endogeneity using the approach employed in Roco et al. (2017) which makes use of the Hausman test.

2.4.2. Binary logit function To examine the factors that influence SRI adoption, an econometric binary Logit model was employed, which is derived from a latent-variable model specified as follows:

SRIi ¼

8 <

1 :0

y*i ¼ a þ Xi b þ ui > 0 ; otherwise

(6)

where y*i is the unobserved latent variable which if greater than zero, ith farmer is likely to be an adopter, Xi is a vector of explanatory variables that impact on SRI adoption, a is a constant and ui is the error tem following a standard logistic distribution. In an attempt to dodge the limitations of the linear probability function, we adopt the following binary response model:

probðSRIi ¼ 1jxÞ ¼ Lða þ Xi bÞ ¼ LðzÞ

(7)

where L is a non-linear function from the family of a logistic function taking values in the range 0 < LðzÞ < 1 and z is a linear function of the explanatory variables. This leads to Equation (8) which is used for calculating predicted probabilities and is interpreted as the probability that SRI ¼ 1 (adoption of SRI) where LðzÞ is a cumulative logistic function for a random variable.

probðSRIi ¼ 1jxÞ ¼ Lða þ Xi bÞ ¼ LðzÞ ¼ lnðoddsi Þ ¼ a þ Xi b

exp ðzÞ ¼ LðzÞ 1 þ exp ðzÞ

(8) (9)

Subsequently, Equation (9) is derived where the dependent variable refers to the Logit of SRI adoption for the ith farmer and coefficient b measures the effect of a one-unit change in Xi on the dependent variable (ceteris paribus). For robust estimates, variance inflation factor (VIF) and pairwise correlation are estimated to rule out any possibility of multicollinearity of the variables. The model specification test reveals that multicollinearity is exempt from the estimation given that highest VIF (2.072) is less than 10 and is the highest correlation coefficient (0.684) is less than 0.8 as guided by Wooldridge (2015) and Damodar (2004) respectively (refer to Appendix A: Table A1 and A2).

3. Results and discussion 3.1. Descriptive Statistics Table 1 presents the unconditional statistics of the explanatory variables used in the inefficiency and the Logit model. Majority of farmers have lived more than five decades and have relatively large families. This is good as a family is a good source of labor for rural households in rice production (Lopez-Ridaura et al., 2018). The

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families in this community consist but not limited to parents, couple, siblings and other distant relations. Usually, the family size is large considering most family members settle in one place for agricultural purposes. In terms of gender participation, more males than females participated in the study with reasonably good farming experience in rice production. For education, we consider a farmer to have attained basic education if they can read and write in the local language (OpokuAmankwa and Brew-Hammond, 2011). We find that about half of the farmers have attained basic education and also attended training on rice production. The ability to read and write aids the farmer to fully comprehend agricultural information and also make proactive searches in an effort to improve their productivity (Chevalier et al., 2004). Finally, most farmers have access to credit, own equipment for weeding and have full control of water. In our case, full water control implies being able to access water on the plot easily. For instance, farmers near the canal or those making use of irrigation have full water control on their plots while those that greatly rely on rains do not. Water control is essential in rice production, and an assessment of whether this affects the efficiency of farmers enrich the discussion on rice production in developing countries (Ali and Talukder, 2008). The discussed summary statistics are essential for production inefficiency analysis (Tian et al., 2015). 3.2. Association of SRI adoption and technical efficiency Table 2 presents the results from the one-step SFA with the frontier model on the left and the inefficiency model on the right. The model diagnostics characteristics are also presented at the bottom of the table to give credibility of the estimation. The endogeneity test was insignificant which implies that endogeneity was not a problem in our estimation (Davidson and MacKinnon, 1995). The value of lambda is significantly higher than zero, indicating the relevance of technical inefficiency in the model. All the observations were used in the estimation, and scale elasticity equal to 1 indicates constant returns to scale similar to Mwalupaso et al. (2019). The mean technical efficiency for non-adopter and adopters imply that there is 13.3 percent waste in production in terms of untapped potential to increase output to the maximum obtainable output from the given inputs (Fried et al., 1993). Against the background of attaining a sustainable world (Ren et al., 2019), this loss in output is critical as it would help in reducing the impact of food insecurity on society (Skaf et al., 2019). We also find that collectively (adopters and non-adopters), land is the major factor of production while organic fertilizer and seeds seem to be overutilized. It is no surprise that some inputs may be used in excessive amounts as smallholders are fond of such actions to maximize agricultural production (Ju et al., 2016). To adequately understand how each respective groups use resources, technical efficiency is very insightful (Bojnec and Latruffe, 2013) as technology adoption is an essential determinant (Syp et al., 2015). We find that SRI adoption is positively and significantly associated with technical efficiency. Other significant factors are the age of the household head, and access to credit. Older rice farmers are less efficient consistent with Rahman (2010). Mindful of the fact that this variable was processed in the estimation while holding family size constant, one possible explanation is that as farmers age, they may not have enough time to manage the production process efficiently. The other justification is that older farmers are less inclined to try new approaches based on their farming experience with traditional methods. Regarding access to credit, rice production is relatively costly as it involves investment in various equipment for irrigation, plowing and weeding (Miah et al., 2006). Therefore, consistent with production reality, access

to credit improves farmers’ productivity. Lastly and most importantly to this study, the adoption of sustainable agricultural technologies empowers farmers through specialization to deepen their knowledge on how to use productive inputs. Also, adoption is merely by choice, thus, only genuinely interested farmers participate. Therefore, this association between SRI and technical efficiency must not be surprising because SRI adoption demonstrates the benefits of a deliberate shift from plant breeding and external input centered production to careful utilization of local inputs and acquisition of better skill (Uphoff, 2003). Impliedly, through the adoption of this approach, there is an efficient use of water (Lazaro, 2004), reduced greenhouse gas emissions (Linquist et al., 2015) and maximizing use of available resources (Uphoff, 2003). Intuitively, these agricultural and environmental sustainability-oriented principles that are behind SRI practices facilitate the improved technical efficiency displayed by adopters (0.782 for non-adopters and 0.952 for adopters). Translating these results in cleaner production context, we would cautiously think that if all farmers did not adopt SRI, their efficiency on average would be 78.2 percent and waste in production would be 21.8 percent. Similarly, by adopting SRI, their efficiency would be 95.2 percent which culminates in a waste of production of 4.8 percent. Based on this result, the adoption of SRI has significant cleaner production implications. Also, according to Barrett et al. (2010), such productivity is an indicator pointing to agricultural development. In the light of the literature on SRI principles and the results presented, SRI practices could fit as an agricultural paradigm described by Clark and Tilman (2017) which is capable of ensuring productivity while minimizing the environmental impact. Contrary to our expectations farming experience, education and family size are not significant. Education facilitates the adoption of new practices (whether SRI or other methods) and learning thereby possibly leading to higher technical efficiency (Jaime and Salazar, 2011). However, looking at the percentage of farmers who have attained basic education in this area, its impact is insignificant. Our finding is similar to that of Roco et al. (2017). As for family size, the majority of members of a household in these communities have offfarm employment and very few members concentrate on farm works. Consequently, even with an excellent farming experience of the household head, there is less investment in labor for improved management practices. This result is similar to Ren et al. (2019) who found that due to high opportunity cost, smallholder farmers in China end up with part-time jobs in urban areas. Table 3 presents the productivity scores of farmers from the two groups from which we can instinctively observe the dedication and persistence of adopters as well as the benefits for adoption. Despite the absence of recipe for the institutionalization of innovation development, success is recorded by the adopter farmers. This is consistent with Zhang et al. (2018) who found that farmers' selfdiscipline is more effective than government regulations when it comes to farmers' adoption of eco-friendly agricultural production. In support, Styger et al. (2011a,b) describes in detail what SRI adopters willingly do: (i) transplant young, single, widely spaced seedlings in lines; (ii) uses limited, intermittent water (less water) rather than the traditional practice of flooding paddy field; (iii) make use of a simple mechanical weeding device which also aerates surface soil apart from removing weeds and; (iv) apply manure, compost, or other organic materials for improving soil structure, fertility, and soil organic matter content. Explicitly, this could be the explanation behind the excellent performance by the adopters who are more technically efficient than their counterparts. According to Table 3, no adopter is less than 80 percent which translates to benefits in terms of waste averting. Following the definition of technical efficiency by Koopmans (1951) that a producer is technically efficient if it is no longer possible to produce

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Table 3 Technical Efficiency distribution and benefits for adoption. Technical Efficiency Category

Non-adopters

<0.5 0.5e0.59 0.6e0.69 0.7e0.79 0.8e0.89 0.9e1

6.73 4.81 13.46 15.38 35.58 24.04

Adopters

Benefits for SRI Adoption (Reduction in waste)

0.96 99.04

þ þ þ þ ¼ ¼

Notes: The þ sign indicates avoidance of waste in production and ¼ signpost equal amount of waste in production.

any further output without producing less of some other output, the non-adopters produce more waste than adopters, and few are performing competitively with their counterparts. The improved efficiency attained by adopters highlights SRI's potential to recuperate dynamism in agriculture and contribute to address global hunger as cereals are staples for most developing countries (Gibson et al., 2000). 3.3. Factors influencing SRI adoption

of a special weeding tool (Stoop et al., 2002). For that reason, farmers who already have the tools are most likely to adopt. In contrast to popular belief that rice needs flooding to grow, SRI emphasizes control of water. No wonder water saving is claimed to have been achieved under this method (Satyanarayana et al., 2004). Accordingly, increased water control would eventually lead to adoption. Therefore, these factors are significant in explaining SRI adoption. 3.4. Implications for practice

Options for diffusing SRI are vast, but at the farm level, they are subject to various factors coupled with the willingness and pledge of the stakeholders. Most importantly, the commitment from policymakers and rice farmers is pivotal to the success of SRI practices. Table 4 presents the factors that are likely to influence the adoption of SRI practices. The lower part of the table indicates that the model is 90 percent correctly specified and as such the estimation is reliable. We find that farming experience, training, ownership of equipment and having control of water have a significant influence on adoption. All the factors have a positive influence except farming experience which in this case reduces SRI adoption probability as experience increases. In view of the fact that farming experience is surmised to be monotonic with age (Kebede et al., 1990), switching from an old practice is almost impossible as farmers get old. On the other hand, with increased training, the likelihood to adopt is high. This is consistent with the result of Hussain et al. (1994) who also found training as a significant determinant of technology adoption in agriculture. This is so because the adoption of new technologies requires new skill and adequate information which are the objectives of most agricultural training (Boothby et al., 2010). As for ownership of equipment, this is very important, especially that SRI is a labor-intensive method that makes use

Table 4 Factors affecting SRI adoption. SRI Adoption

Average Marginal Effects (Std.Err.)

Age Family Size Farming Experience Gender Education Training Water Control Equipment Model Diagnostics Log-likelihood Chi-square Pseudo r-squared Correctly specified N

0.005 (0.008) 0.002 (0.009) 0.015 (0.007)** 0.152 (0.142) 0.015 (0.121) 0.743 (0.054)*** 0.456 (0.080)*** 0.273 (0.096)*** 52.73 58.59*** 0.63 90.38% 208

Notes: Figures in parentheses are standard errors of the coefficient, while ** and *** indicate statistical significance levels at 5%, and 1%, respectively.

The results of the study provide insight into two essential scholarly and practice questions. First, how important is SRI adoption in explaining rice farmers’ efficiency? Second, what factors are effective in influencing SRI adoption? Empirical evidence regarding SRI adoption in general, and more specifically as an approach to improve the technical efficiency of rice farmers is crucial to policymakers. Mali provides a useful case, owing to its deep-rooted commitment to SRI practices and it makes a suitable learning case in the region especially for countries considering SRI adoption. Our results support that SRI adoption is associated with resource management. If policy is directed at ensuring all farmers adopt SRI, efficiency would increase by 17% and waste in terms of untapped potential would reduce to 4.8%. This also strongly suggests the reasons why a farmer may choose to adopt SRI practices. Such information should be useful in designing outreach efforts to these communities to improve agricultural practices. For instance, promoting the education of farmers and advancing policies that promote farmers’ control of water. Beyond improving efficiency, another policy and practice implication is that SRI adoption may need to be complimented or cautiously improved especially that on average, there is still potential to increase rice output by 4.8% for SRI adopters. This is achievable with ease considering that farmers in Mali have been quick to comprehend the significance of SRI for their livelihoods (Styger et al., 2011a,b). 4. Conclusion and policy recommendations Given the prediction that the world population will approach 9.7 billion by 2050, food demand will similarly increase leading to environmental degradation due to intensive agricultural production. Thus, the concern regarding the negative impacts of modern agriculture on the environment has prompted the development and diffusion of sustainable agricultural technologies. Therefore, this study was conducted with the focus on agricultural productivity and adoption of the system of rice intensification. Our results firmly establish that SRI adoption is positively and significantly associated with improved agricultural productivity. We also find that training, ownership of equipment and control of water has a significant influence on the likelihood to adopt sustainable technologies. We strongly recommend the

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institutionalization of such innovation to recuperate the dynamism in agriculture. Notably, increasing farmers' awareness on SRI practices and implementing policy measures directed at improving farmers’ water control and equipment ownership, i.e., equipment acquisition subsidies and lowering irrigation setup costs. While our study makes important advancements in knowledge on SRI adoption, it has limitations primarily revolving around the use of cross-sectional data. Nevertheless, there is a dearth of empirical studies on SRI adoption and as such focused SRI exploration could contribute significantly to the existing literature. To contribute further to the cleaner production literature, our study has important implication for future research - the use of qualitative methods in SRI adoption investigation could elucidate how social norms frame agricultural innovation preferences. Conflicts of interest The authors declare no conflict of interest. Funding This project was funded by the National Natural Science Foundation of China (Project ID: 71473123) and “A Project funded by Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).” Contribution of authors The authors contributed equally.

Table A1 Variance inflation factor Explanatory Variables

VIF

1/VIF

Farming Experience Age Family Size Water Control Equipment Training Education Gender Mean VIF

2.072 1.975 1.216 1.199 1.189 1.177 1.151 1.122 1.388

0.483 0.506 0.823 0.834 0.841 0.85 0.869 0.891

Table A2 Pairwise correlations Variables

(1)

(2)

(1) Gender (2) Age (3) Family Size (4) Education (5) Farming Experience (6) Training (7) Equipment (8) Water Control

1.000 0.108 0.149 0.244 0.232

(3)

(4)

(5)

1.000 0.321 1.000 0.214 0.232 1.000 0.684 0.363 0.263 1.000

(6)

(7)

(8)

0.035 0.165 0.154 0.035 0.107 1.000 0.054 0.066 0.126 0.085 0.095 0.292 1.000 0.039 0.211 0.131 0.106 0.163 0.290 0.307 1.000

Acknowledgments
des We appreciate Dr. Mahamadou Bassirou Tangara of “Faculte Science Economiques et de Gestion FSEG”, University of Bamako, Mali for his helpful guidance during the research period. We also warmly acknowledge Mrs Eunice Nalwembe Matafwali and three anonymous reviewers of this journal for their helpful and highly insightful review of this work.

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