Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Discover Agriculture Research Stepwise methods for more nuanced adoption analysis: a case study of harvest and post‑harvest mechanization in Bangladesh Brendan Brown1,4  · Pragya Timsina2  · Akriti Sharma1  · Sreejith Aravindakshan3  · Timothy Krupnik3 Received: 3 May 2024 / Accepted: 1 October 2024 © Crown 2024 OPEN Abstract The adoption of agricultural harvest and post-harvest mechanization is crucial for addressing drudgery, food losses, climate vulnerability and food security. Despite considerable efforts by government and development partners to prior- itize agricultural mechanization, labour-intensive manual (post-)harvest activities continue to dominate in Bangladeshi smallholder systems. Explorations of this has been limited by simplistic binary approaches that ignore the dynamic pathways to usage outcomes. Instead, we apply non-binary analytical methods to district representative data to highlight the value in moving beyond binary adoption analysis. Results highlight that a national (post-)harvest mechanisation rate of 74% is insufficient to capture the true adoption status, given substantial disparity exists across machinery and by district. Deeper exploration of temporal and spatial differences enable the identification of key trends that warrant further in-depth explorations, while only 46% satisfaction with extension systems highlights the need to re-evaluate key information exchange mechanisms. We conclude that there is a clear need for district and machinery specific policy arrangements if Bangladesh is to achieve (post-)harvest mechanisation objectives that aim to reduce food loses and enable greater food security nationwide. Keywords Smallholder mechanisation · Impact evaluation · Harvest machinery · Postharvest machinery · Sustainable intensification · Stepwise process of mechanisation framework 1 Introduction The United Nations estimates that 8.4 billion people will reside in developing countries by 2050 [1], leading to a rise in total global food demand of between 35 to 56% from 2010 levels [2]. While optimising production will be necessary in addressing this challenge, insufficient attention has been devoted to mitigating food loss across harvest and post-harvest stages [3]. This is particularly pertinent given approximately one-third of all food produced for human consumption is wasted, amounting to 630–670 million tons lost annually and resulting in a financial loss of USD$990 billion [4]. The Food and Agriculture Organization (FAO) estimates post-harvest losses in developing countries to range from 30 to 40% across various food categories, including root crops, fruits, vegetables, cereals, fish, and oilseeds [5]. These losses represent Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1007/ s44279- 024- 00088-1. * Brendan Brown, Brendan.brown@csiro.au | 1International Maize and Wheat Improvement Centre, Kathmandu, Nepal. 2International Maize and Wheat Improvement Centre, Delhi, India. 3International Maize and Wheat Improvement Centre, Dhaka, Bangladesh. 4Commonwealth Science and Industrial Research Organisation, Adelaide, Australia. http://orcid.org/0000-0002-4498-6399 http://orcid.org/0000-0002-7744-6719 http://orcid.org/0000-0002-0118-4556 http://orcid.org/0000-0003-3801-3221 http://orcid.org/0000-0001-6973-0106 https://doi.org/10.1007/s44279-024-00088-1 https://doi.org/10.1007/s44279-024-00088-1 Vol:.(1234567890) Research Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 missed opportunities to bridge the expanding food demand projected for 2050, while also sparing ecologically fragile lands from agricultural expansion. Empowering farmers in developing countries to transition from manual, traditional harvest and post-harvest practices to mechanized methods presents a critical pathway toward achieving the interconnected Sustainable Development Goals (SDGs) of Zero Hunger (SDG 2), Poverty Alleviation (SDG 1), and Sustainable Consumption and Production (SDG 12). Recognizing the substantial potential for efficiency improvements in harvest and post-harvest operations, align- ing with the ambitious target of UN-SDG 12.3 to halve per capita global food waste by 2030 [6] emerges as a strategic imperative, particularly for developing nations. This shift towards harvest and post-harvest mechanization not only improves efficiency but also helps to mitigate (post-) harvest losses, thereby significantly contributing to food security and sustainable development in these regions. The benefits of harvest and post-harvest mechanization extend far beyond mere efficiency. As we confront increas- ingly unpredictable climates, mechanization offers crucial opportunities for adaptation, particularly in response to erratic rainfall patterns. For instance, mechanized harvesting enables operations in waterlogged and wet conditions [7], while simultaneously enhancing coverage and time productivity [8]. Gender considerations also play a significant role, with women often bearing the brunt of manual post-harvest tasks, which are commonly perceived as the most arduous and disliked aspects of farming [9]. In Bangladesh, studies have demonstrated that mechanization can substantially diminish post-harvest losses (from over 6% to under 2%) and boost overall harvest yields by enabling the collection of shattered paddy [10, 11]. Acknowl- edging these benefits, the Bangladeshi government, especially during the initial phases of the COVID-19 pandemic when labour movements faced restrictions, recognized the urgent necessity to accelerate mechanization in the harvest and post-harvest sector [12]. This endeavour involved an ambitious program aimed at providing subsidized harvesting machinery with discounts of up to 70% [7]. The government’s objectives encompass reducing post-harvest crop losses, particularly in rice, by 15%, shortening cultivation time by 50%, and decreasing costs by 20% [13]. Despite the growing interest of the Bangladeshi government in harvest and post-harvest mechanization [11], tradi- tional manual methods continue to dominate. These methods not only increase labour requirements but also affect the timeliness of harvesting and processing, leading to significant crop losses. Recent estimates indicate that the country loses approximately 145 million tons of food annually, equivalent to three months’ worth of food for the entire popula- tion [14]. Postharvest losses in food grains in Bangladesh are reported to be around 15%, while in fruits and vegetables, they range from 20 to 25% [15]. The persistence of manual harvest and post-harvest activities presents several challenges in Bangladesh. Rural labour shortages and timing issues during harvest, particularly concerning rice cultivation, result in critical gaps where even minor delays due to flash floods can lead to significant losses [10, 16]. Although some farmers incorporate limited mechanized post-harvest activities alongside manual harvesting [9], the underutilization of mechanization, particularly for non-rice crops, significantly impacts both the quality and quantity of the produce as well as market prices. This is a concerning issue as harvest and post-harvest activities are acknowledged as the most labour-intensive and costly aspects of crop production in Bangladesh [16]. Despite considerable policy and programming investments, robust information on mechanization in harvest and post-harvest processes remains limited, especially beyond rice. Existing studies often provide simplistic estimations, overlooking nuanced realities of penetration and adoption rates for diverse mechanized options [17]. Furthermore, research tends to oversimplify farmer classifications into adopters and non-adopters, neglecting local contexts and focusing solely on individual adoption rather than collective action [18–21]. Such studies emphasise the need to explore diverse motivations and context-specific approaches, urging for nuanced understandings of the non-binary process and incremental changes for sustainable mechanization. However, existing assessments primarily depend on aggregations of total machine counts extracted from sometimes unreliable methods, lacking the depth, academic rigor, and district- level representation necessary for comprehensive analysis [16]. This research aims to counter this lack of nuanced understanding of the status of harvest and post-harvest mechanisa- tion though proposal and implementation of a non-binary adoption analysis. To achieve this, we investigate the status of nine different harvest and post-harvest machines across 10 Bangladeshi districts using representative sampling methods. Our objective is to move conversation beyond simple machinery counts of purchase and explore various elements of the adoption process, including awareness, usage, and user experiences. This nuanced understanding will inform strategies for improving user experiences, ensuring sustainable adoption and agricultural intensification, and potentially enabling adoption of climate adaptation and gender-sensitive solutions. Additionally, the innovative methods employed can be applied to understand the adoption status of other machinery types. Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Research 2 Methodology 2.1 Sample Selection and Randomization This study employed a multi-stage, stratified random sampling strategy to achieve a representative sample of households across ten districts in Bangladesh. Ten districts were purposively selected based on their participation in a broader imple- menting project (Cereal System Intensification for South Asia—Mechanisation Extension Activity), focusing the study on regions relevant to the project’s objectives while maintaining geographic diversity across districts of Bangladesh. Within each district, two Upazila were randomly selected using a lottery method to ensure unbiased selection across Upazila within each district. Similarly, within each Upazila, two Unions were selected using a lottery method. Within each Union, five villages were randomly chosen using the same lottery method to ensure unbiased representation within Unions. Since household-level sampling frames were unavailable, an estimated number of households was obtained in each village through consultation with local points of contact. A systematic random sampling approach with a fixed interval was then implemented within each village to ensure households across different village sections were included and to avoid clustering, with the interval ensuring no two sampled households shared a boundary. This multi-stage approach ensured representativeness at the district and Upazila levels through random selection, addressed clustering concerns, and provided a nuanced understanding of the pathways to improve user experiences with harvest and post-harvest machines and next steps to ensuring sustainable adoption and agricultural intensification. However, potential limitations arose due to unavailable household-level sampling frames, and voluntary participation might introduce response bias, although offering the interview to the prior participant mitigated this to some extent. Training on enumerators on the need for the sampling frame was crucial to ensuring correct implementation. The main decision-making household head was invited for a voluntary interview, targeting the individual most knowl- edgeable about household decisions and activities. Participation was non-remunerated and based on voluntary consent, and in case of refusal, the immediately preceding household was offered the interview to minimize selection bias due to refusals. The final sample comprised 1,000 respondents from 203 villages, corresponding to 40 Unions (i.e. rural councils), 20 Upazila (i.e. sub-districts), and 10 districts. For District, Upazila and Unions sampled see Supplementary Material: SM1. 2.2 Questionnaire and survey A structured survey questionnaire designed to collect comprehensive data on household demographics, machinery usage, and decision-making perspectives was employed. This was implemented through Kobo Toolbox on the humanitar- ian response server (kobo.humanitarianresponse.info) using Android tablets. Implementation of the survey involved a team of 16 enumerators who underwent thorough training and piloting sessions with the questionnaire, ensuring their proficiency in administering the survey. Respondents were verbally asked for their informed consent before starting the survey and after being provided information about their free will to participate and discontinue at any point during the interview. The questionnaire commenced with inquiries into demographic and wealth-related information. A repetitive question framework was then employed to explore farmers’ experiences and viewpoints regarding various types of machinery, encompassing different stages of engagement such as awareness, experimentation, utilization, and non-use. Each itera- tion of the questionnaire included a generic image of the machinery under discussion, free from branding, to ensure participants’ complete understanding. Skip patterns and validation rules were strategically applied to enhance data quality and prevent redundancy, mandating responses to each question asked. The questionnaire utilized a combina- tion of categorical, numerical, and open-ended questions to capture diverse responses effectively. The questionnaire schedule covered 22 machines (See Supplementary Material: SM2), across five machinery categories: (1) land preparation, (2) planting, (3) irrigation, (4) harvest, and (5) postharvest. This paper reports specifically on harvest and postharvest machinery categories, specifically: Reapers, Harvesters, Threshers, Shellers and Choppers. The survey was piloted with an initial group of farmers to improve survey quality, wording and enumerator understanding, though this pilot data was not used in the presented results. Respondents were verbally asked for their informed consent before starting the survey and after being provided information about their free will to participate and discontinue at any point during the interview. The protocol was conducted under and approved by the CIMMYT Ethics Review board in accordance with the CGIAR ethical research parameters. All data management and analysis was undertaken in Microsoft Excel, while the Sankey figures were generated at SankeyMATIC (https:// sanke ymatic. com/). https://sankeymatic.com/ Vol:.(1234567890) Research Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 2.3 Analytical approach A stepwise approach was employed for adoption analysis, departing from traditional cross-sectional assessments of binary adoption outcomes. This aligns with previous literature that has advocated for a deeper understanding of adop- tion as a dynamic process rather than a binary outcome (e.g., [22–25]). Such works highlight the need to consider the stages of adoption in a stepwise process rather than simply categorising farmers as ‘adopters’ and ‘non-adopters’. Central to this work is the conceptualization of adoption as a stepwise process, transcending the binary adopter vs. non-adopter dichotomy. This approach draws from the Process of Agricultural Utilization Framework (PAUF; [22]), initially applied to investigate the status of Zero Tillage and Conservation Agriculture in Eastern and Southern Africa. Adapted to examine mechanization status in the Nepal Terai, this framework evolved into the Stepwise Process of Mechanization Framework (SPM; [20]). Building on this basis, this study employs various analytical approaches to explore mechaniza- tion status as outlined below. 2.3.1 Updated Stepwise Process of Mechanisation framework (v2) This study operationalizes an adapted SPM framework, classifying respondents into five phases: Exposure, Assessment, Continuation, Utilization, and Ownership. The original SPM framework [20] framed the adoption process in 10 steps. In this work, we expand this to 12 stages, with the original ‘Unassisted user’ disaggregated into two: Periodic Users and Constraint Users. This change enables a deeper understanding of machinery usage patterns beyond simply existing users. Additionally, a first category of ‘Not relevant’ is added, given that this work investigates a wide array of machinery that may not be relevant to every farmer (e.g. non maize growers will not find any relevance in using a Maize Sheller). The adapted SPM framework is provided in Fig. 1. 2.3.2 Adoption pathway analysis Our applied visual analysis has origins drawn from the ’Adoption Pathway Analysis’ framework [24], which visually framed interconnected stages of awareness, trial and non-trial evaluation, adoption and non-adoption, as well as modification and disadoption. Based on the categories of the SPM framework, a visualised stepwise gated intermediate outcomes process was devised to trace the journey from awareness to utilization (or non-utilization) via assistance (or lack thereof), stoppages, and ownership. Recognizing the varying stages of adoption among different machines, a series of ratios were also developed to facilitate analysis, particularly in early adoption phases where proportions of the total population may be minimal. Figure 2 provides a breakdown of each category, stage, and ratio calculation, offering insights into the proportional pathways leading to adoption outcomes, especially in the initial stages of adoption. 2.3.3 Development of awareness, adoption and use curves Developing awareness and adoption curves presents challenges, primarily due to the complexity of obtaining panel data across diverse locations and countries. To address this, this study collected recall data on the timing of farmers’ awareness, first use, and last use of machines. In instances where farmers could not recall specific years, ’unknown’ was recorded and proportionally allocated based on basic typologies to ensure representativeness across the population. 3 Results 99% of respondents identified as male, with the average age of 47 years and with 27.5 years of farming experience. 34% of respondents had completed middle school or above. The household had an average of 183.3 deciles of land, 5.3 household members and 0.7 household members drawing an off-farm income. Full summary statistics by district are given in supplementary information 3 (SM3). 3.1 Binary machinery adoption rates (2021) The proportion of farmers using at least one of the nine investigated harvest and post-harvest machinery was 73.6%. However, the fallacy of binary statistics such as this become apparent with a deeper investigation of such results. For Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Research instance, this is nearly exclusively related to uptake of the power thresher (63.8% of respondents), with no other machin- ery having above 20% adoption across the total sample. Likewise, use of at least one type of post-harvest machine was 73% of respondents, while this was only 5.7% for harvest machinery. Five machines registered adoption rates below 1.5%. Overall, this highlights the risk of aggregating rates of mechanisation into one broad category of ‘harvest and post-harvest mechanisation’. Fig. 1 Adapted Stepwise process of Mechanization Framework Vol:.(1234567890) Research Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 The same is apparent when assessing by district. For example, while 97% of those in Patuakhali use at least one of the investigated machinery, this is reduced to only 41% in Magura. Such results underscore the significance of district-level disaggregation of data, as it offers nuanced insights that complement nationally aggregated statistics. Aggregated and disaggregated results are provided in Supplementary Materials 3 (SM3), given the objective of this paper is to reach beyond binary adoption statistics. The binary adoption percentages are provided as a reference point to highlight the need for the proposed deeper analysis presented in the remainder of this paper. 3.2 Awareness curves Of the nine machines investigated, the self-propelled reaper, flow fodder chopper and silage fodder chopper returned minimal awareness rates. The self-propelled reaper demonstrated negligible awareness, with no district reporting aware- ness levels exceeding 5%. Initial awareness of the flow fodder chopper emerged in 2016, with only Rajshahi district report- ing awareness levels surpassing 5%, while awareness of the Silage fodder chopper was solely reported in Cox’s Bazaar district, at only 1%. As such, these machines are not presented with the awareness curves for the other six machines in Fig. 3. While substantial awareness growth of the thresher commenced in the late 1990s, other machinery have gained recognition in more recent years, which is particularly evident for the two-wheel tractor reaper and combine harvester. With the exception of these two machineries, peak awareness levels appear to have been approached, though a con- sideration of the impact of Covid-19 may be necessary here where a plateau may have been experienced while limited extension activities were active. Future studies may explore if a subsequent awareness spike occurred once extension activities resumed. Notably, there is the vastly varied rates of awareness growth on the same machinery in different districts, especially for the wheeled fodder chopper. Fig. 2 The Pathway visualisation for the Stepwise Process of mechanisation Framework (Example data visualised) Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Research Fig. 3 Awareness curves developed for each of the six-machinery with awareness rates above 10% in at least one district. The x-axis is the period of investigation, while the y-axis denotes the percentage of population for each district Vol:.(1234567890) Research Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 3.3 Adoption curves Only three machines had a national adoption rate above 5%: the Thresher, Wheeled Fodder Chopper and Sheller (Fig. 4). For the thresher, the majority of adoption occurred during the early 2010s, with peak adoption observed across most districts within the same decade. Despite concerted efforts from both public and private agencies to disseminate mecha- nization technologies as early as the 1990s, the adoption of the thresher gained momentum only in 2000 in the south- ern district of Patuakhali, while adoption commenced in most other districts only between 2004 and 2010, and in the economically disadvantaged district of Rajshahi, adoption was delayed until 2014. In Magura, a significant decline in adoption post-2017 was observed for thresher, diverging from trends observed in other areas. In terms of the sheller, predominant adoption occurred in the late 2010s, albeit with only three districts surpassing the 10% adoption threshold, and Dinajpur emerging as the sole district to achieve peak adoption by 2021. Conversely, adoption of the Wheeled fodder chopper appears more recent, with certain districts plateauing in adoption since 2019. Once again, district disaggregated data is key to a wider understanding of adoption trends. 3.4 SPM Pathways Analysis The SPM pathways analysis for the four machines with a national adoption rate above 1.5% (the maize sheller, the thresher, the combine reaper, and the wheeled fodder chopper) are presented to visually gain an understanding of their utilisation status (Fig. 5). All four pathways exhibit a familiarity rate (awareness: total population) exceeding 10% and a progression rate (users: all who are aware) surpassing 2%. The SPM pathway analysis highlight two divergent journeys for the investigated machinery. Both the Thresher and Sheller have substantial rates of familiarity (when relevant) and progression from familiarity to use (87% and 95% respec- tively). Essentially, this means that populations who find the machinery relevant to their livelihoods and are aware of the machine have adopted the machine. This diverges from the wheeled fodder chopper and combine reaper, where the opposite is true: Despite relevance, most of the populations lack familiarity with the machine and few have progressed from awareness to use (19% and 5% respectively). While time may be expected to explain some of this, only the thresher has a substantially different timeline in terms of introduction to communities (see Fig. 3). For each of these machines, those that have not adopted were primarily categorised as ‘Not Interested’. While pre use disinterest in the sheller was minimal, pre-use disinterest was otherwise primarily driven by comfort with current practices (Thresher—72%; Wheeled fodder chopper—58%; Combine Reaper 69%) followed by performance concerns Fig. 4 Historical adoption of harvest and post-harvest machinery in Bangladesh from 1988 to 2021. The x-axis is the period of investigation, while the y-axis denotes the percentage of population for each district. Other machinery are not presented due to negligible adoption Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Research Fig. 5 SPM pathway visualizations for the four machines that exhibit sufficient familiarity and awareness to present meaningful results Vol:.(1234567890) Research Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 (wheel fodder chopper and thresher) and land suitability (combine reaper). This was mostly consistent amongst remain- ing machinery, though sample sizes for pre use disinterest was limited and should be treated with caution with those machines. Although continued dis-adoption remained minimal, periodic utilization was observed, particularly with the thresher. Instances of dis-adoption were largely driven by inhibition (i.e. constrained disadoption) for the sheller and reaper— primarily a reflection of limited access to machinery and performance issues, and 70% of respondents identified access constraints for the combine reaper. Alternatively, deliberate disadoption was primary for the wheeled fodder chopper and thresher, driven by performance concerns. Access was not a concern for either the wheeled fodder chopper or thresher with more than 70% of respondents identifying no access issues. Rarely for any machinery was landowner permission, crop choice, ceased subsidies or operator skills the motivation for disadoption. Ownership across all machines, remained restricted to a small fraction of users, and the dominant mechanisms of access was via service providers for all machines. Intention to own any of the machines in the future was always below 20%, while awareness of subsidies on any machinery was always below 10%. Only the silage fodder chopper has more than 5% of respondents able to access loans, while the average percentage of respondents able to access loans for any machinery was 2%. Where loan sources for machinery were considered, banks were not the most common source, with other financial institutions and friends and family as likely to identified as a source of potential funds to procure machinery. This highlights that private ownership of machinery is likely to remain minimal for the foreseeable future given limited interest, subsidy awareness and loan access available to respondents. The value of the pathways approach can be seen in comparing two machines that have similar binary adoption rates: the Sheller (16%) and the Wheeled fodder chopper (18%). Based on the binary adoption results, as assumption could be made that they are similar in adoption status. However, the pathways analysis highlights divergent experiences. For example, relevance in only 33% for the sheller (irrelevance to non-maize growers) compared to 0% for the Wheeled fod- der chopper. Likewise, 95% of those aware have progressed to use for the sheller, while this is only 50% for the Wheeled Fodder Chopper, suggesting a different level of community perception and satisfaction with each machine. In terms of ownership, only 2% of sheller users own their machine, which increases to 18% for the wheeled fodder chopper. This is likely to have vastly different implications on access for machinery renters and service providers, as confirmed by the inhibitions rates for each machine; 88% of disadopters are constrained, while only 25% are constrained for the wheeled fodder chopper. Overall, this highlights the value in non-binary approaches to understanding not just adoption rates, but adoption status to create more nuanced understandings of past and future requirements to enable change. 3.5 District‑specific machinery utilization patterns The SPM analysis (presented by district) highlights the importance of district aggregated and non-binary adoption monitoring methods (Fig. 6). Such methods highlight large divergence in status between different machines, and the same machines in even adjoining districts. For example, while nationally the two Wheel tractor reaper has a negligible awareness rate, Natore has an awareness rate of 38%, though nearly all of that is filled with unfamiliarity and disinter- est. Similarly, for the self-propelled reaper, nationally awareness is low, but 50% of respondents in Patuakhali indicated awareness yet disinterest. Such situations raise both the necessity of district disaggregated analysis and district specific approaches to extension efforts. Likewise, the power thresher provides a good case study to explore usage divergence by district. Despite Rangpur and Magara having similar adoption rates, at least a third of adopters in Magura have dis-adopted and another third are periodic, of which both categories are almost absent in Rangpur. Likewise, we see ownership disadoption in Magura and Natore. Regarding the combine harvester, Patuakhali stands out as the sole location with substantial awareness and usage of the full header, while Rajshahi exhibits a predominant lack of interest, particularly in the reaper-style combine harvester. Such divergence across districts again highlights the need for disaggregated and non-binary adoption datasets and underscore the necessity for district-specific extension and promotional strategies to effectively target and support the majority of the population interested in adoption. 3.6 Awareness raising and extension preferences Given the persistently high levels of unawareness surrounding many of the investigated machines, a more nuanced examination of information system satisfaction is warranted. Figure 7 presents the current and preferred main sources of information for each machine. Respondents indicated that only 46% of the time did they receive information from Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Research 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar Dinajpur Rangpur Rajshahi Natore Jhenaidah Magura Faridpur Jashore Patuakhali Cox's Bazar R ea pe r ( w ith 2 W T) S el f p ro pe lle d Re ap er C om bi ne H ar ve st er (R ea pe r S ty le ) C om bi ne H ar ve st er (F ul l H ea de r) P ow er T hr es he r P ow er S he lle r W he el ed F od de r C ho pp er F lo w F od de r C ho pp er S ila ge F od de r C ho op er Re ap er Co m bi ne H ar ve st er Tr es he r/ Sh el le r Fo dd er C ho pp er s Not Relevant Unaware Unfamiliar Interested Not Interested Deliberate Constrainted Assisted Periodic Constant Owner Owner (Disadopt) Fig. 6 SPM pathway visualizations for the studies machinery Vol:.(1234567890) Research Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Main Source Redistribu�on Preferred Sa�sfac�on Reaper (a�ached to a Two Wheel Tractor) Gov. Extension 39% 42% 54% Farmers 13% 9% 29% Farmer Groups 4% 4% 0% Federa�ons 7% 0% 0% Local NGOs 11% 27% 75% INGOs 5% 9% 80% TV / Radio 4% 1% 0% Internet 15% 4% 6% Other 2% 6% 100% Self­propelled Reaper Gov. Extension 21% 61% 50% Farmers 35% 9% 19% Farmer Groups 4% 1% 0% Federa�ons 9% 5% 57% Local NGOs 1% 11% 0% INGOs 3% 4% 100% TV / Radio 12% 1% 0% Internet 7% 3% 0% Other 8% 4% 33% Combine Harvester (Reaper Style) Gov. Extension 31% 61% 64% Farmers 43% 16% 23% Farmer Groups 2% 1% 0% Federa�ons 5% 3% 33% Local NGOs 10% 16% 37% INGOs 1% 3% 50% TV / Radio 3% 0% 0% Internet 4% 1% 6% Other 1% 0% 0% Combine Harvester (Full Header) Gov. Extension 18% 41% 80% Farmers 24% 32% 40% Farmer Groups 10% 10% 13% Federa�ons 6% 0% 0% Local NGOs 27% 15% 9% INGOs 0% 1% n/a TV / Radio 9% 1% 14% Internet 6% 0% 0% Other 0% 0% n/a Power Thresher Gov. Extension 20% 44% 76% Farmers 63% 36% 52% Farmer Groups 2% 2% 36% Federa�ons 8% 4% 25% Local NGOs 5% 8% 39% INGOs 0% 6% n/a TV / Radio 0% 0% n/a Internet 1% 1% 38% Other 0% 1% 100% Power Sheller Gov. Extension 17% 40% 25% Farmers 70% 34% 45% Farmer Groups 1% 2% 0% Federa�ons 2% 1% 0% Local NGOs 10% 22% 67% INGOs 0% 0% n/a TV / Radio 0% 0% n/a Internet 2% 1% 33% Other 0% 0% n/a Finger Fodder Chopper Gov. Extension 8% 31% 23% Farmers 76% 42% 54% Farmer Groups 1% 1% 20% Federa�ons 4% 6% 41% Local NGOs 7% 15% 59% INGOs 0% 1% n/a TV / Radio 1% 1% 0% Internet 3% 2% 33% Other 1% 1% 33% Fig. 7 Comparison of the current main (left) and preferred main (right) information source for each of the presented machinery Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Research their preferred main provider, underscoring a misalignment between information systems and respondent desires. Satisfaction rates varied across sources, with international organizations (82%) and government extension services (62%) receiving the highest rates, while farmer federations (28%), farmer groups (16%), internet (14%), and media (TV and Radio) had considerably lower satisfaction rates at just 3%. Satisfaction levels also varied by machine. For instance, while there was an overall 62% satisfaction rate with government extension services, this dropped significantly to 25% for the power sheller and 23% for the wheeled fodder chopper. Similarly, satisfaction with learning from neighbouring farmers was 47%, but notably lower for reapers and combine harvesters compared to threshers, shellers, and fodder choppers. This trend persisted across various information sources, emphasizing the need for machinery-specific promotional strategies to effectively meet community needs. 4 Discussion On a simple binary assessment of harvest and postharvest mechanisation, our results are consistent with broad statements of a limited mechanization rate with the only dominant mechanization in the threshing of rice [9, 17]. However, the real purpose of this research is to highlight that the use of binary adoption statistics, often at national level, come with many challenges to the interpretation and translation to policy and programming [20, 22]. As stated, the aim of this research is to highlight that the national harvest/ postharvest mechanization rate of 73.6% provides little actionable meaning to policy and programming. The proposed in-depth temporal and spatial analyses have proven this to be the case by highlighting new depth of meaning can be ascertained that is relevant to more data- driven and nuanced policy and programming. For example, the summary aggregation of a ‘harvest and post harvest’ adoption rate (73.6%) is shown to be prob- lematic, given that no machine other than the power thresher has been adopted by more than 20% of the surveyed population, and the harvest mechanisation rate is only 5.7%. This highlights that while it may be tempting to report aggregate mechanisation rates, it had the potential to cloud understanding and should be avoided, or at minimum reported with disaggregated statistics. The ability to cite a single number as representative of a whole sector and country may lead to poor policy and programming assumptions. This work also highlights that classifying adoption in a binary manner is problematic, and there is value in applying the categorisation of adoption and non-adoption through the SPM framework. This is clearly exemplified though comparing sheller (16%) and fodder chopper (18%) adoption, which have similar binary adoption rates. By consider- ing relevance, progression, ownership and inhibitions, it becomes evident that the status of these two machines is vastly different and cannot be captured by binary statistics. This has substantial implications to policy and program- ming approaches—by creating another level of understanding of adoption status (not adoption), the approach to promotion requires adaptation. For example, extension efforts must differ between the sheller, where 95% of those aware have progressed to use and more focus needs to be applied in incentivising ownership (2% ownership and 88% inhibited dis-adoption), compared to the Wheeled Fodder Chopper where only 50% have progressed and focus is needed on overcoming constraints to progression. Likewise, the power tiller has been similarly adopted in Rangpur (33%) and Magara (38%), yet at least a third of adopters in Magura have dis-adopted and another third are periodic users, of which both categories are almost absent in Rangpur. This would again have substantial implications on the development of effective policy and programming between the two districts. The aggregation of data to country level is common in Bangladesh where governance is centralised at the national level, yet this work highlights that such aggregation is quite problematic. The use of the district disaggregated SPM approach highlights just how varied adoption can be in even adjoining districts. This is exemplified in particular by the large divergence in status of the wheeled fodder chopper, power sheller and power thresher. For example, the power thresher has distinct adoption curves for each district despite a similar starting year, with varied lag times, inflections, peak adoption and rates of dis-adoption. This is true both of machinery introduced in the same districts at the same time, and how very different awareness and adoption rates are for the same machine in different districts. The benefits of the SPM approach and visualisations cements the argument that more in depth analysis provides increased nuance in understanding. These approaches enable the identification of district specific trends that war- rant further explanation, and accordingly implies that national policy approaches need to be supplemented by more district and context specific policy support, given national level policy making may not provide the best outcomes. Beyond avoiding aggregation at spatial, machinery and adoption levels, this work also uncovers potential concerns in the exiting information pathways available to Bangladeshi farmers. Peak awareness of machinery is occurring in Vol:.(1234567890) Research Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 many situations, but this is often at suboptimal levels (e.g. at low proportions of the population). This highlights potential breakdowns in the inclusiveness and impact of current extension mechanisms related to agricultural mecha- nisation. The exploration of current and preferred sources of information confirms this, with a lack of satisfaction with how extension is delivered. Result suggests a higher satisfaction with international organisations and government programs to receive information, while satisfaction with farmer federation and other farmers reasons low. Similar results are evident in the evaluation of farmer led programming in other locations and require an analysis of why such systems appear to have such low satisfaction rates [26]. Again, supporting the need for more nuanced policy and programming, the same farmers are often identifying different preferred sources for different machinery. This again highlights the need for both district and machinery specific extension strategies to enable rapid mechanisation. It also indicates the need for a diversity of strategies to target one machine, in one district (e.g. no silver bullet to extension). This will likely require in depth qualitative explorations of decision processes, preferences and drivers of change at farmer level. The answer may also not lie in current modern extension approaches, with internet and media having the lowest satisfaction rates of the surveyed information exchange mechanisms. More broadly, these results have wider implications on how we discuss and approach catalysing change in agricultural contexts. It is evident from the temporal analysis that there is a collective need to temper expectations about how long awareness raising takes in developing country contexts. While the power thresher has 8 of the 10 investigated districts with at least 75% adoption by 2021, this has been a four-decade long process. Only a decade before, two of those eight districts had awareness below 25% and another five below 50% awareness. Likewise, the SPM approach highlights access and ownership concerns that do not seem to have a rapid fix—service provision models via private ownership dominate, yet the ambition and ability of private ownership in these populations is very limited. This is likely to limit any further mechanisation potential. This highlights that raising awareness and creating change need patience, at least given the existing extension mechanisms. Methodologically, adoption curves are difficult to find in the literature, and their absence has been an inhibition to the development and validation of ex-ante adoption tools such as the ADOPT tool [27–29]. Expanding the methodol- ogy to develop adoption curves through recall data and overcoming the need for panel data, this approach is hoped to contribute to the further development and validation of scaling tools and literature (noting potential issues with recall data). The method is also useful in identifying where strong growth of adoption occurred and where further investiga- tion could be undertaken to explore the attributes of how change rapidly occurred in some locations but not others. In terms of study limitations, it was outside of the scope of this work to explore and explain the 90 possible districts by machinery results (including awareness, adoption, SPM classification and extension preferences). This work is a process- based exploration, and our stated ambition is to push the discourse in adoption literature beyond binary adoption statis- tics, not to explain exact outcomes. This raises the need for exploring why these trends have has such divergence, which should be explored through in-depth qualitative ways. Recent examples of such types of in-depth qualitative studies in South Asia highlight the prevalence of poverty traps in mechanisation initiatives (e.g. [25]), gender as a key driver of agricultural change (e.g. [30, 31]), breakdowns in the adoption process causing stagnation before use (e.g. [32]), and a lack of incentive for mechanisation service provision (e.g. [33, 34]). Such work was catalysed by initial process-oriented works that identify key trends to be explored (e.g. [20]). Similar processes are identified in the sustainable intensifica- tion of African agricultural systems, first though identification of key trends (e.g. [22]) and then unpacking the contexts of these qualitatively though dis-adoption, constraints to progression and enabling environments (e.g. [35–38]). While out of scope of this work, further research should also be targeted towards translation of a typologies approach into actionable policy directives, while the impact of hire costs, the contribution to rural unemployment and perpetuation of poverty traps would all warrant deeper analysis. 5 Conclusion Despite significant policy attention, the understanding of harvest and postharvest mechanization in Bangladesh has been hindered by limited studies, often relying on simplistic binary adoption percentages and focusing primarily on rice threshing. Our more nuanced analysis, employing the Stepwise Process of Mechanization (SPM) framework across 10 representative districts, reveals that harvest and postharvest mechanization is still primarily in its infancy or experiencing recent growth in awareness. Only three out of nine investigated machines—specifically the thresher, sheller, and fodder chopper—along with the reaper to a certain extent, demonstrate appreciable adoption rates. Vol.:(0123456789) Discover Agriculture (2024) 2:73 | https://doi.org/10.1007/s44279-024-00088-1 Research Though our proposed in-depth approach, we conclude there is value in changing how we approach adoption report- ing, especially with reference to aggregation of results. First, there is value in avoiding the aggregation of whole elements of the farm calendar (e.g. ‘harvest and post-harvest mechanisation’), especially where one machine dominates. Secondly, there is value in exploring beyond nationalised statistics, due to the large variance in adoption status across even adjoin- ing locations. Finally, there is a need to avoid binary adoption reporting, and classify adoption and no adoption into the requisite pathways to ensure a true understanding of adoption status can occur. Doing this provides much greater depth of understanding on the required policy and programming to enact adoption. More specially to Bangladesh, this work highlights the need to explore in more depth the constraints to a highly func- tioning extension system. Peak awareness rates are occurring at relatively low levels, implying that there are limitations in the reach of information systems widely and subsequent potential inclusivity considerations. Likewise, at only 46% satisfaction rate, respondents also indicate that they do not receive information in the ways they would prefer. Overall, the results indicate that Bangladesh needs to consider more localised and context specific policy and programming, given the diversity of awareness growth, adoption status and extension preferences that vary by both location and machinery. Overall, this work provides new knowledge on adoption process though a novel approach that aims to push the dis- cussion about adoption beyond binary summary statistics. The value is highlighted in providing additional data driven understanding to policy and programming. Next steps include more in-depth exploration of identified trends that can feed into more localised and context specific policy and programming. Acknowledgements We gratefully acknowledge the support of (1) the CGIAR initiative: Transforming Agrifood Systems in South Asia (TAFSSA) and (2) the Cereal Systems Initiative for South Asia in Bangladesh (CSISA-MEA) project funded by the United States Agency for International Development (USAID). Additional thanks are provided to the Asian Productivity Organisation (APO) and the CSIRO Impossible Without You program for their support of this work. The views expressed in this paper are those of the authors and do not necessarily reflect the views of funding bodies, nor should they be used for advertising purposes. Author contributions Conceptualization (BB and TK); Data curation (PT, AS, BB); Formal Analysis (BB); Funding acquisition (TK, BB); Investigation (AS, PT); Methodology (BB); Project administration (BB and TK); Supervision administration (BB and TK); Visualization (BB); Writing—original draft (BB); Writing—review & editing (All authors). Funding This work was supported by the Bill and Melinda Gates Foundation under Grant Cereal Systems Initiative for South Asia— Mechanization Extension Activity (CSISA-MEA); and the Asian Productivity Organisation under Grant 23-RC-21-GE-RES-A. Data availability Data is provided on reasonable request. Declarations Ethics approval and consent to participate The research protocol was approved by the CIMMYT Ethics Review board in accordance with the CGIAR ethical research parameters. Consent for publication Respondents were verbally asked for their informed consent before starting the survey and after being provided information about their free will to participate and discontinue at any point during the interview. Competing interests The authors declare no competing interests. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. https://doi.org/10.1016/j.jrurstud.2021.09.025 https://doi.org/10.1016/j.jrurstud.2021.09.025 https://youtu.be/wwYzsqiYM8k https://doi.org/10.1177/10185291241259809 https://doi.org/10.1080/1389224X.2018.1439758 https://doi.org/10.1016/j.landusepol.2018.02.009 https://doi.org/10.1017/S1742170518000108 Stepwise methods for more nuanced adoption analysis: a case study of harvest and post-harvest mechanization in Bangladesh Abstract 1 Introduction 2 Methodology 2.1 Sample Selection and Randomization 2.2 Questionnaire and survey 2.3 Analytical approach 2.3.1 Updated Stepwise Process of Mechanisation framework (v2) 2.3.2 Adoption pathway analysis 2.3.3 Development of awareness, adoption and use curves 3 Results 3.1 Binary machinery adoption rates (2021) 3.2 Awareness curves 3.3 Adoption curves 3.4 SPM Pathways Analysis 3.5 District-specific machinery utilization patterns 3.6 Awareness raising and extension preferences 4 Discussion 5 Conclusion Acknowledgements References