Hostname: page-component-7b9c58cd5d-hpxsc Total loading time: 0 Render date: 2025-03-15T22:47:31.470Z Has data issue: false hasContentIssue false

Toward improving nitrogen use efficiency in rice production: the socio-economic, climatic and technological determinants of briquette urea adoption

Published online by Cambridge University Press:  08 March 2022

Asif Reza Anik
Affiliation:
Department of Agricultural Economics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Toritseju Begho
Affiliation:
Rural Economy, Environment & Society, Scotland's Rural College, Peter Wilson Building, King's Buildings, W Mains Rd, Edinburgh EH9 3JG, UK
Shaima Chowdhury Sharna
Affiliation:
Department of Agricultural Economics, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
Vera Eory
Affiliation:
Rural Economy, Environment & Society, Scotland's Rural College, Peter Wilson Building, King's Buildings, W Mains Rd, Edinburgh EH9 3JG, UK
Md. Mizanur Rahman*
Affiliation:
Department of Soil Science, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
*
Author for correspondence: Md. Mizanur Rahman, E-mail: mizan@bsmrau.edu.bd
Rights & Permissions [Opens in a new window]

Abstract

Deep placement of briquette urea (BU) is environmentally friendly and promotes for better nitrogen use efficiency. Nonetheless, its farm-level adoption is low. This paper contributes to the existing literature on climate-smart technology adoption by examining the factors that affect the BU adoption decision using the national representative Bangladesh Integrated Household Survey (BIHS-15) dataset consisting of 3384 rice farmers in Bangladesh. BU adoption probability is higher for farms that specialize in rice production, have more assets, use mobile phones for farming and have better access to extension services. Also, empowered women have a higher propensity to adopt BU. However, living in the feed the future zone decreases adoption probability. BU adoption probability is inversely correlated with rainfall and salinity vulnerability, while the opposite is observed for cyclone and drought vulnerability. Compared to the prilled urea (PU) users, the BU adopters applied a significantly lower amount of urea. The adopters produce more and have a relatively higher return, though the differences are insignificant. The relatively high price of BU compared to PU and the associated high labor requirement dampers the benefit of adopting the technology. Reallocation of subsidies from PU toward BU could be an effective way of promoting BU technology.

Type
Research Paper
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Introduction

Interconnected problems: climate change and nitrogen pollution

Nitrogen (N) is one of the primary macro-nutrients of plants, playing a central role in plant metabolism (Marschner, Reference Marschner2011). Additional N from organic and inorganic sources is almost universally supplied to agricultural soils to support needs of crops. The ‘green revolution’ of the 20th century was depended critically on these additional nutrient sources, which largely contributed to doubling agricultural productivity of land (Erisman et al., Reference Erisman, Sutton, Galloway, Klimont and Winiwarter2008), and N continues to play a fundamental role in ensuring global food security through the 21st century (Sutton et al., Reference Sutton, Bleeker, Howard, Bekunda, Grizzetti, de Vries, van Grinsven, Abrol, Adhya, Billen, Davidson, Datta, Diaz, Erisman, Liu, Oenema, Palm, Raghuram, Reis, Scholz, Sims, Westhoek, Zhang, Ayyappan, Bouwman, Bustamante, Fowler, Galloway, Gavito, Garnier, Greenwood, Hellums, Holland, Hoysall, Jaramillo, Klimont, Ometto, Pathak, Plocq Fichelet, Powlson, Ramakrishna, Roy, Sanders, Sharma, Singh, Singh, Yan and Zhang2013).

However, globally nitrogen use efficiency (NUE) in agriculture is very low and has a decreasing trend. Between 1961 and 2008, globally, the N balance, estimated as the difference between all nitrogen inputs and outputs on agricultural land, has increased from 22.6 to 151.9 kg ha−1 (Lassaletta et al., Reference Lassaletta, Billen, Grizzetti, Anglade and Garnier2014). The surplus reflects the excess N in the agricultural field than the requirement and results in diffuse pollution. Along with that, globally, the NUE dropped from 68 to 47% (Lassaletta et al., Reference Lassaletta, Billen, Grizzetti, Anglade and Garnier2014). From the full food supply chain, over 80% of N is lost to the environment (Sutton et al., Reference Sutton, Bleeker, Howard, Bekunda, Grizzetti, de Vries, van Grinsven, Abrol, Adhya, Billen, Davidson, Datta, Diaz, Erisman, Liu, Oenema, Palm, Raghuram, Reis, Scholz, Sims, Westhoek, Zhang, Ayyappan, Bouwman, Bustamante, Fowler, Galloway, Gavito, Garnier, Greenwood, Hellums, Holland, Hoysall, Jaramillo, Klimont, Ometto, Pathak, Plocq Fichelet, Powlson, Ramakrishna, Roy, Sanders, Sharma, Singh, Singh, Yan and Zhang2013). Much of the lost N causes environmental pollution through emissions of nitrous oxide (N2O) and ammonia (NH3) to the atmosphere, along with losses of nitrate (NO3) and organic N compounds to water. These emissions have harmful environmental effects, e.g., on a 100-year timescale, N2O has 298 times higher global warming potential than CO2 (IPCC, Reference Stocker, Qin, Plattner, Tignor, Allen, Boschung, Nauels, Xia, Bex and Midgley2013). Agricultural NH3 is one of the largest sources of atmospheric aerosol pollution (Bauer et al., Reference Bauer, Tsigaridis and Miller2016). Nitrogen compounds are also a major cause behind freshwater and marine water pollution, termed eutrophication (Sutton et al., Reference Sutton, Bleeker, Howard, Bekunda, Grizzetti, de Vries, van Grinsven, Abrol, Adhya, Billen, Davidson, Datta, Diaz, Erisman, Liu, Oenema, Palm, Raghuram, Reis, Scholz, Sims, Westhoek, Zhang, Ayyappan, Bouwman, Bustamante, Fowler, Galloway, Gavito, Garnier, Greenwood, Hellums, Holland, Hoysall, Jaramillo, Klimont, Ometto, Pathak, Plocq Fichelet, Powlson, Ramakrishna, Roy, Sanders, Sharma, Singh, Singh, Yan and Zhang2013).

Bangladesh is one of the countries with the highest quantity of excess N on agricultural land (West et al., Reference West, Gerber, Engstrom, Mueller, Brauman, Carlson, Cassidy, Johnston, MacDonald, Ray and Siebert2014). Agricultural N use increased to 1.3 M tons in 2018 from 0.02 M tons in 1961 in Bangladesh (FAOSTAT, 2020). Experimental results show that in the case of rice production in Bangladesh, the agronomic efficiency of N fertilizer is as low as 15–20 kg grain kg N−1, and NUE is only 35–40% (Ahmed et al., Reference Ahmed, Humphreys, Salim and Chauhan2016), which is certainly much lower in real-farm circumstances.

The issue of low NUE is specifically critical for a country like Bangladesh which is also predicted to be one of the most vulnerable countries to changing climate (Shahid and Behrawan, Reference Shahid and Behrawan2008; IPCC, Reference Pachauri and Meyer2014). The country's future rainfall pattern is predicted to become more variable and erratic, while the periods of consecutive dry days may increase (BanDuDeltAS, 2015). Simultaneously, there might be a 25–75% increase in upstream river discharges (Wijngaard et al., Reference Wijngaard, Lutz, Nepal, Khanal, Pradhananga, Shrestha and Immerzeel2017). All these factors will have a huge impact on agriculture, which is an important contributor to GDP and employment (MoF, 2018). In the next three decades, rice and wheat production is predicted to fall by 8 and 32%, respectively (Goosen et al., Reference Goosen, Hasan, Saha, Rezwana, Rahman, Assaduzzaman, Ashraful Kabir, Dubois and van Scheltinga2018). Alongside, the climatic hazards and anomalies such as flood, drought, salinity, cyclone and storm are predicted to increase substantially, consequently impacting the lives and livelihoods of those depending on agriculture (Goosen et al., Reference Goosen, Hasan, Saha, Rezwana, Rahman, Assaduzzaman, Ashraful Kabir, Dubois and van Scheltinga2018). The related threats are several times higher for small and marginal farmers, who constitute around 84% of the country's total farm households (BBS, 2019). These farmers are heavily dependent on agriculture for their livelihood and possess lower adaptive capacity (Wood et al., Reference Wood, Jina, Jain, Kristjanson and DeFries2014). Crucially, relying on climate-smart technologies is particularly important in reducing vulnerability.

Briquette urea (BU) deep placement as part of the solution

Solid urea, the most frequently used N fertilizer, is produced in two forms: prills and granules. Although both have the same chemical properties, their different physical and mechanical properties are distinguishable and make them suitable for different types of applications (Rahmanian et al., Reference Rahmanian, Naderi, Supuk, Abbas and Hassanpour2015). Prilled urea (PU) is around 1–2 mm in diameter (Rahmanian et al., Reference Rahmanian, Naderi, Supuk, Abbas and Hassanpour2015), which is commonly applied through broadcasting (CTC-N, 2021). As PU is smaller in size and applied on the soil surface, it is more volatile, prone to leaching and runoff, has low NUE and ultimately causes more greenhouse gas (GHG) emissions (Gaihre et al., Reference Gaihre, Singh, Islam, Huda, Islam, Satter, Sanabria, Islam and Shah2015). Alternatively, the granular urea is particularly prepared for deep placement in a larger size of 1–3 g and 2–8 mm in diameters (Rahmanian et al., Reference Rahmanian, Naderi, Supuk, Abbas and Hassanpour2015). This granular urea is called BU, which can be applied mechanically and manually (Chatterjee et al., Reference Chatterjee, Mohanty, Guru, Swain, Tripathi, Shahid, Kumar, Kumar, Bhattacharyya, Gautam, Lal, Kumar and Nayak2018) at 7–10 cm soil depth (CTC-N, 2021). The urea deep placement technique is promoted as a climate-smartFootnote 1 solution for rice systems (FAO, 2018). Deep placement of BU can increase NUE by around 40% compared to that of PU broadcasting (FAO, 2014; Ahmed et al., Reference Ahmed, Humphreys, Salim and Chauhan2016), particularly in the wet season and irrigated rice cropping systems (Bandaogo et al., Reference Bandaogo, Bidjokazo, Youl, Safo, Abaidoo and Andrews2015). Huda et al. (Reference Huda, Gaihre, Islam, Singh, Islam, Sanabria, Satter, Afroz, Halder and Jahiruddin2016) noted that NUE in experimental fields could be increased from 35% of PU to 63–67% by applying BU. BU application enhances NUE by reducing NH3 volatilization and reduces N2O emissions by 61–84% (Gaihre et al., Reference Gaihre, Singh, Islam, Huda, Islam, Satter, Sanabria, Islam and Shah2015) compared to PU broadcasting. Furthermore, BU deep placement resulted in an average ten times lower NH3 in floodwater than PU broadcast (Kapoor et al., Reference Kapoor, Singh, Patil, Magre, Shrivastava, Mishra, Das, Samadhiya, Sanabria and Diamond2008). Moreover, various experiments found that BU deep placement can increase rice yield by 20% or higher in comparison with PU broadcast (Gregory et al., Reference Gregory, Haefele, Buresh, Singh, Pandey, Byerlee, Dawe, Achim Dobermann, Mohanty, Rozelle and Bill Hardy2010; Gaihre et al., Reference Gaihre, Singh, Islam, Huda, Islam, Satter, Sanabria, Islam and Shah2015).

Motivated by the above benefits, many countries, including Bangladesh, have tried to promote BU technology as a climate-smart technology. BU was introduced in Bangladesh in the 1990s initially for research purposes. Wider scale promotion of the technology was started through the ‘Improved Livelihood for Sidr-Affected Rice Farmers’ project in 2009. As a result, BU was applied in half a million hectares of land (FAO, 2014). Department of Agricultural Extension (DAE) later took the lead role for upscaling BU and conducted massive training and organized demonstration plots showcasing the technology all over the country (FAO, 2014). Besides, there were mass media campaigning, and around 2500 briquette-making machines were delivered across the country (CTC-N, 2021). FAO (2014) reported that BU deep placement in 2008 helped the government save USD 22 million import costs and USD 14 million subsidies. Despite these efforts, upscaling was not at the desired level: the Bangladesh Integrated Household Survey (BIHS) recorded <3% of the rice farmers in the country applied BU through deep placement in 2015. The ‘Diffusion of Innovations’ theory argues for ‘failed diffusion’ which means adoption never reached or approached universal acceptance in its target population (Rogers, Reference Rogers2010). Considering Brown et al. (Reference Brown, Nidumolu, Kuehne, Llewellyn, Mungai, Brown and Ouzman2016), who reported adoption reaching 10–20% in the first 10 years for many technologies in developing countries, the BU adoption rate is extremely slow.

Adoption of a technology by a large group of heterogeneous actors, such as farmers, is a complex process, heavily influenced by their individual situation, the economic and institutional environment and the characteristics of the technology (Edwards-Jones, Reference Edwards-Jones2006; Dang et al., Reference Dang, Li, Nuberg and Bruwer2019; Prokopy et al., Reference Prokopy, Floress, Arbuckle, Church, Eanes, Gao, Gramig, Ranjan and Singh2019). Recently, adaptation to the changing climate became the focus of research and policy action. Though often studied in isolation, adaptation to climate change is still, however, embedded in the wider considerations that farmers make about their subsistence and commercial food production (Mertz et al., Reference Mertz, Reenberg, Genescio, Lambin, D'haen, Zorom, Rasmussen, Diallo, Barbier, Moussa, Diouf, Nielsen and Sandholt2011). In the process, farmers may prioritize yield and economic benefits instead of environmental consequences of technologies, and thus the adaptive capacity of sustainable practices becomes an important entry point for adoption (Martinez-Baron et al., Reference Martinez-Baron, Orjuela, Renzoni, Rodríguez and Prager2018; Tang et al., Reference Tang, Zhou, Bobojonov, Zhang and Glauben2018). The literature suggests farmers' perceptions about current and future climate and weather conditions to play a pivotal role in the adaptation process (Maas et al., Reference Maas, Wardropper, Roesch-McNally and Abatzoglou2020). Despite decades of effort across the globe, the uptake of most sustainable practices is low, with authors pointing to the need of expanding the social capital, improving institutions, knowledge exchange, providing incentives and removing perverse incentives from agricultural policies (Martinez-Baron et al., Reference Martinez-Baron, Orjuela, Renzoni, Rodríguez and Prager2018; Dang et al., Reference Dang, Li, Nuberg and Bruwer2019). Though the available literature provides important insights, none has explored the role of climatic factors in the adoption of fertilizer technology adoption.

The paper aims to fill two gaps. First, the paper seeks to improve our understanding of the slow adoption of BU deep placement technology and provide informed recommendations that can assist in increasing the efficiency of technology promotion efforts. Secondly, our goal is to examine how farmers' decision to adopt BU deep placement relates to the prevailing climatic conditions and the risks associated with a particular location. So far, these gaps have not been sufficiently addressed in the scientific literature.

Theoretical underpinning

We apply an action-oriented approach, combining insights from the ‘action theory’ that Eisenack and Stecker (Reference Eisenack and Stecker2012) applied to climate change adaptation and pathways described by Neupane et al. (Reference Neupane, Sharma and Thapa2002) and Reid et al. (Reference Reid, Smit, Caldwell and Belliveau2007) (Fig. 1). In a green revolution-driven synthetic fertilizer-based farming system, farmers are gradually experiencing declining soil productivity (Sutton et al., Reference Sutton, Bleeker, Howard, Bekunda, Grizzetti, de Vries, van Grinsven, Abrol, Adhya, Billen, Davidson, Datta, Diaz, Erisman, Liu, Oenema, Palm, Raghuram, Reis, Scholz, Sims, Westhoek, Zhang, Ayyappan, Bouwman, Bustamante, Fowler, Galloway, Gavito, Garnier, Greenwood, Hellums, Holland, Hoysall, Jaramillo, Klimont, Ometto, Pathak, Plocq Fichelet, Powlson, Ramakrishna, Roy, Sanders, Sharma, Singh, Singh, Yan and Zhang2013) while still exposed to economic and livelihood considerations imposed by the household's needs. Meanwhile, the biophysical environment is also changing, strongly driven by climate change. Expanding on Eisenack and Stecker (Reference Eisenack and Stecker2012), we include the economic and social environment, which we refer to as ‘stimuli’. Stimuli are only relevant for adaptation when they influence an ‘exposure unit’, i.e., the farm and the related household. In our case, the adaptation action is direct, as it acts on the exposure unit and reflexive. The ‘operator’, who exercises the adaptation, is the same agent as the ‘receptor’, who experiences the effects of the action. Whether a farmer will adopt the technology depends on the availability of the necessary ‘means’ (e.g., information availability, financial and labor resources, knowledge or social network) and on ‘conditions’ (i.e., constraints and resources that are beyond the farmer's control, e.g., environmental and institutional factors). Furthermore, to what extent disseminating information will be effective also depend on the resources power of the institutions partly responsible for facilitating adoption (e.g., extension, research and other market institutions), which accumulates under the knowledge exchange condition of the farmer.

Fig. 1. Schematic representation for understanding BU adoption behavior.

Source: Developed based on Neupane et al. (Reference Neupane, Sharma and Thapa2002), Reid et al. (Reference Reid, Smit, Caldwell and Belliveau2007) and Eisenack and Stecker (Reference Eisenack and Stecker2012).

There are a few alternative theoretical frameworks for explaining farmers' adoption behavior. The ‘Diffusion of Innovations’ theory is popular in the literature as it explains how, why and at what rate new ideas and technology spread (Rogers, Reference Rogers2010). The framework describes how a decision-making unit adopts or rejects an innovation in a five-stage process to minimize the uncertainty about an innovation. Potential adopters evaluate an innovation on its relative advantage over the current tools or procedures, compatibility with the pre-existing system, complexity and trialability (Rogers, Reference Rogers2010). However, though the framework is popular in the qualitative literature, particularly when analyzing the mass diffusion process, it overlooks the role of an individual's resources or social support in the adoption process. For instance, Shelomi (Reference Shelomi2015) and Chigona and Licker (Reference Chigona and Licker2008) explained the promotion of insects eating and communal computing facilities adoption through the framework. Alternatively, since our focus is on individual's adoption decision, we focus on the ‘action-theory’, which can quantitatively explain an individual's adoption.

Methodology

Data

The data for this research come from the International Food Policy Research Institute's (IFPRI) BIHS conducted in 2015. It addressed a wider dimension of livelihood, including different aspects of agricultural production covering all the three crop growing seasons from December 1st, 2013–November 30th, 2014. The survey is nationally representative of rural Bangladesh, covering rural areas of all the seven administrative divisions of Bangladesh. A total of 6500 households from 325 primary sampling units or villages belonging to all the 64 districts of the seven administrative divisions of Bangladesh were interviewed.Footnote 2 From these, we selected the 3384 farm households who grew rice in any of the cropping seasons during the reference period, who may or may not cultivated other crops. Figure 2 presents the geographic distribution of the sample farmers. We focus on rice production for two reasons. First, rice is the main crop in Bangladesh. It occupies around 75% of the total cropped area and 80% of the irrigated area (BRRI, 2020). Secondly, as compared to rice, urea consumption in other crops is very low.

Fig. 2. Map of Bangladesh showing geographic distribution of the sample farmers.

In addition, we obtained climate vulnerability and hazard data from the Statistical Yearbook of Bangladesh-2015 (BBS, 2016) and the vulnerability maps developed by the Bangladesh Agro-Meteorological Information Portal (DAE, 2020). A detailed description of all the variables is available in Table 1.

Table 1. Measurement techniques and summary statistics of the explanatory variables used in the adoption model

a USD 1 = BDT 78, (Bangladesh Bank, 2021) (https://www.bb.org.bd/econdata/exchangerate.php).

b The WEAI is an aggregate index, calculated from individual-level data. The WEAI comprises two sub-indexes. (a) The first assesses the degree to which women are empowered in five domains of empowerment (5DE) in agriculture. These domains include (1) decisions about agricultural production, (2) access to and decision-making power about productive resources, (3) control of use of income, (4) leadership in the community and (5) time allocation. (b) The second sub-index is the Gender Parity Index (GPI). The GPI measures the percentage of women who are empowered or whose achievements are at least as high as the men in their households (Alkire et al., Reference Alkire, Meinzen-Dick, Peterman, Quisumbing, Seymour and Vaz2013).

c The FtF initiative covers the Southern Delta region in Bangladesh, with the aim of increasing on-farm productivity and improve nutrition via technology and training provision and facilitating private sector engagement. For detailed description of FtF zone, please refer to https://www.feedthefuture.gov/country/bangladesh/.

d For farm category, mode is reported.

e More information about climate vulnerability is available at https://www.bamis.gov.bd/risk-map/.

Note: ***, ** and * indicate the mean difference between adopter and non-adopters are significant at 1, 5 and 10% levels, respectively.

Econometric modeling for determinants of BU technology adoption

To understand farm-level BU adoption decisions, we use the probit model approach since each farmer's decision is binary in nature. The general form of the probit model used is as follows:

(1)$$\Pr ( {y_i = 1\vert {x_{1i} \ldots x_{ki}} } ) = \Phi ( {\beta_1x_{1i} + \beta_2x_{2i} + \cdots + \beta_kx_{ki} + \varepsilon_i} ) $$

where $\Pr$ is the probability; y i is the ith household's adoption decision (e.g., 1 for the BU adopters, 0 otherwise); x is are different farm level socio-economic and biophysical factors that may influence adoption and βs are the parameters to be estimated. The detailed measurement techniques of the variables along with their sources and summary statistics are presented in Table 1 and Figure 3.

Fig. 3. Distribution of the adopters and non-adopters according to climate vulnerability and farm category.

Descriptive analysis for cost and return in rice production

The BIHS-15 database contains crop-wise detailed information on organic manure, different chemical fertilizers, family and hired labor, the quantity of product and by-product produced. For fertilizer price, it collected the prices paid by farmers. The wage rate was recorded by different farming activities. For output, the database recorded sales quantity and price by different installments. Using these, we estimated usage quantity, cost and gross return from rice production.

Results and discussion

Econometric analysis for the determinants of BU technology adoption

Summary statistics of the explanatory variables used in adoption analysis

Table 1 and Figure 3 describe the summary statistics of all independent variables used in the probit model for identifying determinants of BU adoption. Table 1 shows that the difference between BU adopters and non-adopters are statistically significant for all the variables representing the household's socio-economic conditions except for dependency ratio, enterprise profit and women empowerment in agriculture. Compared to the non-adopters, the adopters are in better socio-economic condition. The adopters are more educated and possess a relatively higher value of assets. A relatively lower portion of adopters are landless and marginal farmers and live in the feed the future (FtF) zone. Besides, adopters have more frequent contact (almost double) with the government extension officers, and a relatively higher proportion of adopters use mobile phones for agricultural purposes. Compared to the non-adopters, the adopters devoted a significantly higher proportion of gross cropped area (GCA) to rice.

Compared to the adopters, non-adopters in a relatively higher proportion were from areas with moderate or lower than moderate levels of salinity and cyclone vulnerability. Nevertheless, BU users are more likely to live in areas with less extreme rainfall, represented by a lower rainfall vulnerability score (Fig. 3). In addition, adoption of BU was relatively more common among farmers in drought-prone areas (Table 1).

Determinants of BU adoption

The marginal effects of the explanatory variables on BU adoption decisions are presented in Table 2. The BU adoption decision is positively influenced by asset, extension service, mobile phone use, specialization in rice production, women empowerment in agriculture, cyclone vulnerability and drought vulnerability. In contrast, the variables of FtF zone, temperature variability, rainfall variability, flood depth, salinity vulnerability and rainfall vulnerability have inverse impacts on adoption decisions.

Table 2. Marginal effects of the variables used in explaining adoption

Note: ***, ** and * indicate significance at 1, 5 and 10% levels, respectively.

Farm-level socio-economic and demographic factors as determinants of BU adoption: BU application is laborious, and an adopter either needs an applicator or additional labor. Besides, the average price for PU and BU paid by the farmers is 0.23 and 0.27 USD kg−1, respectively. Both the high price and investment may discourage the relatively poor farmers from adopting BU, which is in line with the literature reporting that farmers having more assets are likely to invest in new farming practices with the support of their financial capital (Wood et al., Reference Wood, Jina, Jain, Kristjanson and DeFries2014). According to the probit model estimate, an increase in the value of a household's total asset increases the probability that BU will be adopted (Table 2). Also, the literature suggests that farmers having higher resources and income are less risk averse (Onyeneke et al., Reference Onyeneke, Igberi, Uwadoka and Aligbe2018), which stimulates the interest to adopt new farm technologies.

Farmers who are more specialized in rice production are more likely to adopt BU. This finding can result from a higher risk–higher return strategy, where they invest more in securing higher rice yields. In contrast, farmers who have a wider crop portfolio might invest less in maximizing the profit margin from each of their crops. This finding aligns with Kurgat et al. (Reference Kurgat, Lamanna, Kimaro, Namoi, Manda and Rosenstock2020) but contradicts Bittinger (Reference Bittinger2010), who observed that chemical fertilizer adoption and crop diversification in Ethiopia were inversely correlated.

The results also show that rice farmers in the FtF zone have a lower adoption probability than their counterparts living in zones outside the FtF (Table 2). The goal of the FtF program in Bangladesh is to support inclusive and sustainable agriculture-led growth and strengthen resilience in certain areas that are vulnerable to poverty, hunger and malnutrition (FtF, 2020). This intervention ascertained that the farmers of these areas are resource-poor and consequently less capable of adopting new farm technologies like BU.

Access to information and institutional factors: In line with previous studies, the probit model shows that the frequency of visits of government extension workers increases the likelihood of adopting BU (Table 2). Moreover, institutional affiliation and information sources can notably influence farming decisions (Aryal et al., Reference Aryal, Rahut, Maharjan and Erenstein2018; Hasan and Kumar, Reference Hasan and Kumar2020). Also, extension service can strengthen farmers' agricultural knowledge base (Haque et al., Reference Haque, Kabir and Nishi2016) since it is an important information source about improved agricultural production and management practices (Onyeneke et al., Reference Onyeneke, Igberi, Uwadoka and Aligbe2018), and particularly can help environmental-friendly technology adoption. A similar outcome is documented by Haque et al. (Reference Haque, Kabir and Nishi2016) for the Bangladeshi rice farmers. Contrarily, in some literature, limitations of extension services were identified as a barrier to adoption. For instance, when extension workers lack updated information and provide subjective opinions about the technology, farmers do not find the information useful and simply disregard it (Sheikh et al., Reference Sheikh, Rehman and Yates2003). But, our findings suggest that extension service in Bangladesh is succeeding in providing useful BU information to farmers.

Another essential medium of information is the mobile phone, which is positively correlated with BU adoption. Owning mobile phones allows harnessing the potential to access farming information, enables farmers to connect with a diverse range of agricultural information sources and assists them to receive targeted agricultural support. Access and use of mobile phones enhance the probability of being acquainted with new production technologies and may positively influence the adoption probability. This result is also consistent with Sharna et al. (Reference Sharna, Kamruzzaman and Anik2020), who find mobile usage has a positive correlation with the adoption level of improved crop variety in Bangladesh. The role of modern communication technology such as mobile phones becomes more important in the face of growing concern about inefficiency in public extension service (Karanasios, Reference Karanasios2011).

Role of women in adoption: The positive coefficient of the index for women empowerment in agriculture (WEAI) indicates a positive correlation between BU adoption and women empowerment (Table 2). Previous studies show that women are more likely to adopt technology and diversified production portfolios when they decide about farm inputs and have more control over marketing decisions (Kurgat et al., Reference Kurgat, Lamanna, Kimaro, Namoi, Manda and Rosenstock2020; Sharna et al., Reference Sharna, Kamruzzaman and Anik2020). Similarly, Onyeneke et al. (Reference Onyeneke, Iruo and Ogoko2012) reported that in Nigeria, women are more capable of adopting technology than men in the face of climate change. Empirical findings of this sort are useful in providing targeted support globally (FAO, 2011), and particularly in Bangladesh, where women contribute a higher proportion of the agricultural labor force than men (BBS, 2018). Overall, the Asian agriculture sector is heading toward feminization due to globalization and rising labor migration (Rola-Rubzen et al., Reference Rola-Rubzen, Paris, Hawkins and Sapkota2020). Since men are more likely to migrate, they leave the farm under female supervision who are more risk-averse than the former (Croson and Gneezy, Reference Croson and Gneezy2009). In several instances, the literature focusing on Bangladesh provided empirical evidence supporting the farm-level productivity and efficiency-enhancing role of women empowerment (Rahman, Reference Rahman2000; Anik and Rahman, Reference Anik and Rahman2021). Simultaneously, the literature reported that though there have been achievements in women empowerment and gender gap reduction in the country, significant inequality exists across and within households. Only in 40% of the farming household women are adequately empowered. It recommends assuring women empowerment by providing them with the legal rights on land and access to information and technology and creating an enabling environment within the society to surge adoption of modern technologies.

Climatic factors and adoption: The climatic factors considered in this paper as part of the theoretical model's biophysical variables significantly impact BU adoption decisions. Although biophysical conditions such as salinity, flood, rainfall, drought and cyclone are beyond a farmer's control, variability in these conditions may act as a stimulator and influence the adoption decision. For example, increasing temperature and rainfall variability reduce BU adoption probability. Adoption probability is lower in areas with a higher risk of salinity and rainfall vulnerability, whereas it is the opposite for drought and cyclone vulnerability. With increasing flood depth, BU adoption probability reduces (Table 2). The importance of appropriate adaptation strategies to address and ease climatic challenges on agriculture, livelihood and vulnerability is documented in the literature (Funk et al., Reference Funk, Dettinger, Michaelsen, Verdin, Brown, Barlow and Hoell2008; Lobell et al., Reference Lobell, Burke, Tebaldi, Mastrandrea, Falcon and Naylor2008). But adoption is context-specific, and depending on the nature of technology and type of climatic factors and vulnerabilities, the relationship between climatic factors and adoption may vary. Asfaw et al. (Reference Asfaw, Di Battista and Lipper2016) reported that weather variability strongly influences the type of technology adopted. For instance, greater rainfall and temperature variability enhance the likelihood of incorporating crop residue into soils, while the opposite happens for using modern inputs and organic fertilizer. Khanal et al. (Reference Khanal, Wilson, Lee and Hoang2018) found that a household's past experiences with climate vulnerability like flood and drought positively influence adoption strategies like increasing chemical fertilizer or farmyard manure use to counteract the effects of climate-induced stresses. Overall, all kinds of climate-driven risks do not encourage the adoption of environment-friendly innovations. Instead, farms' ecosystem responses to climate hazards discourage their adoption (Yoder et al., Reference Yoder, Houser, Bruce, Sullivan and Farmer2021).

The salinity intrusion index negatively correlates with BU adoption (Table 2). This finding aligns with Anik et al. (Reference Anik, Ranjan and Ranganathan2018), which reported that salinity weakens the ability of households to stick to their cropping-based livelihoods and limits overall economic opportunities in southwestern coastal regions of Bangladesh. Due to more crop failure in salinity vulnerable regions, farmers are unwilling to invest in new technologies to accelerate farm production growth. Notably, Bangladesh is facing more climatic hazards than ever before, which exacerbates farmers' adaptive capacity to transform agriculture (Rahman, Reference Rahman2016; Hossain et al., Reference Hossain, Ahmed, Ojea and Fernandes2018), while at the same time, it dampens the capability of rice producers to uptake improved agricultural technologies, such as BU.

Heavy rainfall and flooding increase the losses of agricultural chemicals through leaching from the land and reduce yield (Wang et al., Reference Wang, Ju, Wei, Li, Zhao and Hu2010). Besides, climate change and the associated variabilities may reduce agricultural investment by reducing capabilities and possible returns, but investors can also overestimate the uncertainty (Cooper et al., Reference Cooper, Dimes, Rao, Shapiro, Shiferaw and Twomlow2008). Thus, in areas where rainfall and flood frequently occur, farmers may choose multiple applications of PU in accordance with the climatic events and find an effective and visible effect of this practice on sustaining yield. Hence farmers exposed to rainfall and flood vulnerabilities might be less likely to invest in relatively expensive BU technology, which does not always provide immediate results. Houser and Stuart (Reference Houser and Stuart2020) also corroborated that the increase in frequency and intensity of heavy rain provokes nitrogen loss and introduces economic risks to farmers. Therefore, farmers respond by multiplying nitrogen application rates to stabilize production and cope with the climate vulnerability. The political-economic structure of society motivates farmers to respond to climate change in ways that accelerate the environmental contradictions of agriculture.

BU adoption probability falls with increasing temperature and rainfall variability (Table 2). Rahman (Reference Rahman2016) noted that different climatic factors, including their variabilities, significantly influence agricultural land use diversity and yield in Bangladesh. Variability in climatic factors, including rainfall and temperature variability, have a profound impact on farm production, and ultimately, the sustainability of the whole system is challenged (Aryal et al., Reference Aryal, Sapkota, Rahut and Jat2020). In addition, the variabilities contribute to uncertainty in the production process, which affects farm production and efficiency (Semenov and Porter, Reference Semenov and Porter1995). All these may discourage a farmer from adopting since the perceived benefit from adoption may not be higher than the cost of adoption.

In contrast to all other climatic variables, drought and cyclone vulnerability are associated with an increased BU adoption probability (Table 2). This is in line with the literature reporting farmers in the drought-prone areas of Bangladesh adopting different adaptation strategies (Alauddin and Sarker, Reference Alauddin and Sarker2014). Since drought is a gradual process and has a trend, a farmer in drought-prone areas may face less uncertainty and have reasons to be optimistic about sustaining production loss through adopting BU technology.

Farmers with past experiences of frequent and disastrous cyclones perceive decreasing farm productivity and are less willing to adopt new technologies (Hasan and Kumar, Reference Hasan and Kumar2020). This would suggest high cyclone vulnerability is associated with lower BU adoption. However, in Bangladesh, the southern-coastal part is more exposed to cyclone vulnerability (DAE, 2020), where the BU technology was first introduced (FAO, 2014). Hence, the farmers in the region are expected to be well informed and acquainted with the technology than other parts of the country.

Comparison of input use by the adopters and non-adopters

The quantity and cost of fertilizers and soil additives differ between adopters and non-adopters. However, the differences are statistically significant only for urea, gypsum, calcium and lime. Since BU application reduces N-losses and has the potential to improve NUE, the adopters applied around 36% less urea than the non-adopters, which enabled them to save around 13 USD ha−1 (Table 3). Although the average price of BU is around 17% higher than that of PU. But since unlike other fertilizers where broadcasting is the common method, BU must be point-placed 5–7 cm below the soil surface (Bandaogo et al., Reference Bandaogo, Bidjokazo, Youl, Safo, Abaidoo and Andrews2015). Furthermore, since it is a labor-intensive application process, the adopters of BU require more family and hired labor for fertilizer application. As such, the BU adopter's hired labor cost is about 10 USD ha−1 more than their counterparts (Table 4).

Table 3. Average quantity (kg ha−1) and cost (USD ha−1) of different fertilizer application by the adopters and non-adopters

TSP, triple super phosphate; SSP, single super phosphate; MAP, monoammonium phosphate; DAP, diammonium phosphate; MoP, muriate of potash; NPKS, nitrogen, phosphorus, potassium and sulfur.

Figures in parentheses are standard deviations. *** indicates the mean difference between adopters and non-adopters is significant at the 1% level.

Organic fertilizer quantity and calcium and lime price is not available in the dataset.

Table 4. Average quantity (man-days ha−1) and cost (USD ha−1) of labor used in fertilizer application by the adopters and non-adopters

Note: Figures in parentheses are standard deviations. *** indicates the mean difference between adopters and non-adopters is significant at the 1% level.

Production and return from using BU

BU reportedly increases rice yield compared to PU in agronomic experiments (Miah et al., Reference Miah, Gaihre, Hunter, Singh and Hossain2016; Chatterjee et al., Reference Chatterjee, Mohanty, Guru, Swain, Tripathi, Shahid, Kumar, Kumar, Bhattacharyya, Gautam, Lal, Kumar and Nayak2018). We found that although BU application resulted in a higher grain yield, the insignificant yield difference between the adopters and non-adopters can be the outcome of a farmer's low investment-low return strategy, particularly when the farmer is capital constrained. A BU adopter may decide for a relatively low N-fertilizer quantity, which is just enough to provide the same yield as was earlier obtained with PU. It is not necessary that higher NUE will always ensure a higher yield (Table 5).

Table 5. Quantity of rice grain (ton ha−1) and straw (ton ha−1) produced and the associated return (USD/ha)

Note: Figures in parentheses are standard deviations. No significant difference between the adopters and non-adopters are observed.

Conclusions and policy implications

This paper aimed to explore the determinants of BU adoption through analyzing a database that is representative of rural Bangladesh. Since PU is the main source of N in Bangladesh and BU is a full agronomic alternative, the substitution could help the adopters raise NUE and positively contribute to environmental sustainability. But since the environment may not be the prime concern for all farmers, a farmer may not intentionally adopt BU unless adoption results in higher production and profit in the changing environment. Our results show that compared to the non-adopters, the BU adopters applied less urea and hence they had lower urea cost, though PU price was relatively low compared to BU. But since BU is applied through the deep-placement method and the applicator is unavailable locally, labor usage and cost increase significantly for the adopters. Though BU adopters have a high yield, the yield difference is not statistically significant.

We employed the ‘action theory’ and the probit model. The empirical results of the probit model provide insights into how rice farmers in Bangladesh decide about BU adoption based on differences in their socio-economic characteristics, information sources, level of women empowerment, climatic conditions and vulnerability under which the farm operates. Overall, the climate-related variables have a more comprehensive impact than other variables on adoption decisions. In general, the farm households with more assets, access to information, extension services, own mobile phones and located outside the FtF zone are more likely to adopt. Higher adoption probability for the households where women are more empowered is an encouraging result since compared to men, women have an increasing participation rate in agriculture. Besides, the rice farmers who also cultivate a wide range of crops are reluctant to use BU.

According to our results, climate change adaptation could become an entry point for increasing adoption where it relates to drought and cyclone vulnerability. However, farmers exposed to other types of climate vulnerability and variability are less likely to adopt, which can be a result of higher risk-averse characteristics and relatively poor economic status than their counterparts living in less vulnerable areas.

The policy recommendations from the study are as follows. First, the adoption program should concentrate on areas vulnerable to climate and hazards. Similarly, more focus is required for the farmers with relatively low assets and living in economically fragile areas (e.g., FtF) since they have a low adoption probability. Secondly, access to information through conventional extension and contemporary digital technology should be promoted. The latter becomes more important considering the immense workload the government extension agents have to bear.Footnote 3 Thirdly, since women are more involved in agriculture and are more likely to adopt BU, targeting women groups and associations in smallholder rural communities can have significant positive impacts on increasing the uptake of climate-smart technology. Fourthly, the BU applicator should be made available at the farm level. Finally, the cost that BU adopters saved through applying a low level of urea fertilizer is consumed by the relatively high BU price and more labor requirements. To make BU lucrative to the farmers, the price of BU must be made lower than conventional urea through allocating more subsidies, particularly from PU to BU production and import. While the exact mechanism here requires further research, issues like efficiency and transparency in the subsidy distribution system should be considered.

Author contributions

A.R.A. and M.M.R. designed the study. T.B. and V.E. contributed to the plan. A.R.A. and S.C.S. carried out data compilation and analysis. A.R.A., T.B. and S.C.S. wrote the initial draft of the manuscript. All authors contributed substantially to the writing of the manuscript.

Financial support

This paper results from research funded by UKRI GCRF South Asian Nitrogen Hub (SANH). The project team includes partners from across South Asia and the UK. Neither UKRI nor any of the partner institutions are responsible for the views advanced here.

Conflict of interest

We have no conflict of interest to declare.

Footnotes

1 Climate-smart agriculture has three main objectives: sustainably increasing agricultural productivity and incomes; adapting and building resilience to climate change and reducing GHG emissions, where possible (http://www.fao.org/climate-smart-agriculture/en/).

2 More information about these surveys are available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=hdl:1902.1/21266.

3 In 2008, there are 14,870,576 farm holdings (BBS, 2017) and the Department of Agricultural Extension has only 12,799 personals working as Sub-Assistant Agricultural Officer (DAE, 2017).

References

Ahmed, S, Humphreys, E, Salim, M and Chauhan, BS (2016) Growth, yield and nitrogen use efficiency of dry-seeded rice as influenced by nitrogen and seed rates in Bangladesh. Field Crops Research 186, 1831.CrossRefGoogle Scholar
Alauddin, M and Sarker, MAR (2014) Climate change and farm-level adaptation decisions and strategies in drought-prone and groundwater-depleted areas of Bangladesh: an empirical investigation. Ecological Economics 106, 204213.CrossRefGoogle Scholar
Alkire, S, Meinzen-Dick, R, Peterman, A, Quisumbing, A, Seymour, G and Vaz, A (2013) The women's empowerment in agriculture index. World Development 52, 7191.CrossRefGoogle Scholar
Anik, AR and Rahman, S (2021) Women's empowerment in agriculture: level, inequality, progress, and impact on productivity and efficiency. The Journal of Development Studies 57, 930948.CrossRefGoogle Scholar
Anik, AR, Ranjan, R and Ranganathan, T (2018) Estimating the impact of salinity stress on livelihood choices and incomes in Rural Bangladesh. Journal of International Development 30, 14141438.CrossRefGoogle Scholar
Aryal, JP, Rahut, DB, Maharjan, S and Erenstein, O (2018) Factors affecting the adoption of multiple climate-smart agricultural practices in the Indo-Gangetic Plains of India. Natural Resources Forum 42, 141158.CrossRefGoogle Scholar
Aryal, JP, Sapkota, TB, Rahut, DB and Jat, ML (2020) Agricultural sustainability under emerging climatic variability: the role of climate-smart agriculture and relevant policies in India. International Journal of Innovation and Sustainable Development 14, 219245.CrossRefGoogle Scholar
Asfaw, S, Di Battista, F and Lipper, L (2016) Agricultural technology adoption under climate change in the Sahel: micro-evidence from Niger. Journal of African Economies 25, 637669.CrossRefGoogle Scholar
Bandaogo, A, Bidjokazo, F, Youl, S, Safo, E, Abaidoo, R and Andrews, O (2015) Effect of fertilizer deep placement with urea supergranule on nitrogen use efficiency of irrigated rice in Sourou Valley (Burkina Faso). Nutrient Cycling in Agroecosystems 102, 7989.CrossRefGoogle Scholar
BanDuDeltAS (2015) Baseline Report on Water Resource. Dhaka: Bangladesh Delta Plan 2100 Formulation Project, GED.Google Scholar
Bangladesh Bank (2021) Exchange rate of Taka. Bangladesh Bank, Dhaka. Available at: https://www.bb.org.bd/econdata/exchangerate.php (accessed on 25 July 2021).Google Scholar
Bauer, SE, Tsigaridis, K and Miller, R (2016) Significant atmospheric aerosol pollution caused by world food cultivation. Geophysical Research Letters 43, 53945400.CrossRefGoogle Scholar
BBS (2016) Bangladesh Disaster-Related Statistics, Climate Change and Natural Disaster Perspectives. Dhaka: Bangladesh Bureau of Statistics (BBS), Ministry of Planning, Government of the Peoples’ Republic of Bangladesh.Google Scholar
BBS (2017) Statistical Yearbook of Bangladesh 2018. Dhaka: Bangladesh Bureau of Statistics.Google Scholar
BBS (2018) Labour Force Survey Bangladesh 2016–17. Agargaon, Dhaka: Statistics Division, Bangladesh Bureau of Statistics (BBS), Ministry of Planning, Government of the People's Republic of Bangladesh.Google Scholar
BBS (2019) Statistical Yearbook of Bangladesh 2018. Dhaka, Bangladesh: Bangladesh Bureau of Statistics (BBS).Google Scholar
Bittinger, AK (2010) Crop Diversification and Technology Adoption: The Role of Market Isolation in Ethiopia (Doctoral dissertation). Montana State University-Bozeman, College of Agriculture, Bozeman, MT.Google Scholar
Brown, PR, Nidumolu, UB, Kuehne, G, Llewellyn, R, Mungai, O, Brown, B and Ouzman, J (2016) IAS91 – Development of the Public Release Version of Smallholder ADOPT for Developing Countries. Canberra: Australian Centre for International Agricultural Research, pp. 158.Google Scholar
BRRI (2020) Rice in Bangladesh. Rice knowledge bank. Bangladesh Rice Research Institute. Available at http://www.knowledgebank-brri.org/riceinban.php (accessed 3 January 2020).Google Scholar
Chatterjee, D, Mohanty, S, Guru, PK, Swain, CK, Tripathi, R, Shahid, M, Kumar, U, Kumar, A, Bhattacharyya, P, Gautam, P, Lal, B, Kumar, PD and Nayak, AK (2018) Comparative assessment of urea briquette applicators on greenhouse gas emission, nitrogen loss and soil enzymatic activities in tropical lowland rice. Agriculture, Ecosystems & Environment 252, 178190.CrossRefGoogle Scholar
Chigona, W and Licker, P (2008) Using diffusion of innovations framework to explain communal computing facilities adoption among the urban poor. Information Technologies & International Development 4, 57.CrossRefGoogle Scholar
Cooper, PJM, Dimes, J, Rao, KPC, Shapiro, B, Shiferaw, B and Twomlow, S (2008) Coping better with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: an essential first step in adapting to future climate change? Agriculture, Ecosystems & Environment 126, 2435.CrossRefGoogle Scholar
Croson, R and Gneezy, U (2009) Gender differences in preferences. Journal of Economic Literature 47, 448474.CrossRefGoogle Scholar
CTC-N (2021) Urea Deep Placement (UDP) Technique. Climate Technology Centre and Network (CTC-N). Available at https://www.ctc-n.org/products/urea-deep-placement-udp-technique (accessed 9 February 2021).Google Scholar
DAE (2017) Annual Report 2016-17. Department of Agricultural Extension, Ministry of Agriculture. Dhaka: Government of Bangladesh.Google Scholar
DAE (2020) Climate Vulnerable Risk Maps of Bangladesh. Dhaka: Bangladesh Agro-Meteorological Information Portal, Agro-Meteorological Information Systems Development Project, Department of Agricultural Extension, Ministry of Agriculture.Google Scholar
Dang, HL, Li, E, Nuberg, I and Bruwer, J (2019) Factors influencing the adaptation of farmers in response to climate change: a review. Climate and Development 11, 765774.CrossRefGoogle Scholar
Edwards-Jones, G (2006) Modelling farmer decision-making: concepts, progress and challenges. Animal Science 82, 783790.CrossRefGoogle Scholar
Eisenack, K and Stecker, R (2012) A framework for analyzing climate change adaptations as actions. Mitigation and Adaptation Strategies for Global Change 17, 243260.CrossRefGoogle Scholar
Erisman, JW, Sutton, MA, Galloway, JN, Klimont, Z and Winiwarter, W (2008) How a century of ammonia synthesis changed the world. Nature Geoscience 1, 636639.CrossRefGoogle Scholar
FAO (2011) The State of Food and Agriculture. Women in Agriculture: Closing the gap for Development. Rome: FAO.Google Scholar
FAO (2014) FAO success stories on climate-smart agriculture. Food and Agriculture Organisation (FAO). Available at http://www.fao.org/3/a-i3817e.pdf.Google Scholar
FAO (2018) Climate-Smart Agriculture. Available at http://www.fao.org/3/a-i3817e.pdf (accessed 13 July 2021).Google Scholar
FAOSTAT (2020) Food and Agriculture Organisation (FAO). Available at http://www.fao.org/faostat/en/#data/RFN (accessed 9 January 2020).Google Scholar
FtF (2020) The U.S. Government's global hunger & food security initiative. Available at https://www.feedthefuture.gov/country/bangladesh/.Google Scholar
Funk, C, Dettinger, MD, Michaelsen, JC, Verdin, JP, Brown, ME, Barlow, M and Hoell, A (2008) Warming of the Indian Ocean threatens eastern and southern African food security but could be mitigated by agricultural development. Proceedings of the National Academy of Sciences 105, 1108111086.CrossRefGoogle ScholarPubMed
Gaihre, YK, Singh, U, Islam, SM, Huda, A, Islam, MR, Satter, MA, Sanabria, J, Islam, MR and Shah, AL (2015) Impacts of urea deep placement on nitrous oxide and nitric oxide emissions from rice fields in Bangladesh. Geoderma 259, 370379.CrossRefGoogle Scholar
Goosen, H, Hasan, T, Saha, SK, Rezwana, N, Rahman, R, Assaduzzaman, M, Ashraful Kabir, A, Dubois, G and van Scheltinga, CT (2018) Nationwide Climate Vulnerability Assessment in Bangladesh. Final Draft. Dhaka:Ministry of Environment, Forest & Climate Change.Google Scholar
Gregory, DI, Haefele, SM, Buresh, RJ and Singh, U (2010) Fertilizer use, markets, and management. In Pandey, S, Byerlee, D, Dawe, D, Achim Dobermann, A, Mohanty, S, Rozelle, S and Bill Hardy, B (eds), Rice in the Global Economy. Strategic Research and Policy Issues for Food Security. Los Banos, Philippines: International Rice Research Institute, pp. 231263.Google Scholar
Haque, MM, Kabir, MH and Nishi, NA (2016) Determinants of rice farmers’ adoption of integrated pest management practices in Bangladesh. Journal of Experimental Agriculture International 14, 16.CrossRefGoogle Scholar
Hasan, MK and Kumar, L (2020) Perceived farm-level climatic impacts on coastal agricultural productivity in Bangladesh. Climatic Change 161, 617636.CrossRefGoogle Scholar
Hossain, MA, Ahmed, M, Ojea, E and Fernandes, JA (2018) Impacts and responses to environmental change in coastal livelihoods of south-west Bangladesh. Science of the Total Environment 637, 954970.CrossRefGoogle ScholarPubMed
Houser, M and Stuart, D (2020) An accelerating treadmill and an overlooked contradiction in industrial agriculture: climate change and nitrogen fertilizer. Journal of Agrarian Change 20, 215237.CrossRefGoogle Scholar
Huda, A, Gaihre, YK, Islam, MR, Singh, U, Islam, MR, Sanabria, J, Satter, MA, Afroz, H, Halder, A and Jahiruddin, M (2016) Floodwater ammonium, nitrogen use efficiency and rice yields with fertilizer deep placement and alternate wetting and drying under triple rice cropping systems. Nutrient Cycling in Agroecosystems 104, 5366.CrossRefGoogle Scholar
IPCC (2013) Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. In Stocker, TF, Qin, D, Plattner, G-K, Tignor, M, Allen, SK, Boschung, J, Nauels, A, Xia, Y, Bex, V and Midgley, PM (eds), Climate Change 2013: The Physical Science Basis. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press, pp. 11552.Google Scholar
IPCC (2014) Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. In Pachauri, RK and Meyer, LA (eds), Climate Change 2014: Synthesis Report. Geneva, Switzerland: Intergovernmental Panel on Climate Change, p. 151.Google Scholar
Kapoor, V, Singh, U, Patil, SK, Magre, H, Shrivastava, LK, Mishra, VN, Das, RO, Samadhiya, VK, Sanabria, J and Diamond, R (2008) Rice growth, grain yield, and floodwater nutrient dynamics as affected by nutrient placement method and rate. Agronomy Journal 100, 526536.CrossRefGoogle Scholar
Karanasios, STAN (2011) New & Emergent ICTs and Climate Change in Developing Countries. Manchester, UK: Center for Development Informatics. Institute for Development Policy and Management, SED. University of Manchester.Google Scholar
Khanal, U, Wilson, C, Lee, BL and Hoang, VN (2018) Climate change adaptation strategies and food productivity in Nepal: a counterfactual analysis. Climatic Change 148, 575590.CrossRefGoogle Scholar
Kurgat, BK, Lamanna, C, Kimaro, A, Namoi, N, Manda, L and Rosenstock, TS (2020) Adoption of climate-smart agriculture technologies in Tanzania. Frontiers in Sustainable Food Systems 4, 55.CrossRefGoogle Scholar
Lassaletta, L, Billen, G, Grizzetti, B, Anglade, J and Garnier, J (2014) 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environmental Research Letters 9, 105011.CrossRefGoogle Scholar
Lobell, DB, Burke, MB, Tebaldi, C, Mastrandrea, MD, Falcon, WP and Naylor, RL (2008) Prioritizing climate change adaptation needs for food security in 2030. Science (New York, N.Y.) 319, 607610.CrossRefGoogle ScholarPubMed
Maas, A, Wardropper, C, Roesch-McNally, G and Abatzoglou, J (2020) A (mis)alignment of farmer experience and perceptions of climate change in the US inland Pacific Northwest. Climatic Change 162, 10111029.CrossRefGoogle Scholar
Marschner, P (2011) Mineral Nutrition of Higher Plants, 3rd Edition. London: Academic Press.Google Scholar
Martinez-Baron, D, Orjuela, G, Renzoni, G, Rodríguez, AML and Prager, SD (2018) Small-scale farmers in a 1.5 C future: the importance of local social dynamics as an enabling factor for implementation and scaling of climate-smart agriculture. Current Opinion in Environmental Sustainability 31, 112119.CrossRefGoogle Scholar
Mertz, OMC, Reenberg, A, Genescio, L, Lambin, EF, D'haen, S, Zorom, M, Rasmussen, K, Diallo, D, Barbier, B, Moussa, IB, Diouf, A, Nielsen, and Sandholt, I (2011) Adaptation strategies and climate vulnerability in the Sudano-Sahelian region of West Africa. Atmospheric Science Letters 12, 104108.CrossRefGoogle Scholar
Miah, MMA, Gaihre, YK, Hunter, G, Singh, U and Hossain, SA (2016) Fertilizer deep placement increases rice production: evidence from farmers’ fields in southern Bangladesh. Agronomy Journal 108, 805812.CrossRefGoogle Scholar
MoDMR (2013) Vulnerability to Climate Induced Drought: Scenario and Impact. Dhaka: Comprehensive Disaster Management Program (CDMP II), Ministry of Disaster Management and Relief (MoDMR).Google Scholar
MoF (2018) Bangladesh Economic Review 2018. Dhaka: Ministry of Finance.Google Scholar
Neupane, RP, Sharma, KR and Thapa, GP (2002) Adoption of agroforestry in the hills of Nepal: a logistic regression analysis. Agricultural Systems 72, 177196.CrossRefGoogle Scholar
Onyeneke, RU, Iruo, FA and Ogoko, IM (2012) Microlevel analysis of determinants of farmers’ adaptation measures to climate change in the Niger Delta Region of Nigeria: lessons from Bayelsa State. Nigerian Journal of Agricultural Economics 3, 918.Google Scholar
Onyeneke, RU, Igberi, CO, Uwadoka, CO and Aligbe, JO (2018) Status of climate-smart agriculture in southeast Nigeria. GeoJournal 83, 333346.CrossRefGoogle Scholar
Prokopy, LS, Floress, K, Arbuckle, JG, Church, SP, Eanes, FR, Gao, Y, Gramig, BM, Ranjan, P and Singh, AS (2019) Adoption of agricultural conservation practices in the United States: evidence from 35 years of quantitative literature. Journal of Soil and Water Conservation 74, 520.CrossRefGoogle Scholar
Rahman, S (2000) Women's employment in Bangladesh agriculture: composition, determinants, and scope. Journal of Rural Studies 16, 497507.CrossRefGoogle Scholar
Rahman, S (2016) Impacts of climate change, agroecology and socio-economic factors on agricultural land use diversity in Bangladesh (1948–2008). Land Use Policy 50, 169178.CrossRefGoogle Scholar
Rahmanian, N, Naderi, S, Supuk, E, Abbas, R and Hassanpour, A (2015) Urea finishing process: prilling versus granulation. Procedia Engineering 102, 174181.CrossRefGoogle Scholar
Reid, S, Smit, B, Caldwell, W and Belliveau, S (2007) Vulnerability and adaptation to climate risks in Ontario agriculture. Mitigation and Adaptation Strategies for Global Change 12, 609637.CrossRefGoogle Scholar
Rogers, EM (2010) Diffusion of Innovations, 4th edition. New York: Simon and Schuster.Google Scholar
Rola-Rubzen, MF, Paris, T, Hawkins, J and Sapkota, B (2020) Improving gender participation in agricultural technology adoption in Asia: from rhetoric to practical action. Applied Economic Perspectives and Policy 42, 113125.CrossRefGoogle Scholar
Semenov, MA and Porter, JR (1995) Non-linearity in climate change impact assessments. Journal of Biogeography 2, 597600.CrossRefGoogle Scholar
Shahid, S and Behrawan, H (2008) Drought risk assessment in the western part of Bangladesh. Natural Hazards 46, 391413.CrossRefGoogle Scholar
Sharna, SC, Kamruzzaman, M and Anik, AR (2020) Determinants of improved chickpea variety adoption in high Barind region of Bangladesh. International Journal of Agricultural Research, Innovation and Technology 10, 5663.CrossRefGoogle Scholar
Sheikh, AD, Rehman, T and Yates, CM (2003) Logit models for identifying the factors that influence the uptake of new ‘no-tillage’ technologies by farmers in the rice–wheat and the cotton–wheat farming systems of Pakistan's Punjab. Agricultural Systems 75, 7995.CrossRefGoogle Scholar
Shelomi, M (2015) Why we still don't eat insects: assessing entomophagy promotion through a diffusion of innovations framework. Trends in Food Science & Technology 45, 311318.CrossRefGoogle Scholar
Sutton, MA, Bleeker, A, Howard, CM, Bekunda, M, Grizzetti, B, de Vries, W, van Grinsven, HJM, Abrol, YP, Adhya, TK, Billen, G, Davidson, EA, Datta, A, Diaz, R, Erisman, JW, Liu, XJ, Oenema, O, Palm, C, Raghuram, N, Reis, S, Scholz, RW, Sims, T, Westhoek, H and Zhang, FS, with contributions from Ayyappan, S, Bouwman, AF, Bustamante, M, Fowler, D, Galloway, JN, Gavito, ME, Garnier, J, Greenwood, S, Hellums, DT, Holland, M, Hoysall, C, Jaramillo, VJ, Klimont, Z, Ometto, JP, Pathak, H, Plocq Fichelet, V, Powlson, D, Ramakrishna, K, Roy, A, Sanders, K, Sharma, C, Singh, B, Singh, U, Yan, XY and Zhang, Y (2013) Our Nutrient World: The Challenge to Produce More Food and Energy with Less Pollution. Edinburgh: Global Overview of Nutrient Management. Centre for Ecology and Hydrology, Edinburgh on behalf of the Global Partnership on Nutrient Management and the International Nitrogen Initiative.Google Scholar
Tang, L, Zhou, J, Bobojonov, I, Zhang, Y and Glauben, T (2018) Induce or reduce? The crowding-in effects of farmers’ perceptions of climate risk on chemical use in China. Climate Risk Management 20, 2737.CrossRefGoogle ScholarPubMed
Wang, H, Ju, X, Wei, Y, Li, B, Zhao, L and Hu, K (2010) Simulation of bromide and nitrate leaching under heavy rainfall and high-intensity irrigation rates in North China Plain. Agricultural Water Management 97, 16461654.CrossRefGoogle Scholar
West, PC, Gerber, JS, Engstrom, PM, Mueller, ND, Brauman, KA, Carlson, KM, Cassidy, ES, Johnston, M, MacDonald, GK, Ray, DK and Siebert, S (2014) Leverage points for improving global food security and the environment. Science (New York, N.Y.) 345, 325.CrossRefGoogle ScholarPubMed
Wijngaard, RR, Lutz, AF, Nepal, S, Khanal, S, Pradhananga, S, Shrestha, AB and Immerzeel, WW (2017) Future changes in hydro-climatic extremes in the Upper Indus, Ganges, and Brahmaputra River basins. PLoS One 12, e0190224.CrossRefGoogle ScholarPubMed
Wood, SA, Jina, AS, Jain, M, Kristjanson, P and DeFries, RS (2014) Small holder farmer cropping decisions related to climate variability across multiple regions. Global Environmental Change 25, 163172.CrossRefGoogle Scholar
Yoder, L, Houser, M, Bruce, A, Sullivan, A and Farmer, J (2021) Are climate risks encouraging cover crop adoption among farmers in the southern Wabash River Basin? Land Use Policy 102, 105268.CrossRefGoogle Scholar
Figure 0

Fig. 1. Schematic representation for understanding BU adoption behavior.Source: Developed based on Neupane et al. (2002), Reid et al. (2007) and Eisenack and Stecker (2012).

Figure 1

Fig. 2. Map of Bangladesh showing geographic distribution of the sample farmers.

Figure 2

Table 1. Measurement techniques and summary statistics of the explanatory variables used in the adoption model

Figure 3

Fig. 3. Distribution of the adopters and non-adopters according to climate vulnerability and farm category.

Figure 4

Table 2. Marginal effects of the variables used in explaining adoption

Figure 5

Table 3. Average quantity (kg ha−1) and cost (USD ha−1) of different fertilizer application by the adopters and non-adopters

Figure 6

Table 4. Average quantity (man-days ha−1) and cost (USD ha−1) of labor used in fertilizer application by the adopters and non-adopters

Figure 7

Table 5. Quantity of rice grain (ton ha−1) and straw (ton ha−1) produced and the associated return (USD/ha)