بررسی پایداری روش‌های سنتی فرآوری برنج در میان کشاورزان خرد برنج در جنوب شرق نیجریه

نوع مقاله : مقاله پژوهشی انگلیسی

نویسندگان

• چیگوزیریم اونوسیریبه ¹
• جود مباناسور ¹
• فیلیپس اِن‌تو ¹
• مک‌مانِس اندوکوو ²

¹ دانشکده کسب‌وکارهای کشاورزی و مدیریت، دانشگاه کشاورزی مایکل اوکپارا، ایالت آبیای نیجریه

² دانشکده مهندسی کشاورزی و منابع زیستی، دانشگاه کشاورزی مایکل اوکپارا، ایالت آبیای نیجریه

https://doi.org/10.22067/jam.2024.89115.1267

چکیده

برنج یکی از غذاهای اصلی در سراسر جهان است، اما فرآوری آن به دلیل مصرف آب و انرژی و انتشار گازهای گلخانه‌ای، اثرات قابل‌توجهی بر محیط‌زیست دارد. در نتیجه، تولیدکنندگان برنج سعی می کنند روش‌های فرآوری پایدار را برای کاهش اثرات منفی زیست‌محیطی و افزایش سودآوری اتخاذ کنند. این مطالعه میزان پایداری روش‌های فرآوری برنج شالیزاری (شالی) مدرن و سنتی را در میان کشاورزان خرده‌مالک شالیکار در جنوب شرق نیجریه تحلیل می‌کند. داده‌ها از 240 تولیدکننده برنج با استفاده از رویکردهای آماری مانند آمار توصیفی، شاخص پایداری (تحلیل نسبت ارزیابی وزن‌دهی) و تحلیل رگرسیون چندجمله‌ای جمع‌آوری شد. نتایج نشان داد که 34.7 درصد از کشاورزان شالیکار از روش‌های نوین و 65.3 درصد از روش‌های سنتی فرآوری استفاده می‌کردند. از ‌نظر 77 درصد کشاورزان خرده مالک، آسیاب کردن سنتی باعث انتشار میزان قابل‌توجهی گاز کربن می‌شود و همچنین 68 درصد کشاورزان آلودگی صوتی را در حد بالایی ارزیابی می‌کنند. 80 تا 100 درصد کشاورزان خرده مالک که از تکنیک‌های مدرن استفاده می‌کنند به محیط‌زیست اهمیت می‌دهند و می‌خواهند انتشار گاز، زباله‌های جامد، مصرف انرژی و مصرف آب خود را کاهش دهند. شاخص پایداری در کشاورزانی که از روش‌های فرآوری سنتی یا مدرن استفاده می‌کنند تحت‌تاثیر جنسیت، تعداد نیروی کار، سرمایه‌گذاری، درآمد، هزینه تولید، میزان درک تغییرات آب و هوا و پایداری محیطی قرار می‌گیرد. این مطالعه استفاده از منابع انرژی تجدیدپذیر را برای افزایش بهره‌وری و کاهش اثرات زیست‌محیطی توصیه می‌کند.

کلیدواژه‌ها

• آلاینده
• اثرات زیست‌محیطی
• انتشار گازهای گلخانه‌ای
• سوددهی
• سرمایه‌گذاری
• مصرف انرژی

موضوعات

• تکنولوژی‌های پس از برداشت

Introduction

Rice is a staple food for millions of Nigerians and plays a crucial role in food security across West Africa (Akinbile, Ogunmola, Abolude, and Akande, 2020; Soullier, Demont, Arouna, Lançon, and Del Villar, 2020). Rice consumption in Nigeria is projected to rise by 4% to 7.8 MMT in MY (Million Metric Tons in Marketing Year) 2023/24, marking an increase in consumption compared to other African staples like wheat and corn (United United States Department of Agriculture Foreign Agricultural Service, 2023). Smallholder farmers are the primary producers of rice in Nigeria (Esiobu, Onubuogu, Njoku, and Nwachukwu, 2020; Onyeneke, Amadi, Njoku, and Osuji, 2021), and the government has implemented policies to boost local production and reduce imports (3Ikebudu, 2021; Karkare, Odijie, Ukaoha, and van Seters, 2022).

The production and processing of rice involve complex procedures requiring various technologies (Sethy, Barpanda, Rath, and Behera, 2020). While large-scale operations utilize highly-mechanized processes (Materne and Reddy, 2007), most smallholder farmers in Nigeria still rely on traditional, labor-intensive methods (Bello, Baiyegunhi, and Danso-Abbeam, 2021; Obianefo et al., 2021). These traditional techniques, though less efficient, are perceived to preserve food quality and nutrition better than modern methods (Adekoyeni, Fagbemi, and Ismaila, 2018; Danbaba et al., 2019; Zhao, Lin, and Chen, 2020).

However, the increasing costs of labor and resources are pushing more smallholder farmers towards adopting more efficient rice production methods to remain competitive (Akite, Okello, Kasharu, and Mugonola, 2022; Bello et al., 2021; Obianefo et al., 2021). This shift presents opportunities for the sustainable development of Nigeria's small-scale modern rice processing industry (Kosemani and Bamgboye, 2020; Onyekwena, 2016; Salisu, Gao, and Quan, 2021).

Both traditional and modern rice processing techniques have environmental implications, including carbon emissions, solid waste generation, water pollution, and noise pollution (Chungsangunsit, Gheewala, and Patumsawad, 2005; Kumar, Priyadarshinee, Roy, Dasgupta, and Mandal, 2016; Lenis Rodas, Morales Rojas, Jaramillo Marín, Salcedo Jiménez, and Pérez Bayer, 2022; Lim, Manan, Hashim, and Alwi, 2013). The sustainability of these processes must be evaluated considering environmental, economic, and organizational factors (Batista and Francisco, 2018; Dzhengiz, 2020; Gowda and Jayaramaiah, 1998; Roy, Chan, and Xenarios, 2016).

While previous studies have examined various aspects of rice processing techniques, there are several gaps in the existing literature. First, there is limited research specifically focusing on the sustainability of traditional paddy rice processing among smallholder farmers in Southeast Nigeria. Second, many studies have focused on either environmental or economic aspects, but few have integrated all three dimensions of sustainability (environmental, economic, and organizational) in their analysis. Third, while some researchers have used econometric models to study the effects of various factors on sustainability (De La Maza, Davis, and Azevedo, 2021; Rajabov and Mustafakulov, 2020), there is a lack of comprehensive studies applying both Multinomial Logistic Regression and Weight Assessment Ratio Analysis (WARA) to assess the sustainability of rice processing techniques. Lastly, the existing literature often fails to provide a clear comparison between traditional and modern processing techniques in terms of their overall sustainability within the context of smallholder farming in Nigeria.

Given these gaps, the objectives of this study are:

• 1. To examine the distribution of modern and traditional rice processing techniques among smallholder farmers in Southeast Nigeria.
• 2. To assess the environmental effects of both traditional and modern rice processing methods in the study area.
• 3. To evaluate the sustainability of traditional paddy rice processing techniques using a comprehensive approach that integrates environmental, economic, and organizational factors.
• 4. To identify the key determinants influencing the adoption and sustainability of different rice processing techniques among smallholder farmers.
• 5. To provide evidence-based recommendations for policymakers and stakeholders to promote sustainable rice processing practices in Nigeria.

Materials and Methods

The study was carried out at three locations: Abia, Ebonyi, and Imo states in southeastern Nigeria. These states are predominantly agrarian and major producers of paddy rice (see Figure 1). A two-stage sampling technique was used to select the rice processors. The first stage was the purposive selection of the state's rice-producing communities. The second stage was randomly selecting 240 rice farmers from the selected communities. The researchers visited each farm periodically to collect data about the sustainability performance of the modern and traditional paddy processing systems. Data were collected using a survey and focused group discussion. The data collected were analyzed using statistical tools such as descriptives, sustainability index (Weight Assessment Ratio Analysis), and multinomial regression analysis. The farmers' perception of organizational sustainability was characterized by conducting a content analysis of the transcripts of their responses (Baccar, Bouaziz, Dugué, Gafsi, and Le Gal, 2020). To estimate the factors affecting the sustainability index of traditional rice milling, the following multinomial logit model was adopted.

$\begin{array}{ccc}{P}_{\mathrm{ij}}=\frac{e{B}_j{X}_i}{1+{\sum }_{\mathrm{k=0}}^j{X}^{B}k{X}_i}& \mathrm{...}& \mathrm{(1)}\end{array}$

The explicit form of the model is stated as:

$\begin{array}{cc}\log \left(\frac{p_{\mathrm{ij}}\left(\mathrm{categoryi}\right)}{P_{\mathrm{ij}}\left(\mathrm{categoryref}\right)}\right)=b_0+b_1{X}_1+b_2{X}_2+b_3{X}_3+b_4{X}_4+b_5{X}_5+b_6{X}_6+b_7{X}_7+b_8{X}_8+b_9{X}_9+b_{\mathrm{10}}{X}_{\mathrm{10}}+e_i& \mathrm{(2)}\end{array}$

where:

P= probability

i = 1, 2, 3, ...., n variables

j= categories

Y= high=1, average =2, and Yref= low =5

X₁= level of education of the small-scale farmers, measured in years

X₂=sex (male=1 and female=0)

X₃ = experience of the small-scale farmers,measured in years

X₄ =labor size of traditional rice mills,measured in numbers

X₅ =investment of traditional rice mills in naira

X₆ =income of traditional rice mills in naira

X₇ = cost of production of traditional rice mills in naira

X₈=knowledge of climate change of small-scale farmers using traditional rice mills (yes =1, no=0)

X₉ =knowledge of economic sustainability of small-scale farmers using traditional rice mills (yes =1, no=0)

X₁₀=knowledge of organizational sustainability of small-scale farmers using traditional rice mills (yes =1, no=0)

b_i = the parameter

e_i = the error term

To estimate the factors affecting the sustainability index of modern rice milling, the following multinomial logit model was adopted

$\begin{array}{cc}{P}_{\mathrm{ij}}=\frac{e{B}_j{X}_i}{1+{\sum }_{\mathrm{k=0}}^j{X}^{B}k{X}_i}& \mathrm{(3)}\end{array}$

The explicit form of the model is stated as

$\begin{array}{cc}\log \left(\frac{p_{\mathrm{ij}}\left(\mathrm{categoryi}\right)}{P_{\mathrm{ij}}\left(\mathrm{categoryref}\right)}\right)=b_0+b_1{X}_1+b_2{X}_2+b_3{X}_3+b_4{X}_4+b_5{X}_5+b_6{X}_6+b_7{X}_7+b_8{X}_8+b_9{X}_9+b_{\mathrm{10}}{X}_{\mathrm{10}}+e_i& \mathrm{(4)}\end{array}$

where:

P= probability

i = 1, 2, 3, ...., n variables

j= categories

Y= high=1, average =2, and Yref= low =5

X₁= level of education of the small-scale farmers, measured in years

X₂=sex (male=1 and female=0)

X₃= experience of the small-scale farmers using modern mills,measured in years

X₄= labor size of modern rice mills, measured in numbers

X₅= investment of modern rice mills in naira

X₆= income of modern rice mills in naira

X₇ = cost of production of modern rice mills in naira

X₈=knowledge of climate change of small-scale farmers using modern rice mills (yes =1, no=0)

X₉ =knowledge of economic sustainability of small-scale farmers using modern rice mills (yes =1, no=0)

X₁₀= knowledge of organizational sustainability of small-scale farmers using modern rice mills (yes =1, no=0)

b_i = the parameter

e_i = the error term

The Weight Assessment Ratio Analysis (WARA) model (Dehnavi, Aghdam, Pradhan, and Varzandeh, 2015; Ghenai, Albawab, and Bettayeb, 2020; Zavadskas, Stević, Tanackov, and Prentkovskis, 2018) is specified as follows:

Wj=qik=1mqk (5) $\begin{array}{cc}{W}_j=\frac{q_i}{{\sum }_{\mathrm{k=1}}^m{q}_k}& \mathrm{(5)}\end{array}$

where:

W_j = relative weight criteria of the farmer's response to sustainability questions

i = individual farmer response

j = 1, 2, …, n

q = the rank of the farmer response

m= sample size

Endogenous variables: Information was gathered through questionnaires and interviews with the rice farmers. The data collected is based on the processing activities of rice farmers. The data collected on environmental sustainability was computed using the Weight Assessment Ratio Analysis (see equation 5) to get an index score to measure the environmental impact of rice processing using modern and traditional methods. The high index score is 0.7-1, the average is 0.5-0.69, while the low index is <0.5 (Daniel, Tsoulfas, Pappis, and Rachaniotis, 2004). This indicates the adverse impact of processing rice on the farmer and the environment. The index is the dependent variable for the multinomial logit models (see equations 2 and 4) performed in this study.   

Exogenous variables: The exogenous variable is the independent variable included in equations 2 and 4, which influences the environmental sustainability of traditional and modern rice processing, respectively. The first variable is the level of education of the farmers. Some reports on environmental sustainability considered education as a factor (Anabaraonye, Okafor, and Hope, 2018; Baur, 2022; Jayne, Snapp, Place, and Sitko, 2019; Mancini, Termorshuizen, Jiggins, and van Bruggen, 2008). The sex of the farmers was considered as another variable. Gender, the roles assigned to different sexes that vary across societies and cultures, is an essential component of any sustainability study involving farmers (Aluko, 2018; Jellason, Conway, and Baines, 2021). The experience of the farmers in rice processing and their knowledge of the traditional and modern methods of rice processing were also considered important variables for the study (Ojo and Baiyegunhi, 2020a; Ojo, Baiyegunhi, Adetoro, and Ogundeji, 2021). Moreover, labor size, a measure of the number of workers used to process the paddy, has been included as an important variable as it affects the total time spent on processing the paddy (Kulyakwave, Xu, Yu, and Mwakyusa, 2022; Mujeyi, Mudhara, and Mutenje, 2021). Amount of investment in the processing as an exogenous variable was also considered because the amount of investment in the equipment used in the processing of paddy affects the processing time, which in turn affects the environmental impact of the entire process (Salisu, Gao, and Quan, 2021). Lastly, the cost of production and income are important as they affect the ability of the farmer to purchase inputs needed in the processing of paddy, affecting the processing time and the environment of the process (Onyeneke, 2021).

Results and Discussion

Distribution of rice processing methods among farmers

The distribution of the farmers according to their use of the traditional or modern method of rice processing is presented in Table 1. The table showed that 34.7% of rice farmers use modern rice processing methods, while 65.3% use the traditional method of rice processing. The percentage split showed that the two rice processing methods are popular with rice farmers in Nigeria. However, the data reveals that the percentage of farmers employing traditional rice processing methods in the region is nearly double that of those utilizing modern techniques. The higher number of traditional processing techniques can be associated with the cost of purchase and maintenance of modern processing equipment. Additionally, there is this perception among local processors that the traditional processing of paddy rice is more efficient and reliable than modern rice processing methods (Anthony, Alabi, Ebukiba, and Gamba, 2021; Sissoko, Ohene-Yankyera, and Wongnaa, 2021; Tahir, Onewo, and Alkali, 2022). However, according to Hasan, Tanaka, Alam, Ali, and Saha (2020) and Soullier et al. (2020), this method results in lower throughput capacity compared to farmers who use modern machinery to process their rice. The results agreed with the finding of Yusuf (2018) that rice farmers in Nigeria use more traditional rice processing methods, which have resulted in a low level of processing output and quality of rice produced. This low yield is probably due to the daily drudgery with manual labor involving traditional techniques.

Table 1. Methods of paddy rice processing

Technique of paddy rice processing	Frequency	Percentage
Modern processing	83	34.72803
Traditional processing	156	65.27197
Total	239	100

Effect of processing techniques on riceyield and quality among farmers

The investigation strongly suggested that land availability determines the quantity of rice grown and the amount of rice processed. First, however, we categorize the rice farmers based on the quantity of rice processed (see Figure 2). From the questionnaire, we grouped the processors into three: those that process 0.5 to 5 tonnes of rice daily, 6 to 10 tonnes daily, and 11 to 15 tonnes daily. Then, each group was separated based on those that used modern processing techniques and those that used traditional techniques. Figure 1 shows the quantity of rice produced using traditional and modern processing techniques for each category. According to the graph, in the 0 to 5 tonnes category, about 77% of rice farmers use traditional milling methods, while 8% use modern milling methods. In the6 to 10 tonnes daily category, 58% of processed rice was by modern milling methods, while 20% used traditional milling methods. Furthermore, in the11 to 15 tonnes daily category, 34% used modern milling methods, while 3% used traditional methods. The analysis revealed that the use of milling methods is correlated with the amount of rice produced. However, Figure 2 shows that processors are dominated by rice millers in the category of 0.5 to 5 tonnes daily. The reason is that the farming cluster is dominated by small farm holders whose first objective is to feed their families before selling the excess. This finding agreed with Kyaw, Ahn, and Lee (2018), who asserted that small-scale rice farmers typically mill less rice than is necessary to provide their families with sufficient income. Low-quantity rice milling can result from acombination of available quantity to mill, effective milling systems, and drudgery due to local milling techniques. However, Ndirangu and Oyange (2019) noted that the modern milling procedure could increase rice farmers' daily output from 1.5 to 3 tonnes. This increase in output is because of the availability of high-capacity modern mill machines and efficient material handling techniques, which enable rice farming to be more productive and profitable for small-scale farmers. However, the trend observed among most rice farmers in most Sub-Saharan countries is the continued use of traditional milling methods due to the limited access to reliable and affordable machines (Fatunbi and Kombat, 2020).

The information regarding these quality indices was provided by farmers and processors who were asked to rate the degree of acceptability on a scale of 0 to 5, with 0 being the lowest and 5 being the highest (refer to Figure 2). The participants willingly shared their insights to contribute to the research and provide valuable data on the perceived quality of rice produced using different processing methods. Concerning palatability, the average response from small-scale farmers using traditional milling techniques was 3.5, while those using modern milling techniques rated it at 3. This indicates that the traditionally milled rice was perceived to have better palatability among the respondents. The farmers' input on this aspect helps in understanding the preference for rice processed through traditional methods. However, it is important to note that the nutritional content of traditional and milled rice was not the focus of this research and falls beyond its scope. This acknowledgment is necessary to avoid drawing conclusions regarding the nutritional differences between the two processing methods.

Fig. 1. Quantity of rice produced using the traditional and modern method

Moving on to the assessment of appearance, a comparison between small-scale farmers using traditional milling techniques and those using modern mills revealed notable differences. Traditionally milled rice received a low rating with a mean response of 1.5, while modern milled rice scored significantly higher at 3.7. This difference in ratings can be attributed to the fact that traditionally milled rice retains more of the bran layer of the paddy kernel, as supported by previous studies (Dhankhar and Hissar, 2014; Tong, Gao, Luo, Liu, and Bao, 2019). The farmers and processors' input on appearance offers valuable insights into the visual appeal of rice processed using different methods.

In terms of taste, the respondents rated traditionally milled rice equal to modern rice mills. This implies that there was no significant difference in taste perception between the two processing methods. The input provided by the farmers and processors aids in understanding the taste-related aspects of rice quality and helps in evaluating the potential consumer preferences.

Considering cooking time, the farmers rated traditionally milled rice as having a faster cooking time, with a mean rating of 3.8, while modern milled rice received a slightly lower rating of 3.5 (refer to Figure 2). This information contributes to the understanding of the practical aspects of rice preparation and may influence consumer choices based on convenience and time-saving factors.

Access to available modern rice processing machines

The cluster of rice processing machines available from the respondents was a dehusking, pre-cleaning machine, aspiration or winnowing machine, destoner, paddy separator, weighing machine, sifter, grader, polisher, blender, and bagging/packaging machines. Farmers were questioned about access to each machine in the three study areas. The distribution of the respondents is shown in Figure 3. From the Figure, about 14% and 15% of the rice farmers had access to pre-cleaning and paddy separator machines, respectively. Dehusking, de-stoning, packaging, and weighing machines were accessed by 10%, 12%, 13%, and 15% of the rice farmers, respectively (see Figure 4). The farmers who use length grading, husk aspiration, and polishing machines were 5% each, while 2% and 4% of the rice farmers have access to sifting and blending machines, respectively. There was a clear trend in which small-scale farmers with fewer machines have smaller farms with lower productivity. However, most farmers in the study areas had poor access to machinery. It has been reported that farmers with access to the highest numbers of machines also produced the most rice per hectare (Bello et al., 2021; Panpluem, Mustafa, Huang, Wang, and Yin, 2019). This finding suggests that having access to multiple machines is crucial as it increases farm productivity and allows farmers to diversify their income streams. Moreover, it was observed that despite the few machines available to most farmers, they could produce a significant amount of rice and sell their products at a value higher than the market price and therefore earned higher incomes than imported rice. Thus, this positive correlation between the number of machines and average output can be achieved only when the farmers can use these machines effectively to carry out the desired tasks (Osabuohien, Okorie, and Osabohien, 2018). Therefore, these results suggest considerable room for improvement regarding farmer knowledge and proficiency with the various types of machines available in the study area.

Fig. 2. Quality of rice produced using the traditional and modern method

Environmental effects of rice processing

Modern rice processing can significantly impact the environment, particularly when inefficient machines and systems are used, resulting in a range of negative environmental consequences. These attributes were noise pollution, carbon emission due to fossil fuel use, wastewater discharge to the environment, high water usage, and high solid waste used to large amounts of processed rice. Farmers were asked how these indices affect them and the community where the processing machines are located. They were asked to rate these attributes in terms of low, moderate, and high among them and the community. Figure 4 shows that 68% of the respondents identified noise pollution as a primary concern, while 42% identified carbon emissions from the machines as a primary concern. Water usage and water pollution were of minor concern, while solid waste discharge was third in the least of their environmental concerns. The reason is that modern rice processing methods contribute minimally to water use, water pollution, and solid waste. When weighted as a single factor of environmental effects, the overall view indicated that about 31%, 30%, and 39% of rice farmers rate the environmental effects of modern rice processing to be high, moderate, and low, respectively. It has been reported that rice mills operating with a single-cylinder engine and flat belt drives produce more noise than those being run using an electric motor along with flat belt drives, and the single-cylinder engine rice millers are more prevalent in sub-Saharan Africa (Nag and Gite, 2020; Oyedepo, 2019).

Fig. 3. Machines used for modern processed rice

Furthermore, the commonly observed causes of high noise in the rice mills may be due to the use of a long flat belt drive, crank-and-pitman mechanism, absence of an electric motor enclosure, poor machine maintenance, and inadequate acoustic design of the workroom in the rice mill (Paudel and Baral, 2020). Studies have shown that excessive noise exposure can adversely affect human health, including hearing loss and other chronic health conditions (Mansor et al., 2020; Seidman and Standring, 2010). Also, ithas been reported that modern mills emit low carbon compared to traditional mills, which are operated by direct combustion of rice straws and residues (pellets) (Field, Tanger, Shackley, and Haefele, 2016; Wassmann et al., 2021). The water used by a modern rice mill is primarily forcleaning rice; therefore, the overall water usage is generally minimal. However, water used for cleaning purposes in rice mills may carry high levels of phosphorus, nitrogen, and organic matter (Sayanthan and Thusyanthy, 2018). Wastewater from rice mills also contains high levels of inorganic and organic contaminants, posing a hazard to the environment (Kumar and Deswal, 2021). Several chemical constituents present in the effluents from rice mills pose a potential threat to humans if present in high enough concentrations. In 2011, researchers in Thailand reported that rice grains might generate hazardous gases, which can replace oxygen (O2) in confined spaces (75 Parnphumeesup and Kerr, 2011; Resanond, Jittsanguan, and Sriphraram, 2011). These gases may contribute to explosions and other severe accidents and adversely affect human health and the environment.

The traditional rice processing method has some environmental effects, too, as observed by the respondents. Using the same rating method in Figure 4 above, Figure 5 showed that about 76% of the respondents indicated that the level of carbon emission from the traditional milling method is high. In comparison, 67% of the farmers identified water use and water pollution as of significant concern. However, only 58% indicated that solid discharge is of significant concern, while 52% mentioned noise pollution as a concern in traditional rice processing. When compared to the overall weighted average of the environmental effect of the modern methods, the overall environmental effect of the traditional method was 11% higher than the modern methods of rice processing among the respondents. It has been noted that waste generated during the milling process for most traditional millers ends up in streams or rivers. At the same time, some of the wastewater from the mills is releasedinto the environment without proper treatment (Rasheed et al., 2020). Using outdated milling machines also leads to higher particulate matter emissions and greenhouse gases, such as methane and carbon dioxide (Motevali, Hashemi, and Tabatabaeekoloor, 2019).

Fig. 4. Environmental effects of modern processed rice

Fig. 5. Environmental effects of traditional rice processing technique

Farmers have reported that wastewater from their rice milling activities can reach the watercourses, thereby polluting the water sources and affecting the local people's health (Akiwumi, 2019; Njoku, Nwali, and Ajana, 2017). Solid waste is a significant concern among rice farmers,as it is frequently dumped into the soil without proper treatment, resulting in soil degradation and loss of fertility (Nwachukwu, Ikeagwuani, and Adeboje, 2021; Ulakpaa and Eyankwareb, 2021).

This finding shows that farmers need to incorporate more sustainable practices into their operations by switching to eco-friendly and sustainable practices. Consequently, there is a decline in traditional mill operations, leading to the acquisition of more efficient and quieter equipment. Additionally, enhanced water management practices and reduced carbon emissions are achieved through better waste management. Farmers, therefore, should be open to learning new practices that could improve their agricultural operations and find ways to lower the cost and risk involved in implementing these new sustainable practices in their business operations, such as the utilization of improved equipment and machinery, allowing them to increase productivity while reducing their production costs and negative environmental impact (Vogel et al., 2020).

The sustainability index of the traditional and modern rice milling methods

The sustainability index in wireframe contour plots (Figure 6), following the index computed by applying equation 5, shows that small-scale farmers using the traditional rice milling method care about the environment, with 80-100 percent willing to reduce their gas emissions, solid waste, energy use, and water use. At the same time, profit increases, an aspect of business sustainability.

From Figure 6, the average sustainability score of the rice farmers is 60 percent, which shows a moderate level of concern for the UN Sustainable Development Goals (Acosta, Maharjan, Peyriere, and Mamiit, 2020; Gil et al., 2019). This finding shows that they need to improve their business and organizational sustainability and incorporate more sustainable practices into their operations. By reducing the number of traditional mill processes, investing in more efficient equipment, improving water management, reducing carbon emissions through better management of their waste, switching to eco-friendly and sustainable practices, and investing more time and planning on how to lower the cost of production. Collaborate with local environmental agencies to improve farm pollution management while maintaining good profit margins, and partner with other small-scale farmers. Moreover, a similar business model can help build a strong community that supports each other's ventures (Donner, Verniquet, Broeze, Kayser, and De Vries, 2021; Inman et al., 2018; Sarkis, Cohen, Dewick, and Schröder, 2020; Zucchella and Previtali, 2019). To improve their overall sustainability score, small-scale farmers should be open to learning new practices that could improve their agricultural operations and find ways to lower the cost and risk involved in implementing these practices in their business operations, such as the utilization of improved equipment and machinery that would allow them to increase productivity while reducing their production costs and the reliance on manual labor and emissions (Vogel et al., 2020).

The sustainability index in wireframe contour plots (Figure 7), derived by applying equation 5, shows that small-scale farmers use the modern method of rice milling. The small-scale farmers have a percentage of 80-100 focusing on environmental sustainability indicators such as energy and water use. Their organizational and business sustainability index indicates a high percentage for profit, investment, cost management, and long-term planning. The concern for carbon emission and solid waste was 20-40 percent. Their sustainability index focuses on environmental sustainability indicators, such as energy and water use. The sustainability index of the small-scale farmers using the modern method of rice milling shows a percentage above 80. According to researchers, modern rice processing is more economically and environmentally sustainable than manual rice processing (Devkota et al., 2020; Kosemani and Bamgboye, 2020).

Fig. 6. Wireframe contour plots of the sustainability index of traditional processing technique

Fig. 7. Wireframe contour plots of the sustainability index of modern processing technique

Factors affecting the sustainability index of paddy rice processing

Applying equations 1 and 2, the observed variables that might have an impact on the long-term viability of paddy rice processing are shown in Table 2. The distribution shows that the model's chi-square probability (124.93) was statistically significant at 1%. The independent variables that were included in the model had significant effects, leading to the rejection of the null hypothesis. So, we agree with the alternative theory that the independent factors that were included had a big impact on the dependent variable. The strength of the model is indicated by the Cox and Snell, McFadden, and Nagelkerkepseudo R-square values, which are 0.75, 0.853, and 0.656, respectively. This indicates that the collection of variables included in the model accounts for between 75.0, 85.3, and 65.6 percent of the variability. The model is 98 percent accurate, according to the proportionate by chance accuracy rate (Bayaga, 2010). The likelihood ratio test evaluates the overall relationship between the dependent and independent variables (Bayaga, 2010).

Table 2. Factors affecting the sustainability index of the traditional rice processing technique (Equation 2)

Power of classification of the sustainability index of the traditional milling process
	High	Moderate	Low	Percent correct
High	43	8	14	100
Moderate	23	15	10	95
Low	16	7	15	96
Overall percentage	53	20	25	98
Effect	Model Fitting Criteria	Likelihood Ratio Tests
	-2 Log Likelihood of Reduced Model	Chi-Square	p-value
Intercept	67.896	2.251	0.025
Education level	22.134	0.612	0.462
Sex	70.775	5.131	0.001
Experience	72.717	7.073	0.000
Labor size	84.561	9.3321	0.000
Investment	72.214	6.57	0.002
Income	-67.536	-1.892	0.043
Cost of production	-73.921	-8.277	0.000
Knowledge of climate change	22.032	1.235	0.881
Perception of economic sustainability	58.036	3.021	0.021
Perception of business sustainability	66.547	2.418	0.075
Cox and Snell	Nagelkerke	McFadden
0.75	0.853	0.656
Chi-square	124.93

The gender (sex) of the small-scale farmers using the traditional rice mills was statistically significant at 1% and positively related to the level of sustainability. Gender has long been recognized as an essential factor in the production process of small-scale rice farmers in developing regions, where each gender has unique roles and responsibilities. These gender roles create significant advantages and disadvantages for female small-scale farmers of rice-growing communities in their understanding and choice of sustainability practices. The heavy responsibilities placed on women contribute to the low participation rate of women in agriculture, making them less likely to adopt environmentally sustainable practices such as composting, not using chemicals, and planning the rice mill business. However, women tend to have a better understanding of household issues and are more likely to educate their children about environmental issues; they can be the driving force behind the adoption of sustainability practices and play a vital role in advancing environmental sustainability in their communities.

The traditional rice milling experience of the small-scale farmers was statistically significant at the 1% and positively related to the level of sustainability (see Table 2). The correlation between sustainable rice production and small-scale farmers' experience in the rice mills' production management is significant (Bello et al., 2021). The experience of the rice producers in milling their rice at the mill is significant in the rice farming process because this is a critical process that contributes to the quality of rice and the sustainability of the process and environment. Improving small-scale farmers' experience and skill level in milling rice will create a better environment for sustainable rice production. Improved experience will make the production process more sustainable for farmers and improve the quality of the produced rice.

The labor size of the small-scale farmers using traditional rice mills was statistically significant at the 1% and positively related to the level of sustainability, as presented in Table 2. This finding is because, as the labor size increased, the efficiency in using energy in small-scale farms also increased (Stępień et al., 2021). As a result, small-scale farmers could harvest more paddy from the land and process them, increasing rice production. In contrast, when the labor size decreased, the efficiency of the small-scale farmers in terms of energy consumption also fell, causing a drop in the sustainable production of rice. This finding can be attributed to the fact that farmers who use smaller quantities of laborers cannot achieve maximum efficiency of inputs in their operations, which affects the sustainability of their production (Dahlin and Rusinamhodzi, 2019; Houedjofonon et al., 2020). However, the labor size of small-scale rice farmers using traditional mills also affects environmental sustainability. This finding is because the energy and manpower-intensive nature of the traditional rice milling processes means that the farmers will consume a small amount of non-renewable energy sources and high raw materials such as land and water. Non-renewable energy is the leading cause of climate change, and reducing the quantity used will improve environmental sustainability (Khan, Trinh, Khan, and Ullah, 2022). However, these resources are limited and cannot be replenished by natural means. Therefore, it is vital to use these resources responsibly to preserve the environment for future generations.

The investment of the small-scale farmers using traditional rice mills was statistically significant at the 1% and positively related to the level of sustainability, as shown in Table 2. This finding implies that the investment the small-scale farmers make increases the sustainability of the farmers using traditional rice mills. Based on the findings of Ritthaisong, Johri, and Speece (2014), investment in the mills results in the economic sustainability of the rice milling business. Jirapornvaree, Suppadit, and Kumar (2021), reported on the impacts of the sustainable production of jasmine rice. They concluded that the investment in modern rice mills positively impacts the environmental and economic sustainability of rice farming enterprises in Indonesia. The income of the small-scale farmers using traditional rice mills was statistically significant at 10% and negatively related to the level of sustainability, as presented in Table 2. This result implies that the income of small-scale farmers negatively affects their sustainability practices. Some reports are similar to our findings that income affects sustainability practices adversely (Ndayambaje et al., 2019; Olanipekun, Olasehinde-Williams, and Alao, 2019; Ojo and Baiyegunhi, 2020b). A study by Qadir et al. (2014) found in their research that as the income of farmers increased, the adoption of sustainable land management practices decreased. The study also found a negative relationship between household income and sustainability. Several reasons may explain this relationship between income and sustainability. Krishnankutty, Blakeney, Raju, and Siddique (2021) found similar results to our study, where lower-income farmers were less proactive in their agricultural management processes. In our study, we hypothesized that as the level of income increases for farmers, they would adopt more sustainable agricultural practices. However, Krishnankutty et al. found that this was not the case; instead, as income levels increased, sustainable practices decreased.

The cost of production was statistically high at 1% and negatively related to the level of sustainability. This result implies that the production cost impedes small-scale farmers' ability to adopt sustainability practices. Some researchers reported that higher production costs resulted in a lower adoption of sustainable practices by small-scale farmers in developing countries (Samson et al., 2018). This finding is primarily due to the higher input costs required to implement and maintain sustainable agriculture and processing systems by rice farmers compared to conventional agriculture systems. In addition, most smallholder farmers are low-income earners who find investing in costly sustainable practices that improve socioeconomic well-being challenging. Thus, the long-term impact of adopting cost-reducing and sustainable practices on agricultural production is hard to predict due to the short-term costs associated with adopting such practices by rice farmers. Nonetheless, the long-term economic benefits of farmers' large-scale adoption of sustainable farming practices and food processing technologies would be significantly higher as it would contribute towards reducing the dependence on expensive chemicals.

The perception of sustainability was statistically significant at 10% and positively related to the level of sustainability. This result implies that the small-scale rice farmers' perception of economic sustainability promotes the ability of the small-scale farmers to adopt sustainability practices.

Equations 3 and 4 were used to obtain the result in Table 3. The distribution shows that the model's chi-square probability (89.254) was statistically significant at 1%. Therefore, the independent variables included in the model were significant effects, and the null hypothesis was rejected. So, we agree with the alternative theory that the independent variables that were included have a significant impact on the dependent variable. The model's strength is indicated by the pseudo R-square values of Cox and Snell, McFadden, and Nagelkerke, which are 0.65, 0.78, and 0.55, respectively. It indicates that the set of variables used in the model accounts for between 65.0, 78, and 55 percent of the variability. Therefore, the model is 98 percent accurate, according to the proportional by chance accuracy rate (Bayaga, 2010).

Table 3. Factors affecting the sustainability index of the modern rice processing technique (Equation 4)

Power of classification of the sustainability index of the modern milling process
	High	Moderate	Low	Percent correct
High	25	5	5	98
Moderate	12	11	2	96
Low	14	3	1	100
Overall percentage	63	25	10	98
Effect	Model Fitting Criteria	Likelihood Ratio Tests
	-2 Log Likelihood of Reduced Model	Chi-Square	p-value
Intercept	55.801	2.601	0.045
Education level	85.248	7.541	0.0001
Sex	23.214	0.124	0.632
Experience	59.214	4.214	0.00341
Labor size	32.124	2.457	0.054
Investment	99.215	10.254	0.000155
Income	25.214	2.413	0.012
Cost of production (excluding cost of energy)	-45.214	-3.241	0.012
knowledge of climate change	42.013	3.417	0.024
Knowledge of economic sustainability	58.036	3.021	0.0214
Knowledge of business sustainability	78.215	6.275	0.000125
Cox and Snell	Nagelkerke	McFadden
0.65	0.784	0.551
Chi-square	89.254

The level of education of the small-scale farmers using modern rice processing was statistically significant at 1% and positively related to the level of sustainability, as shown in Table 3. This result implies that the skills and training acquired, especially through collaboration with aid extension agents and information communication technologies by small-scale farmers using modern rice processing, positively impacted sustainability (Anugwa, Onwubuya, Chah, Abonyi, and Nduka, 2022). Rahman (2003) reported that when the education level was high, more farmers used modern rice processing than farmers with low education levels. Farmers with high education levels are more aware of the importance of adopting sustainable production practices such as modern rice processing because they are better informed about technological advances in agriculture and are more knowledgeable about the production and marketing of agriculture. On the other hand, low-education farmers tend to use traditional farming methods because they are unaware of the benefits of using modern rice processing methods (Amponsah, Addo, Dzisi, Asante, and Afona, 2018).

The modern rice milling experience of the small-scale farmers was statistically significant at 1% and positively related to the level of sustainability. Farmers utilizing modern rice processing techniques are embracing more sustainable practices. There are three main reasons for small-scale farmers in Nigeria choosing to adopt modern rice milling technologies over traditional rice processing techniques (Adisa, Ahmed, Ebenehi, and Oyibo, 2019; Obisesan, Salman, Adenegan, and Obi-Egbedi, 2018). Firstly, adopting new technologies reduces the time and effort involved in rice processing, ensuring that the rice is produced sustainably. Secondly, modern rice processing techniques produce better-quality rice (Joshi et al., 2020). Thirdly, the high initial cost of adopting new machinery can be recovered through increased production. Therefore, small-scale farmers can improve their productivity and profitability by choosing the right rice processing technology.

The labor size of the small-scale farmers using modern rice processing was statistically significant at 1% and negatively related to the level of sustainability. Therefore, the smaller the labor size, the higher the sustainability level (Ahmed, Saha, Sarkar, and Alam, 2022). The sustainability index analysis strongly suggested that the level of sustainability was higher when the labor size of the farmers using modern rice processing was less than three laborers per hectare of paddy field. Therefore, indicating that optimizing labor size is crucial for enhancing sustainable agricultural practices among small-scale rice farmers.

The investment of the small-scale farmers using modern rice mills was statistically significant at 1% and positively related to the level of sustainability. The first contributing factor towards the sustainability of the small-scale farms that used the modern rice mill was their access to modern equipment through increased investments. There was a strong correlation between the accessibility to modern equipment and the level of sustainability in the agricultural sector (Kumari and Garg, 2021). Farmers with more modern machinery were more likely to achieve higher yield levels and better production efficiency when compared to those who did not have access to such machinery. As a result, these farmers could produce large quantities of rice for market sale with minimal cost and effort.

Modern rice processing has been recently gaining popularity as an environmentally friendly process. The processors use several techniques to reduce the water and energy needed to process rice. These techniques are washing, drying, threshing, and milling. The income of the small-scale farmers using modern rice mills was statistically significant at 10% and positively related to the level of sustainability. This finding suggests that farmers who invest in higher-efficiency equipment tend to produce greater profits than those who stick with traditional equipment. This effect is likely due to economies of scale in processing, i.e., as production increases, it becomes easier to maximize efficiency and increase profit. When assessing the sustainability of farming operations, it is crucial to consider both costs and benefits because increased production may carry hidden costs such as pollution or depletion of natural resources. In the case of modern rice processing, most of the machinery used is low-cost items that can be built by the owner or purchased with a small loan.

The cost of production, or the amount that a producer must expend to produce a unit quantity of rice, is statistically high at 1% and negatively related to the level of sustainability of the rice farmer using a modern method of rice processing. This finding means that as the cost of production increases, a farmer's sustainability level decreases. However, this relationship could be stronger. Therefore, other factors, such as the amount of rice produced per harvest and the cost to the farmer of his machinery, may be more important than the cost of production in determining the sustainability of the rice farmer. The other major factor that affects the sustainability and profitability of the rice farmer is the quality of the rice he produces.

The farmers' perception of economic sustainability was statistically significant at 10% and positively related to the sustainability of modern rice processing (see Table 3). This result implies that the perception of the farmers on the economic sustainability of modern rice processing is correlated with the level of its sustainability (Escobar et al., 2022). On the other hand, the study showed that the technical complexity of modern rice processing is not significantly related to the perception of the farmers on its economic sustainability. In other words, the level of technological complexity does not affect the reliability of the output generated by the system in its effort to earn a living for the farmers and sustain their business in the long run.

This finding explored the farmers' perception of organizational sustainability using modern rice processing. The farmers' perception of organizational sustainability was statistically significant at 10% and positively related to the sustainability of farmers using modern rice processing. Based on the results of this study, there is a positive relationship between organizational sustainability and the level of technical innovation in an organization. Hence, the need to develop more sustainable organizations in the agricultural sector. This study shows a relatively close but not strict link between farmers' perceptions of their farms' sustainability and their rice processors' technical innovation. However, stronger relationships may exist between farmers' perceptions of the sustainability of their farms and the levels of the organization's environmental performance, as well as how they implement new technologies to improve sustainability.

Conclusion

1. Small-scale rice farmers prefer traditional processing. The traditional approach was chosen due to the expensive cost of modern processing machines, which have a higher output than the traditional method.

2. While the nutritional content of rice processed using traditional techniques was deemed superior, farmers recognized the appearance of rice produced by modern methods as the most outstanding. The farm employsthe modern technique using different machines for rice processing, which impacts the environment; carbon emission levels and noise pollution were the significant environmental impacts of the modern rice processing technique. 

3. The traditional technique of rice processing released high carbon into the environment, resulting in high water usage and, consequently, water pollution. The farmers who use the traditional technique care for the environment by reducing carbon emission energy and water use, while the farmers that use the modern technique care for the environment by reducing their water and energy use significantly. 

4. Factors such as gender experience, labor size, investment, income, cost of production, knowledge of climate change, and business economic sustainability were the significant factors that influenced the farmers using the modern and traditional rice processing techniques to have a sustainability index.

5. The education level of the farmers and their knowledge of sustainability practices prompt them to prefer modern technology to traditional techniques.

Recommendation

There is a need to make recommendations based on the findings.

The Government needs to develop a support program to reduce the cost of acquiring the machines required to use a modern rice processing technique. It will reduce the production cost of the farmers. Alternative energy sources need to be explored to reduce the impact of high carbon emissions on the environment.

Farmers should be educated about the value of using renewable energy sources to improve production and the environmental impact. The use of more modern machinery for paddy rice production should be encouraged to improve the output and quality of rice produced and reduce production costs.

Farmers should be encouraged to adopt modern farming techniques to reduce the impact of chemicals on the environment and adopt improved techniques to produce high-quality, healthy produce that satisfies consumer demands. Measures should be taken to reduce the waste from food processing. Incentives should be provided to encourage the use of renewable energy and reduce the use of fossil fuels in production. The use of modern technology needs to be encouraged in order to increase production and decrease costs.

Conflict of Interest: The authors declare no conflict of interest.

Authors Contribution: The authors made equal contributions.

References

1. Acosta, L. A., Maharjan, P., Peyriere, H. M., and Mamiit, R. J. (2020). Natural capital protection indicators: Measuring performance in achieving the Sustainable Development Goals for green growth transition. Environmental and Sustainability Indicators, 8, 100069.DOI
2. Adekoyeni, O. O., Fagbemi, S. A., and Ismaila, A. R. (2018). Ofada rice identity, physical qualities and processing technology options for upgrading: A review. Annual Research and Review in Biology, 1-9..DOI
3. Adisa, R. S., Ahmed, T. A., Ebenehi, O., and Oyibo, F. O. (2019). Perceived Benefits of Adoption of Improved Rice Production Technologies Among Small–Scale Farmers in Kogi state, Nigeria. Journal of Agricultural Extension, 23(1), 138-148..DOI
4. Ahmed, S., Saha, C. K., Sarkar, S., and Alam, M. M. (2022). Effect of paddy drying methods on the performance of rice mills in Bangladesh. Drying Technology, 1-15.DOI
5. Akinbile, C. O., Ogunmola, O. O., Abolude, A. T., and Akande, S. O. (2020). Trends and spatial analysis of temperature and rainfall patterns on rice yields in Nigeria. Atmospheric Science Letters, 21(3), e944.DOI
6. Akite, I., Okello, D. M., Kasharu, A., and Mugonola, B. (2022). Estimation of profit efficiency of smallholder rice farmers in Uganda: A stochastic frontier approach. Journal of Agriculture and Food Research, 8, 100315.DOI
7. Akiwumi, O. O. (2019). Pollution status of water and sediments of Asa River exposed to industrial effluents in Ilorin, Kwara State, Nigeria (Doctoral dissertation)..
8. Aluko, Y. A. (2018). Women&#039;s use of indigenous knowledge for environmental security and sustainable development in Southwest Nigeria.  International Indigenous Policy Journal, 9(3).. https://www.jstor.org/stable/48767541
9. Amponsah, S. K., Addo, A., Dzisi, K., Asante, B., and Afona, D. (2018). Assessment of rice farmers’ knowledge and perception of harvest and postharvest losses in Ghana. Cogent Food and Agriculture, 4(1), 1471782.DOI
10. Anabaraonye, B., Okafor, J. C., and Hope, J. (2018). Educating farmers in rural areas on climate change adaptation for sustainability in Nigeria.  International Journal of scientific and Engineering Research, 10(4), 1391-1398..DOI
11. Anthony, L., Alabi, O. O., Ebukiba, E. S., and Gamba, V. (2021). Factors influencing output of rice produced and choice of marketing outlets among smallholder farming households, Abuja, Nigeria. Sarhad Journal of Agriculture, 37(1), 262-277.DOI
12. Anugwa, I. Q., Onwubuya, E. A., Chah, J. M., Abonyi, C. C., and Nduka, E. K. (2022). Farmers’ preferences and willingness to pay for climate-smart agricultural technologies on rice production in Nigeria. Climate Policy, 22(1), 112-131..DOI
13. Baccar, M., Bouaziz, A., Dugué, P., Gafsi, M., and Le Gal, P. Y. (2020). Sustainability Viewed from Farmers’ Perspectives in a Resource-Constrained Environment. Sustainability, 12(20), 8671..DOI
14. Batista, A. A. D. S., and Francisco, A. C. D. (2018). Organizational sustainability practices: A study of the firms listed by the corporate sustainability index. Sustainability, 10(1), 226..DOI
15. Baur, P. (2022). When farmers are pulled in too many directions: comparing institutional drivers of food safety and environmental sustainability in California agriculture. In Social Innovation and Sustainability Transition (pp. 241-260). Springer, Cham..DOI
16. Bayaga, A. (2010). Multinomial Logistic Regression: Usage and Application in Risk Analysis. Journal of Applied Quantitative Methods, 5(2)..
17. Bello, L. O., Baiyegunhi, L. J., and Danso-Abbeam, G. (2021). Productivity impact of improved rice varieties’ adoption: case of smallholder rice farmers in Nigeria. Economics of Innovation and New Technology, 30(7), 750-766..DOI
18. Chungsangunsit, T., Gheewala, S. H., and Patumsawad, S. (2005). Environmental assessment of electricity production from rice husk: a case study in Thailand. International Energy Journal, 6..DOI
19. Dahlin, A. S., and Rusinamhodzi, L. (2019). Yield and labour relations of sustainable intensification options for smallholder farmers in sub‐Saharan Africa. A meta‐analysis. Agronomy for Sustainable Development, 39(3), 1-18.DOI
20. Danbaba, N., Idakwo, P. Y., Kassum, A. L., Bristone, C., Bakare, S. O., Aliyu, U., ... and Danbaba, M. K. (2019). Rice postharvest technology in Nigeria: An overview of current status, constraints and potentials for sustainable development. Open Access Library Journal, 6(8), 1-23..DOI
21. Daniel, S. E., Tsoulfas, G. T., Pappis, C. P., and Rachaniotis, N. P. (2004). Aggregating and evaluating the results of different Environmental Impact Assessment methods. Ecological Indicators, 4(2), 125-138..DOI
22. De La Maza, C., Davis, A., and Azevedo, I. (2021). Welfare analysis of the ecological impacts of electricity production in Chile using the sparse multinomial logit model. Ecological Economics, 184, 107010..DOI
23. Dehnavi, A., Aghdam, I. N., Pradhan, B., and Varzandeh, M. H. M. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148..DOI
24. Devkota, K. P., Khanda, C. M., Beebout, S. J., Mohapatra, B. K., Singleton, G. R., and Puskur, R. (2020). Assessing alternative crop establishment methods with a sustainability lens in rice production systems of Eastern India. Journal of Cleaner Production, 244, 118835..DOI
25. Dhankhar, P., andHissar, T. (2014). Rice milling. IOSR Journal of Engineering, 4(5), 34-42.. http://iosrjen.org/Papers/vol4_issue5%20(part-4)/F04543442.pdf
26. Donner, M., Verniquet, A., Broeze, J., Kayser, K., and De Vries, H. (2021). Critical success and risk factors for circular business models valorising agricultural waste and by-products. Resources, Conservation and Recycling, 165, 105236..DOI
27. Dzhengiz, T. (2020). A literature review of inter-organizational sustainability learning. Sustainability, 12(12), 4876..DOI
28. Escobar, N., Bautista, I., Peña, N., Fenollosa, M. L., Osca, J. M., and Sanjuán, N. (2022). Life Cycle Thinking for the environmental and financial assessment of rice management systems in the Senegal River Valley. Journal of Environmental Management, 310, 114722..DOI
29. Esiobu, N. S., Onubuogu, C. G., Njoku, S. M., and Nwachukwu, B. C. (2020). Sustainability and Determinate of Farmers’ Mitigation Strategies to Greenhouse Gases Emission: A Case in Rice Agric-Food System of Nigeria. In Plant Stress Physiology. IntechOpen.DOI
30. Fatunbi, O. A., and Kombat, R. (2020). Access to mechanization for smallholder farmers in Africa. In The sustainable intensification of smallholder farming systems (pp. 187-213). Burleigh Dodds Science Publishing..DOI
31. Field, J. L., Tanger, P., Shackley, S. J., and Haefele, S. M. (2016). Agricultural residue gasification for low-cost, low-carbon decentralized power: An empirical case study in Cambodia. Applied Energy, 177, 612-624..DOI
32. Ghenai, C., Albawab, M., and Bettayeb, M. (2020). Sustainability indicators for renewable energy systems using multi-criteria decision-making model and extended SWARA/ARAS hybrid method. Renewable Energy, 146, 580-597..DOI
33. Gil, J. D. B., Reidsma, P., Giller, K., Todman, L., Whitmore, A., and van Ittersum, M. (2019). Sustainable development goal 2: Improved targets and indicators for agriculture and food security. Ambio, 48(7), 685-698..DOI
34. Gowda, M. C., and Jayaramaiah, K. M. (1998). Comparative evaluation of rice production systems for their sustainability. Agriculture, Ecosystems and Environment, 69(1), 1-9..DOI
35. Hasan, K., Tanaka, T. S., Alam, M., Ali, R., and Saha, C. K. (2020). Impact of modern rice harvesting practices over traditional ones. Reviews in Agricultural Science, 8, 89-108..DOI
36. Houedjofonon, E. M., Adjovi, N. R. A., Chogou, S. K., Honfoga, B., Mensah, G. A., and Adegbidi, A. (2020). Scale economies and total factor productivity growth on poultry egg farms in Benin: a stochastic frontier approach. Poultry Science, 99(8), 3853-3864.DOI
37. Ikebudu, M. (2021). Government Intervention on Agricultural Goods Imports in Developing Countries: A Survival Necessity or An Unnecessary Protectionism? The Case of Nigeria’s Policy Intervention on Rice. International Journal of Innovative Science and Research Technology, 6(3), 521-530.. https://ijisrt.com/assets/upload/files/IJISRT21MAR277.pdf
38. Inman, A., Winter, M., Wheeler, R., Vrain, E., Lovett, A., Collins, A., ...and Cleasby, W. (2018). An exploration of individual, social and material factors influencing water pollution mitigation behaviours within the farming community. Land Use Policy, 70, 16-26..DOI
39. Jayne, T. S., Snapp, S., Place, F., and Sitko, N. (2019). Sustainable agricultural intensification in an era of rural transformation in Africa. Global Food Security,  20, 105-113..DOI
40. Jellason, N. P., Conway, J. S., and Baines, R. N. (2021). Understanding impacts and barriers to adoption of climate-smart agriculture (CSA) practices in North-Western Nigerian drylands. The Journal of Agricultural Education and Extension, 27(1), 55-72..DOI
41. Jirapornvaree, I., Suppadit, T., and Kumar, V. (2021). Assessing the economic and environmental impact of jasmine rice production: Life cycle assessment and Life Cycle Costs analysis. Journal of Cleaner Production, 303, 127079..DOI
42. Joshi, K. D., Upadhyay, S., Chaudhary, P., Shrestha, S., Bhattarai, K., and Tripathi, B. P. (2020). The rice processing industry in Nepal: Constraints and opportunities. Agricultural Sciences, 11(11), 1060-1080..DOI
43. Karkare, P., Odijie, M., Ukaoha, K., and van Seters, J. (2022). Inconsistent policies or political realities? Nigeria’s Trade and Industrial Policy Imperatives (Discussion Paper No. 318). European Centre For Development Policy Management.. https://ecdpm.org/application/files/8916/5780/1367/Inconsistent-policies-or-political-realities-ECDPM-Discussion-Paper-318-2022.pdf
44. Khan, M. K., Trinh, H. H., Khan, I. U., and Ullah, S. (2022). Sustainable economic activities, climate change, and carbon risk: an international evidence.  Environment, Development and Sustainability, 24(7), 9642-9664..DOI
45. Kosemani, B. S., and Bamgboye, A. I. (2020). Energy input-output analysis of rice production in Nigeria. Energy, 207, 118258..DOI
46. Krishnankutty, J., Blakeney, M., Raju, R. K., and Siddique, K. H. (2021). Sustainability of traditional rice cultivation in Kerala, India—a socio-economic analysis. Sustainability, 13(2), 980..DOI
47. Kulyakwave, P. D., Xu, S., Yu, W., and Mwakyusa, J. G. (2022). Analysis of inputs variability on rice growth stages in Mbeya region. African Journal of Agricultural Research,  18(9), 742-751..DOI
48. Kumar, A., Priyadarshinee, R., Roy, A., Dasgupta, D., and Mandal, T. (2016). Current techniques in rice mill effluent treatment: emerging opportunities for waste reuse and waste-to-energy conversion. Chemosphere, 164, 404-412..DOI
49. Kumar, S., and Deswal, S. (2021). A review on current techniques used in India for rice mill wastewater treatment and emerging techniques with valuable by-products. Environmental Science and Pollution Research, 28(7), 7652-7668..DOI
50. Kumari, A., and Garg, V. (2021). Impact of credit on sustainable agricultural development in India. Journal of Sustainable Finance and Investment, 1-12..DOI
51. Kyaw, N. N., Ahn, S., and Lee, S. H. (2018). Analysis of the factors influencing market participation among smallholder rice farmers in magway region, central dry zone of Myanmar. Sustainability, 10(12), 4441..DOI
52. Lenis Rodas, Y. A., Morales Rojas, A. D., Jaramillo Marín, S., Salcedo Jiménez, C., and Pérez Bayer, J. F. (2022). Rice husk fixed bed gasification for circular economy in compact rice mills. Energy Sources, Part A:Recovery, Utilization, and Environmental Effects, 44(1), 1875-1887.. https://ouci.dntb.gov.ua/en/works/lDQoWjY9/
53. Lim, J. S., Manan, Z. A., Hashim, H., and Alwi, S. R. W. (2013). Towards an integrated, resource-efficient rice mill complex. Resources, Conservation and Recycling, 75, 41-51..DOI
54. Mancini, F., Termorshuizen, A. J., Jiggins, J. L., and van Bruggen, A. H. (2008). Increasing the environmental and social sustainability of cotton farming through farmer education in Andhra Pradesh, India. Agricultural Systems, 96(1-3), 16-25..DOI
55. Mansor, A. A., Abdullah, S., Nawawi, M. A., Ahmed, A. N., Napi, N. M., and Ismail, M. (2020, May). Temporal and Spatial Analysis of the Occupational Noise at Rice Mill in Kedah. In IOP Conference Series: Earth and Environmental Science (Vol. 498, No. 1, p. 012094). IOP Publishing..DOI
56. Motevali, A., Hashemi, S. J., and Tabatabaeekoloor, R. (2019). Environmental footprint study of white rice production chain-case study: Northern of Iran. Journal of Environmental Management, 241, 305-318..DOI
57. Mujeyi, A., Mudhara, M., and Mutenje, M. J. (2020). Adoption determinants of multiple climate smart agricultural technologies in Zimbabwe: Considerations for scaling-up and out. African Journal of Science, Technology, Innovation and Development, 12(6), 735-746..DOI
58. Nag, P. K., and Gite, L. P. (2020). Farm Mechanization: Nature of Development. In Human-Centered Agriculture (pp. 149-171). Springer, Singapore..DOI
59. Ndayambaje, B., Amuguni, H., Coffin-Schmitt, J., Sibo, N., Ntawubizi, M., and VanWormer, E. (2019). Pesticide application practices and knowledge among small-scale local rice growers and communities in Rwanda: a cross-sectional study. International Journal of Environmental Research and Public Health, 16(23), 4770..DOI
60. Ndirangu, S. N., and Oyange, W. A. (2019). Analaysis of Millers in Kenya’s Rice Value Chain. IOSR Journal of Agriculture and Veterinary Science(IOSR-JAVS), 12, 38-47..
61. Njoku, C., Nwali, C. C., and Ajana, A. J. (2017). Water qualities as affected by rice mill processing wastes in Abakalikisouthestern Nigeria. IOSR Journal of Environmental Science, Toxicology and Food Technology, 11, 7..
62. Nwachukwu, A., Ikeagwuani, F. C., and Adeboje, A. O. (2021). Investigation on the Background Radiation of Abakaliki Rice Mill in Ebonyi State, Nigeria. Atom Indonesia, 47(1), 25-30..DOI
63. Obianefo, C. A., Ng’ombe, J. N., Mzyece, A., Masasi, B., Obiekwe, N. J., and Anumudu, O. O. (2021). Technical Efficiency and Technological Gaps of Rice Production in Anambra State, Nigeria. Agriculture, 11(12), 1240..DOI
64. Obisesan, O. O., Salman, K. K., Adenegan, K. O., and Obi-Egbedi, G. O. (2018). Choice of processing techniques among rice processors in Nigeria. World Journal of Entrepreneurship, Management and Sustainable Development..DOI
65. Ojo, T. O., and Baiyegunhi, L. J. S. (2020a). Determinants of climate change adaptation strategies and its impact on the net farm income of rice farmers in south-west Nigeria. Land Use Policy, 95, 103946..DOI
66. Ojo, T. O., and Baiyegunhi, L. J. S. (2020b). Impact of climate change adaptation strategies on rice productivity in South-west, Nigeria: An endogeneity corrected stochastic frontier model. Science of The Total Environment, 745, 141151..DOI
67. Ojo, T. O., Baiyegunhi, L. J., Adetoro, A. A., and Ogundeji, A. A. (2021). Adoption of soil and water conservation technology and its effect on the productivity of smallholder rice farmers in Southwest Nigeria. Heliyon, 7(3), e06433..DOI
68. Olanipekun, I. O., Olasehinde-Williams, G. O., and Alao, R. O. (2019). Agriculture and environmental degradation in Africa: The role of income.  Science of the Total Environment, 692, 60-67..DOI
69. Onyekwena, C. (2016). Case Study of OLAM Outgrower Scheme in Rukubi Rice Farming Communities, Nasarawa State Nigeria (No. 1093-2016-88007)..DOI
70. Onyeneke, R. U. (2021). Does climate change adaptation lead to increased productivity of rice production? Lessons from Ebonyi State, Nigeria.  Renewable Agriculture and Food Systems, 36(1), 54-68..DOI
71. Onyeneke, R. U., Amadi, M. U., Njoku, C. L., and Osuji, E. E. (2021). Climate Change Perception and Uptake of Climate-Smart Agriculture in Rice Production in Ebonyi State, Nigeria. Atmosphere, 12(11), 1503..DOI
72. Osabuohien, E. S., Okorie, U. E., and Osabohien, R. A. (2018). Rice production and processing in Ogun state, Nigeria: qualitative insights from farmers&#039; association. In Food Systems Sustainability and Environmental Policies in Modern Economies (pp. 188-215). IGI Global..DOI
73. Oyedepo, S. O. (2019). Energy use and energy saving potentials in food processing and packaging: case study of Nigerian industries. In Bottled and Packaged Water (pp. 423-452). Woodhead Publishing..DOI
74. Panpluem, N., Mustafa, A., Huang, X., Wang, S., and Yin, C. (2019). Measuring the technical efficiency of certified organic rice producing farms in Yasothon province: Northeast Thailand. Sustainability, 11(24), 6974..DOI
75. Parnphumeesup, P., and Kerr, S. A. (2011). Stakeholder preferences towards the sustainable development of CDM projects: Lessons from biomass (rice husk) CDM project in Thailand. Energy Policy, 39(6), 3591-3601..DOI
76. Paudel, D. R., and Baral, H. N. (2020). Study of noise level status at different rice mills in Surkhet Valley, Nepal. Himalayan Physics, 86-92..DOI
77. Qadir, M., Quillérou, E., Nangia, V., Murtaza, G., Singh, M., Thomas, R. J., ...and Noble, A. D. (2014, November). Economics of salt‐induced land degradation and restoration. In Natural Resources Forum (Vol. 38, No. 4, pp. 282-295)..DOI
78. Rahman, S. (2003). Profit efficiency among Bangladeshi rice farmers. Food Policy, 28(5-6), 487-503..DOI
79. Rajabov, N., and Mustafakulov, S. I. (2020). Econometric analysis of the impact of the investment climate on the sustainability of socio-economic development of navoi region. TRANS Asian Journal of Marketing and Management Research, 9(10), 82-90..
80. Rasheed, R., Umer, R., Hamid, A., Rizwan, A., Javed, H., Ahmad, S. R., and Su, Y. (2020). Waste valorization and resource conservation in rice processing industries—An analytical study from Pakistan. Environmental Science and Pollution Research, 27(34), 43372-43388..DOI
81. Materne, M., and Reddy, A. A. (2007). Commercial cultivation and Profitability, (ed) Lentil, Yadav, Shyam S. McNeil, David L. and Stevenson, Philip C. Lentil..DOI
82. Resanond, A., Jittsanguan, T., and Sriphraram, D. (2011). Company&#039;s Competitiveness Enhancement for Thai Agribusiness through the Clean Development Mechanism (CDM) under the Kyoto Protocol. Journal of Sustainable Development, 4(2), 80..DOI
83. Ritthaisong, Y., Johri, L. M., and Speece, M. (2014). Sources of sustainable competitive advantage: The case of rice-milling firms in Thailand. British Food Journal.DOI
84. Roy, R., Chan, N. W., and Xenarios, S. (2016). Sustainability of rice production systems: an empirical evaluation to improve policy. Environment, Development and Sustainability, 18(1), 257-278..DOI
85. Salisu, J., Gao, N., and Quan, C. (2021). Techno-economic assessment of co-gasification of rice husk and plastic waste as an off-grid power source for small scale rice milling-an Aspen Plus model. Journal of Analytical and Applied Pyrolysis, 158, 105157..DOI
86. Samson, B. K., Voradeth, S., Zhang, S., Tao, D., Xayavong, S., Khammone, T., ... and Wade, L. J. (2018). Performance and survival of perennial rice derivatives (Oryza sativa L.) in Lao PDR. Experimental Agriculture, 54(4), 592-603..DOI
87. Sarkis, J., Cohen, M. J., Dewick, P., and Schröder, P. (2020). A brave new world: Lessons from the COVID-19 pandemic for transitioning to sustainable supply and production. Resources, Conservation, and Recycling, 159, 104894..DOI
88. Sayanthan, S., and Thusyanthy, Y. (2018). Rice Parboiling and Effluent Treatment Models; a Review. International Journal of Research Studies in Agricultural Sciences (IJRSAS), 4(5), 17-23..
89. Seidman, M. D., and Standring, R. T. (2010). Noise and quality of life. International Journal of Environmental Research and Public Health, 7(10), 3730-3738..DOI
90. Sethy, P. K., Barpanda, N. K., Rath, A. K., and Behera, S. K. (2020). Image processing techniques for diagnosing rice plant disease: a survey. Procedia Computer Science, 167, 516-530..DOI
91. Sissoko, M. D. D., Ohene-Yankyera, K., and Wongnaa, C. A. (2021). Profitability and technical efficiency of rice farms using traditional and improved milling machines: Evidence from Mali. African Journal of Science, Technology, Innovation and Development, 1-13..DOI
92. Soullier, G., Demont, M., Arouna, A., Lançon, F., and Del Villar, P. M. (2020). The state of rice value chain upgrading in West Africa. Global Food Security, 25, 100365..DOI
93. Stępień, S., Czyżewski, B., Sapa, A., Borychowski, M., Poczta, W., and Poczta-Wajda, A. (2021). Eco-efficiency of small-scale farming in Poland and its institutional drivers. Journal of Cleaner Production, 279, 123721..DOI
94. Tahir, A. D., Onewo, D. E., and Alkali, H. M. (2022). Analysis of Factors Influencing Investment Patterns among Small Scale Rice Farmers in Kano State, Nigeria. Nigeria Agricultural Journal, 53(1), 1-5.. https://www.ajol.info/index.php/naj/article/view/227565
95. Tong, C., Gao, H., Luo, S., Liu, L., and Bao, J. (2019). Impact of postharvest operations on rice grain quality: A review. Comprehensive Reviews in Food Science and Food Safety, 18(3), 626-640..DOI
96. Ulakpaa, R. O. E., and Eyankwareb, M. O. (2021). Contamination assessment of water resources around waste dumpsites in Abakaliki, Nigeria; A Mini Review. Journal Clean WAS (JCleanWAS), 5(1), 17-20..DOI
97. United States Department Foreign Agricultural Service (October 03, 2023). Grain and Feed Update. USDA Foreign Agricultural Service..
98. Vogel, C., Mathé, S., Geitzenauer, M., Ndah, H. T., Sieber, S., Bonatti, M., and Lana, M. (2020). Stakeholders’ perceptions on sustainability transition pathways of the cocoa value chain towards improved livelihood of small-scale farming households in Cameroon. International Journal of Agricultural Sustainability, 18(1), 55-69..DOI
99. Wassmann, R., Van-Hung, N., Yen, B. T., Gummert, M., Nelson, K. M., Gheewala, S. H., and Sander, B. O. (2021). Carbon footprint calculator customized for rice products: Concept and characterization of rice value chains in southeast Asia. Sustainability, 14(1), 315..DOI
100. Yusuf, O. U. (2018). Profitability and energy gaps of semi-mechanised and traditional rice production technologies in north central and north western Nigeria. Agricultural Engineering International: CIGR Journal, 20(2), 116-125.. https://cigrjournal.org/index.php/Ejounral/article/view/4299
101. Zavadskas, E. K., Stević, Ž., Tanackov, I., and Prentkovskis, O. (2018). A novel multicriteria approach–rough step-wise weight assessment ratio analysis method (R-SWARA) and its application in logistics. Studies in Informatics and Control, 27(1), 97-106..DOI
102. Zhao, M., Lin, Y., and Chen, H. (2020). Improving nutritional quality of rice for human health. Theoretical and Applied Genetics, 133(5), 1397-1413.. https://link.springer.com/article/10.1007/s00122-019-03530-x
103. Zucchella, A., and Previtali, P. (2019). Circular business models for sustainable development: A “waste is food” restorative ecosystem. Business Strategy and the Environment, 28(2), 274-285..DOI

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