There are many warehouses across the world, including company stores for raw materials and components, finished goods stores, and so on. Warehousing connects the manufacturer and the consumer, and it's critical for providing the needed level of customer service while also keeping costs down. Warehousing has evolved into one of the most essential aspects of a company's logistics system. Maize producers all around the world are suffering because of modern maize storage. Because of the numerous advantages, farmers are in a better position to accept the use of modern warehouses in maize storage. Although the municipality of Ejura-Sekyedumase has some modern storage facilities, they are hardly used. The elements that impact the use of storage facilities in Ejura – Sekyedumase Municipal were investigated in this study. The study also looked at the extent of usage of these modern warehousing facilities, farmers' perceptions of the government's "One District One Warehouse" (1D1W) policy, and what farmers anticipate from newly built warehouses under the 1D1W policy to help them make decisions about whether to utilize them. The study used a survey research design with questionnaires to collect data. Maize growers in Ejura-Sekyedumase served as the study's unit. Quantitative and qualitative approaches were used to analyze the data. From the analysis, it was found that the production of maize in the Ejura-Sekyedumase Municipality is undertaken by the less educated farmers and the youths (people with ages less than 40). Also, most of the farmers in the municipality do not have access to financial support. With regards to the storage of maize in a warehouse, majority of maize farmers in the Municipal do not store their products in a warehouse because they have rooms and sheds for the storage of their produce. FBO participation, farmer category, the quantity of produced harvested, warehouse awareness and access to financial support were the important factors that influence farmers’ decision to use a warehouse. Also, the major reason why farmers are not using warehousing facilities in the municipal is a result of poor management services by the warehouse owners and managers. Based on the findings, the study recommends that, the local government authority should raise funds to pay off the private partner, own the facility, and decide on the management and pricing policies. These will help in the sustainability of the warehouses. [...]

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DECLARATION

DEDICATION

ACKNOWLEDGEMENT

ABSTRACT

LIST OF TABLES

TABLE OF FIGURES

CHAPTER ONE
1.0 INTRODUCTION
1.1 Chapter overview
1.2 Background
1.3 Problem statement
1.4 Research questions
1.5 Research objectives
1.6 Justification

CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
2.2 History and approaches of warehousing
2.3 Nature and significance of warehousing
2.4 Purposes behind warehousing of maize
2.5 Warehousing Philosophy
2.6 Conceptual framework of adoption of agricultural new technologies
2.7 Factors affecting Adoption of Agricultural New Technologies in Ghana
2.7.1. Demographic factors
2.7.2. Socio-Economic factors
2.7.3. Institutional factors
2.8. Farmers' perceptions of new agricultural technology

CHAPTER THREE
METHODOLOGY
3.1 Introduction
3.2 Research Design
3.3 Study Population
3.4 Sample and Sampling Technique
3.5 Data Collection Instrument and Procedures
3.6 Data Analysis
3.7 Explanatory Variables in the Logit Regression Model

CHAPTER FOUR
RESULTS AND DISCUSSION
4.1 Introduction
4.2 Demographic characteristics of respondents
4.3 The extent of warehouse usage among maize farmers
4.4 To determine the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipality
4.5 Farmer’s perception of the 1D1W policy
4.6 What do farmers expect from the newly constructed warehouse?

CHAPTER FIVE
SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS
5.1 Introduction
5.2 Summary
5.3 Conclusions
5.4 Recommendations
5.5. Limitations of the study
5.6 Suggestions for further study

REFERENCES

APPENDIX

DECLARATION

Abbildung in dieser Leseprobe nicht enthalten

DEDICATION

We dedicate this work to our beloved parents who have been our source of inspiration and gave us strength when we thought of giving up, who continually provided their moral, spiritual, emotional, and financial support. We again dedicate this work to our friends. Their love, encouragement, sacrifices and prayers saw us through.

ACKNOWLEDGEMENT

All glory to the highest God for granting us life, wisdom and protection through our years of study. We are indeed grateful to our supervisor, Dr. James Osei Mensah, for his guidance and supervision. Also, our sincere gratitude goes to Ellis Suntaa Mwintuoh and Mr. Fuseini Bawaror Bugilla for their support in data collection. And to all respondents who availed themselves for questioning during the field survey, we are indeed grateful. It was due to their cooperation that this study was made possible. Finally, to our family and friends, we are indeed grateful for your immense support and advice. Thank you, all.

ABSTRACT

LIST OF TABLES

Table 3.1. Summary of the variables used to analyze the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipality.

Table 4.1: Gender of respondents

Table 4.2. Descriptive characteristics of the respondents

Table 4.3. Usage of Warehouse Facilities

Table 4.4. Number of years of utilizing a warehouse by respondents who uses a warehouse.

Table 4.5. Awareness of modern warehouse availability by respondents

Table 4.6. Respondents who are aware of a modern warehouse availability in the municipal and have used the facility before.

Table 4.7. Readiness to patronize a modern warehouse if made available

Table 4.8. Availability of warehouse in the municipality

Table 4.9. Experience of high storage losses in the municipality

Table 4.10. Extent of warehouse usage in the municipality (months)

Table 4.11. Factors influencing warehouse usage by maize farmers in Ejura-Sekyedumase

Table 4.12 Perception index table of respondents

TABLE OF FIGURES

Figure 3.1: Guide of the Ejura-Sekyedumase Region

Figure 4.1 Years of warehouse usage by respondents

Figure 4.2 Warehouse awareness by respondents

Figure 4.3 Respondents who are aware of a warehouse and have used it.

Figure 4.4 Respondents readiness to patronize a modern warehouse facility

Figure 4.5 Warehouse availability in the municipal

Figure 4.6 Post-harvest losses experienced by respondents

CHAPTER ONE

1.0 INTRODUCTION

1.1 Chapter overview

This section serves as the introduction to the research on the evaluation of warehouse usage in the maize industry. It comprises the study's background, the issue description, the research questions, the study's objectives, and the study's rationale.

1.2 Background

Most countries in Sub-Saharan Africa (SSA) rely heavily on agriculture to sustain their economies. According to the World Bank (2011), SSA has an estimated population of 862 million people, with 60% of the population living in urban areas. Because farming is mostly done in rural areas, Ofori (2008) asserted that a decreasing proportion of the population is involved in food production and that increasing quantities of food should be transported across long distances to meet the growing demand in urban areas. To react to the growing food interest, the administrations in these areas focused on increasing food production. The issue is not only the amount of food to be provided but also the number of food losses after harvest. After harvest, a staggering amount of food is lost (World Bank, 2011). The magnitude of post-harvest disasters reported in many agricultural countries is a serious issue that must be addressed. As the population of these areas continues to grow, it is becoming increasingly difficult to predict whether farmers will want to produce to meet the population's food needs in the next years. This isn't surprising considering that the global population has grown fourfold in the last century, with current estimates putting it at 9.2 billion by 2050 and 11.5 billion by 2100, with more than 87 percent of the population residing in non-industrial countries in Africa, Asia, and Latin America (Vries, 2001). As a result, countries with low wages and food shortages become more concerned about global and public food security in the long run (World Bank, 2011). Postharvest tragedies are any sort of misfortune that occurs between the time the food is collected and the time it reaches the final shopper, and they can be subjective, such as a mistake in supplement structure, sufficiency, or edibility, or quantitative, such as food wastage. These gathered yield mishaps may occur as a result of animals and diseases in the field, helpless harvest treatment, microbiological defilements, moisture, and supplement calamity, and mechanical damage. Postharvest loss (PHL) is, as a result, one of the major impediments to achieving food security in the agricultural sector throughout the world, and Ghana is no exception.

Maize is perhaps the most common rural crop in the SSA, and it is grown for a variety of purposes, including human consumption and animal feed. It's also used to help in the preparation of ready-to-eat meals. Maize is the major grain crop and staple food for over 1.2 billion people in SSA and Latin America, according to research (IITA, 2009). Maize is the most popular grain in Ghana, and it is also the country's second-largest producer (MoFA, 2012). According to research published in 2005, wheat, rice, and maize provide a significant percentage of the world's calorie requirements, with roughly 30 harvests contributing to the total (Mama, 2005). Despite the growing production of maize, many disasters occur. According to Basavaraja (2007), disasters occur from the beginning of creation until the end of transactions. Another study conducted in 2016 found that maize farmers lose between 20% and 30% of their crops due to post-harvest calamities such as a lack of enough storage space (Korang, 2016).

Smallholder farmers, who develop less than 5 acres of land, account for the lion's share of farmers in Ghana. The main reason smallholder farmers store grains is to ensure household food security. Homestead stockpiling also serves as a sort of investment fund, allowing you to meet future financial requirements through a contract, trade, or blessed gift. Small-holder farmers typically store for theoretical addition, or 'profit from trading stocks,' when there is a crucial between occasional value variations (FAO, 1994). Farmers that continue to use their traditional technique of storage, which does not effectively safeguard their products, will, all things being equal, be exploited by the market. Although product pricing may rise, the hopeless stockpiling system may result in a large number of items going to waste. This has a large impact on farmers' income, resulting in poverty. Successful storage filling as economic security would help to close the "hunger gap" and strengthen global financial development.

1.3 Problem statement

The capacity of any storage area to assure the quality and quantity of the goods being stored is what determines its efficacy. The traditional storage technique fails to address the problem of post-harvest disasters constructively. The traditional storage areas are made out of mud storehouses, bamboo poles, and other materials, but they do not ensure the maize quality as it should be, but rather hasten its obliteration (Nuamah, 2015). As a result, modern stockrooms must be used to reduce post-harvest losses. Effective storage, according to research, will reduce fluctuations in agricultural revenue and eliminate merchant abuse of farmers (Coulter, 1995). In addition, Lipinski (2003) contends that to improve food security, effective management of post-harvest losses, as well as increased production, is necessary. Few stockrooms, such as the Millennium Challenge Warehouse, Ejura Farms Warehouse, World Food Program ultramodern warehouse, and others, were erected against this backdrop by the government, several groups, and the Millennium Development Authority under the first agreement (MiDA). These cutting-edge stockrooms have the most advanced capabilities for dealing with post-harvest disasters in various areas. Farmers are not utilizing these warehouses, which were built to assist farmers store and safeguard the quality and quantity of their crops. Despite the importance of warehouses and the significant resources invested in their construction, patronage of warehouses built about ten years ago in Ejura-Sekyedumase has been extremely low, with the facilities being completely abandoned by farmers, despite the government's 1 District 1 Warehouse (1D1W) policy. This begs the question, “What is it that prevents farmers from patronizing these warehouses, despite their importance?”, which has remained unaddressed for years. To ensure that the 1D1W is used effectively, it's important to understand farmers' perceptions and expectations so that the government can execute the new warehousing policy more effectively.

As a result, the goal of this research is to assess why farmers aren't using the warehouses, what they think about the present 1D1W policy, and what they anticipate from these newly built warehouses so that they can make an informed decision about whether or not to utilize them.

1.4 Research questions

1. What is the extent of usage of warehousing facilities in Ejura-Sekyedumase by maize farmers?
2. What factors influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipality.
3. What are farmers’ perceptions of the 1D1W policy?
4. What do farmers expect from the newly constructed warehouse?

1.5 Research objectives

The overall objective is to assess maize farmers’ usage of warehousing facilities in Ejura-Sekyedumase.

The study is guided by four specific objectives: which are;

1. To determine the extent of usage of warehousing facilities in Ejura-Sekyedumase by maize farmers.
2. To determine the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipalities.
3. To assess farmers’ perception of the 1D1W policy.
4. To understand what farmers expect from the newly constructed warehouse to inform their decision to patronage the warehouse facilities under the 1D1W.

1.6 Justification

In Ghana, most maize farmers have displayed worrisome execution when it comes to storing their crops, with some even surrendering. This study will be especially useful because, with the persistent expansion of maize yearly yields and the fundamental job of warehousing in the maize store network, it is expected that this study will contribute to addressing the warehousing problems that farmers face at the end of each production season.

Similarly, the study's findings and recommendations will serve as a guide for management, government, policymakers, and maize experts.

It would also add to the inventory of reflections and ideas generated by diverse qualified experts and researchers, a combination that might lead to new processes, regulations, and approaches for increasing farmer results while reducing waste. The goal of this study is to look at the real-world elements that influence maize storage and adoption. Maize storage is a major problem in Ejura-Sekyedumase, notwithstanding the explicit aims. This study will contribute to the attributes that influence the utilization of warehouses in Ejura-Sekyedumase. The goal of the evaluation is to discover if there is a pattern in farmer responses to the municipality's adoption of storage facilities.

On this foundation, educational institutions and academics will build their future study, allowing scholars to engage more fully on a worldwide scale. The study is crucial since agriculture is the backbone of Ghana's economy, and the government would benefit from increased revenue. Finally, other academics interested in enhancing storage and organization value will benefit from the findings.

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

The literature on variables influencing the adoption of modern storage facilities, farmers’ perception of new agricultural technologies and the concept of decentralization and industrialization as frameworks within which the 1D1W is pursued is discussed in this chapter.

2.2 History and approaches of warehousing

The history of storage is still largely unappreciated. Maize storage has progressed from traditional methods to high-level warehouse buildings. Calabashes, screens, clay objects, pots, and caverns were all common storage methods. All the conventional accumulation techniques were ineffective since they were geared toward weevils and grain spoiling. A few farmers utilized ashes as produced items before caring for all grains; the rubbish was dusted over the grain before it was placed in various holders like guardians. These were supposed to keep the weevils away from the grains. These conventional techniques had several drawbacks, including the possibility that a few animals, such as mice, would make their way in and create havoc. Furthermore, the techniques exposed the grains to pilferage, resulting in financial problems (Fishbein, 1980).

Farmers now rely on temporary limit methods. A portion of these storage chambers is dedicated to the drying of the collection and is urgently needed to meet this demand. They anticipate reaching the upper limit only if the grain is allowed to dry past the drying time. There's also the ethereal amassing procedure. The maize cob, sorghum, or millet particles are knotted in bunches and strung from tree appendages, poles, or tight lines in or inside the home using this method. Similarly, there is a restriction on the ground or on drying floors. Because the grain is introduced to all irritations, including local animals and the environment, the method should be temporary. It is frequently used simply if the producer is dispatched to another work or if infers are required for transporting maize grains to the stockroom for safe storage. The open wood stages, on the other hand, consist of several typically straight shafts set on a level plane on the action of upstanding supports. If the stage is used within a design, it may be raised just 35-40cm above ground level to work with cleaning and inspiration; nevertheless, open-air stages should be raised at least 1 meter above ground level. They're mostly rectangular, with some indirect or polygonal phases thrown in for good measure. Grain is stored in stacks, woven containers, or sacks at various stages. Fire may be lighted beneath elevated stages in a portion of the wet places to dry transport and divert scared little animals or other disruptions.

2.3 Nature and significance of warehousing

The genuine handling of unprocessed materials and fragment components until they are employed in the progress cycle is prioritized above the storage capacity. A stockroom is a location in a company's organizational structure where raw materials, semi-finished goods, and completed things are stored or held for a length of time. Stock in a stockroom accumulates or obstructs a stream of items, which adds expenses to the products over time.

In an ideal world, if the demand for the company's goods was known for sure, and goods could be transmitted and provided instantly to meet the need, no stocks would be needed, and a limit would be unnecessary. Nonetheless, working for a company that doesn't have any stock is absurd and ineffective, because demand is unpredictable. Various organizations utilize inventories appropriately to increase market revenue coordination and maintain overall expenses to a minimum. Maintaining stocks necessitates storage and material handling, as well as leadership coordination. As a result, rather than being a monetary requirement, accumulating becomes a money-related convenience (Bhat, 2011).

Warehousing serves as an important economic constraint. It makes raw materials, resources, and final goods more useful in terms of time. A company may offer its clients shorter lead times by tracking down the finished product and stocking it in a location closer to the market. A company or a farmer can use storage to make their products available when and where customers need them. Warehousing provides both time and spot utility, allowing the company to utilize customer service as a unique value-added real device.

The capacity dissemination focus is also fitted with a variety of security elements, such as an observation camera that acts as a cheat alert. Some people even hire wellness authorities to take over the whole office. Metal structure makes up the majority of the small stockroom. The whole building, including columns, traces, dividers, and housetops, is made of incredible materials. In comparison to a stockroom built of wood or concrete, the layout is more adaptable and can be modified appropriately in less time using these materials.

Various size stockrooms are primarily located near important complaints and where transportation is available (Bhatt, 2007). Stockrooms relocated by massive corporations are frequently large enough to accommodate a large number of consumers in the action country. This allows them to disperse the goods effectively without having to ship them from nation to country.

2.4 Purposes behind warehousing of maize

Taking care of grain in the stockroom is critical for safeguarding the money earned and ensuring that the general population has a sufficient supply of food. For farmers to acquire a quality product that fits their lifestyle, and for customers to get high-quality food results, all-around secured maize is essential. Farmers must make basic money-related speculations and put in various significant lengths of work to advance crops in the field; however, once the product enters a stockroom, concerns about a lack of significant value gradually fade, causing hardships and lowering the quality and quantity of set aside items.

The objective of collecting is to guarantee that objects care for both quantitatively and emotionally. If we exclude adversities caused by natural changes, such as reformist grain drying and breath, as well as different cycles that contribute to a loss in quantity and quality, the major issue is to protect stock from microorganism-caused accidents and to restrict development and aggravation.

The importance of grains being taken care of in a correctly built dispersion focus cannot be overstated. This implies that there is no wetness from the grain's breathing during limit, that there are no gaps in the generous dividers through which horrible tiny animals or rats may enter, and that there is no wetting that produces precipitation. The temperature of the grain at the divider varies with the seasons, as it should (Nayak et al., 2013). Except in exceptional circumstances, storage facility compartments do not require special treatment, such as carbon dioxide or phosphine, to achieve impermeability.

2.5 Warehousing Philosophy

Warehousing is the foundation of the critical action plan, which emphasizes the proper management of raw materials and pieces until they are utilized in the construction and achievement of advanced economies (Bhatt,2007). In the same way, it aids in reaching the optimal degree of customer service in any situation that necessitates high coordination costs. Furthermore, maize warehousing sets an ephemeral restriction on the amount of material that may be masterminded or reused in the reverse coordinating chain.

A flow warehouse is a structure where items for dispersions are stored. It serves as a barrier between the maker and the dealer before the corn is distributed to the retail client. Farmers, for example, would have to spread it to consumers from all over the world after the social gathering and to do so, they would have to assign these items or commodities to a distributor in a certain location. Because Ejura-Sekyedumase has a large amount of maize to distribute, the distributor demands this course warehouse where they may store these items without a significant stretch before distributing them to retail outlets. Most farmers' assignment stockrooms feature an appropriation focus amassing system to make maize, sorghum, and wheat item management more secure and lucrative.

There are several advantages to having a maize distribution stockroom, including time savings. Wholesalers may save a lot of money by using a flow stockroom to avoid passing on all of the corn. This is because they no longer have to sift through items merely to send them on to other retail outlets. It may help workers save time and effort when it comes to putting items together or managing their workflow.

A distribution stockroom is the best option for securing the goods or items that distributors are attempting to disperse to retail locations. All goods in a dispersion stockroom are preserved in good shape and retain their characteristics. This is because these tasks are tried to be completed in a moving stockroom. They are usually worked with the proper temperature, which aids wholesalers in attaining their inspiration while also preserving the item's quality and condition. As a result, they will be able to save a significant amount of money by preventing the maize from being destroyed and damaged.

In addition to the specific advantages that a distribution stockroom is likely to provide, you may also receive a great deal from the enormous piece of mind that it will provide. You will appreciate the consistency of the cerebrum that your organization will develop since your amazing and item are throughout assured, facilitated, and confirmed, as well as because the employees will not have to apply a great deal of exertion and time.

2.6 Conceptual framework of adoption of agricultural new technologies

Several interconnected components within the decision environment in which farmers work influence the adoption of agricultural technology. Lack of credit, limited access to information, insufficient farm size, insufficient human capital, tenure arrangements, lack of adequate farm equipment, chaotic supply of complementary inputs, and inappropriate transportation infrastructure, for example, were identified by Feder et al. (1985) as key constraints to rapid adoption of innovations in less developed countries. However, in various locations and for farmers in different socioeconomic conditions, not all characteristics are equally essential. Farmers' socioeconomic circumstances are the most mentioned variables influencing technology adoption. Age, education, family size, landholding size, and other characteristics that reflect a farmer's wealth level are the most often included variables in this category. Farmers with larger land holdings are considered to have the financial resources to invest in new technologies and the willingness to take risks if the technology fails (Feder, 1985). Some new technologies are labor-saving, while others are labor-intensive. In the case of labor-intensive technology such as better seed types and fertilizer, labor availability is a major factor in adoption.

2.7 Factors affecting Adoption of Agricultural New Technologies in Ghana

According to an empirical assessment of research on technology adoption in developing nations, the many factors that impact technology adoption may be classified into three main groups (IFAD, 2013). That is; variables relating to the characteristics of farmers (Pretty, 2011); factors relating to the characteristics and relative performance of technology (Deressa, 2009); and program and institutional elements (Teklewold, 2013). Education level, expertise in the occupation, age, gender, wealth level, farm size, plot features, labor availability, resource endowment, risk aversion, and other factors all play a role in farmer characteristics. Food and cash generation functions of the product, individual perceptions of the characteristics, complexity, and performance of the innovation, its availability and that of complementary inputs, the relative profitability of its adoption compared to substitute technologies, and the period of recovery are all factors that affect the characteristics and performance of the technology and practices. The availability of credit, the availability and quality of information on technologies, the accessibility of markets for products and inputs, the land tenure system, and the availability of adequate infrastructure, extension support, and other institutional factors are among the institutional factors. Farmers' investment in and adoption of sustainable technology was shown to be aided by enabling policies and programs, market connections, and access to institutional assistance and finance (Shiferaw, 2014).

2.7.1. Demographic factors

In the study of adoption variables, the gender gap between family heads is a key explanatory variable. Male and female members of rural families had varied levels of responsibility due to the social structure of the time. In most rural regions, women are marginalized members of society who lack easy access to technological knowledge. As a result, several adoption studies have found that having a female leader has a detrimental impact on technology adoption decisions. Males enjoy freedom of movement and participation in various meetings and trainings due to existing socio-cultural attitudes and conventions. As a result, male-headed families with better access to knowledge are more likely to employ innovation than female-headed households that are more influenced by cultural norms and traditions. The prevalence of income disparities between female-headed and male-headed families might potentially be a factor in the differences in agricultural new technology adoption. The cost of agricultural new technology is readily afforded by male-headed households with higher income. The cost of agricultural new technology is readily afforded by male-headed households with higher income. Another element in understanding farmers' technology adoption behavior is the age of the household head, which influences farmers' information availability and shapes their capacity to put available knowledge into action. Older farmers may have greater expertise and resources, giving them more opportunities to experiment with new technologies. Younger farmers, on the other hand, are more inclined than older farmers to accept new technologies since they have received more education. Various agricultural technology adoption studies yielded contradictory results when it came to the impact of age on adoption. Some of the data indicated that farmers' adoption behavior is influenced by their age. Different agricultural technology adoption studies conducted by other researchers, on the other hand, found that age had a favorable impact on adoption.

2.7.2. Socio-Economic factors

The most prevalent and essential variable identified to explain farmers' agricultural technology adoption behavior is the household head's educational status. Several studies have shown that it has a substantial positive impact on technology adoption. For example, Mahadi et al. (2013) investigated the variables that influence the adoption of enhanced sorghum cultivars in Ethiopia's Somali region. They discovered that in the research region, farmers with greater education are more likely to adopt better sorghum cultivars. This observation is consistent with prior findings. In previous research, Shiferaw et al. (2009) discovered that a household head's degree of education improved awareness and decision-making. Educated household heads are more likely to be aware of and comprehend an erosion problem, as well as to take steps to control it, rather than viewing erosion as a misfortune. I completely agree that education has a good and major impact on agricultural technology uptake. This is because education can improve a farmer's knowledge, skill, and attitude. It also improves farmers' analytical and problem-solving abilities. Furthermore, education improves decision-makers' locative capacity by allowing them to think critically and effectively utilize information sources. Farmers with a higher level of education should be more aware of more information sources and more efficient in assessing and understanding information regarding new agricultural technology than farmers with a lower level of education. As a result, I agree that farmers with a higher level of education are more likely to accept agricultural new technology than those who do not (Shiferaw, 2014). Many studies done in various parts of Ghana revealed that farm land, livestock ownership, and access to other productive assets have all influenced the food security status of Ghanaian rural communities. Adoption and intensity of usage of agricultural technology are heavily influenced by the availability and amount of family labor. Rural households with an active labor force are more likely to be interested in experimenting with agricultural technology. The impact of labor availability on adoption is, of course, dependent on the features of the technology being adopted. Farmers with more labor would be more likely to adopt new technologies that are more appealing and labor intensive than previous technology. Some new technologies are labor-saving, while others are labor-intensive. Tractors, harvesters, herbicides, and other labor-saving technologies, for example, will have a detrimental influence. Adoption of labor-intensive technology such as better seed types and fertilizer is influenced by labor availability. Many adoption studies, such as Solomon et al. (2012) have indicated that family labor has a beneficial influence on technology adoption. According to the reviewer, more family labor increases the likelihood of adopting agricultural new technology. The majority of Ghanaian farmers do not employ labor-saving technologies such as tractors and harvesters in their farming operations. They rely on labor-intensive technologies, and this agricultural new technology necessitates human resources from seeding to crop harvesting. On the other hand, the influence of farm size on adoption and intensity of usage of agricultural technology varies between adoption studies. Some research found that the variable had a beneficial impact on adoption decisions. For example, researchers in Ethiopia's Central Highlands investigated the factors that influence acceptance and intensity of usage of enhanced maize varieties and discovered a substantial beneficial effect. Other researches, such as Ogada et al. (2014), discovered a reversal impact of land size on the adoption of inorganic and enhanced maize cultivars together. According to reports, farm size has a favorable correlation with adoption. The reviewer agreed with those researchers who claimed that the size of a farm had a favorable link with the adoption of agricultural new technology. This is because most Ghanaian farmers cultivate a variety of crops, which necessitates a bigger farm area. Furthermore, the majority of Ghanaian farmers practice mixed farming (crop and animal production). Off-farm income, according to Diiro (2013), would give farmers with liquid cash to purchase productivity-enhancing inputs like better seed and fertilizers. Ibrahim et al. (1967) found that the respondent's yearly income had a strong positive association with the adoption of suggested technologies in Bangladesh, i.e., the greater the respondent's annual income, the more they embraced recommended technologies. In our research, the impact of yearly gross income on the adoption of maize, wheat, barley, and sorghum technology packages was strong and statistically significant (Diiro, 2013).

2.7.3. Institutional factors

Institutional considerations refer to the extent to which institutions influence smallholders' adoption of technology. All agricultural development services, including as finance, insurance, and information distribution, are provided by institutions. Facilities and processes that improve farmers' access to productive inputs and product markets are also included. Extension service is a critical institutional component that distinguishes farmers' adoption status. Currently, the extension system is responsible for a large portion of agricultural technology distribution. As a result, having access to training, demonstrations, field days, and other extension services offers a platform for acquiring relevant knowledge that encourages technology adoption. Several studies have measured farmers' access to extension services using various variables. Farmers who had more regular contact with extension agents were more likely to embrace wheat technology, according to a research performed in four Ethiopian areas. Farmers who had less frequent contact with extension agents were less likely to accept wheat technology (Tefera, 2016). In a similar study, Mahdi (2013) discovered that the distance between farmers' homes and the DA's office had a detrimental impact on technology adoption. Farmers' expertise with extension is another key factor to consider. Farmers' yield demands are likely to rise as a result of their experience with agricultural extension. With the advent of new technologies, there is an increased need for information that can be used to make choices. Agricultural extension agencies, as a result, provide valuable information on new agricultural technology. Access to such information sources can be critical in the adoption of better cultivars (Egge, 2005). The respondents' market distance is critical for manufacturers to obtain a competitive market pricing by lowering transportation costs. Farmers are losing touch with up-to-date market information as a result of the growing distance between them and modern agricultural technologies. According to the data (Tefera, 2016), maize and technology package adoption increased as families moved closer to the market, but wheat technology package usage decreased. The market drove technological adoption, and these findings corroborated Bayissa's (2014) conclusions. Other research has found a negative association between residential distance from an all-weather road and fertilizer adoption. Gebressilassie and Bekele (2015), for example, discovered that distance to market hubs was adversely and strongly associated to fertilizer adoption. The transportation cost of agricultural inputs was reduced by reducing the distance from the market. As a result, there was a negative connection between market distance and the usage of inorganic fertilizer (Ogada, 2014). Access to credit services is a source of funding for low- and middle-income households to purchase agricultural supplies. In Ethiopia, credit services are provided in both kind and cash, with a focus on credit services for agricultural production systems. According to several writers, farmers who have access to financing have a higher likelihood of adopting agricultural new technology than those who do not. Access to finance, according to Daniel and Kafle (2011), can improve the likelihood of families adopting agricultural new technology by compensating financial shortfalls (Kafle, 2011). A similar finding suggests that financial resources were required to fund the adoption of new technology (Ogada, 2014). They found that households with higher access to official and/or informal finance were more likely to embrace technology.

2.8. Farmers' perceptions of new agricultural technology

Social scientists studying agricultural technology adoption decisions have long been interested in the relevance of commodity-attribute perceptions. Indeed, anthropologists and sociologists have taken the lead in this field, arguing that farmers' subjective judgments of agricultural technology impact adoption behavior using qualitative approaches (Kivlin and Fliegel, 1966, 1967; Nowak, 1992). Economists have been lagging in their studies on this issue. However, because most previous economists' studies on technology adoption (O'Mara, 1980) had access to direct observations of farmers' views, it was unable to test the premise that perceptions of technological characteristics affect adoption decisions in a direct and quantifiable way. Instead, in economic models of the drivers of adoption decisions, factors that impact farmers' access to information and therefore their perception formation (e.g. extension, education, media exposure, etc.) are commonly utilized (Feder et al., 1985; Shakya and Flinn, 1985; Kebede et al., 1990; Poison and Spencer, 1991; Strauss et al., 1991). Economists studying consumer demand, on the other hand, have accumulated a substantial amount of evidence demonstrating that consumers have subjective preferences for product characteristics and that their demand for products is significantly influenced by their perceptions of the product's attributes (Jones, 1989; Lin and Milon, 1993). Lin and Milon (1993) discovered that commodity characteristics and consumers' safety perceptions were relevant in predicting decisions to consume and the frequency of consumption of shellfish in the United States, using a double-hurdle model. Jones (1989) discovered that customers' subjective views affected cigarette smoking decisions using Cragg's double-hurdle framework. Farmers' (the consumers of agricultural technologies) subjective judgments of technological features influence their adoption decisions, according to economists studying the adoption of new agricultural technology. No research analyzing the direct impact of farmers' subjective judgments of agricultural technology features on adoption decisions was discovered in large reviews of adoption studies in developing countries (Feder et al., 1985; Feder and Umali, 1993). Farmers' views of the qualities of contemporary rice varieties had a major impact on adoption decisions in Sierra Leone, according to Adesina and Zinnah (1993). The scarcity of empirical economic studies on this topic calls for more research.

CHAPTER THREE

METHODOLOGY

3.1 Introduction

This section discusses the processes that were used to gather and analyze data. It also goes into great depth on the study's research methodology, subject areas, unit of analysis, sampling technique, and sample size.

3.2 Research Design

A descriptive survey approach was used to look into the current status of warehouses and then provide remedies based on the findings.

According to Kerlinger (1995), descriptive research isn't limited to fact-finding, but may also lead to the development of key knowledge principles and the resolution of critical issues. It tends to be employed while gathering data regarding individuals' views, beliefs, behaviors, or any of the variety of education, economic or societal difficulties (Orodho and Kombo, 2002). (Orodho and Kombo, 2002).

3.3 Study Population

The study was carried out in Ejura-Sekyedumase. The study populace for this research includes all maize farmers in the region. Ejura-Sekyedumase Municipality was chosen based on the Region, Ghana. In 1998, an Administrative Instrument (L.I 1400) separated it from the preceding Sekyere and Offinso regions. L.I. 2098, enacted in 2012, elevated the region to municipal status (GSS, 2013). Within Longitudes 1o5” W and 1o 39” W and Latitudes 7o 9” N and 7o 36” N, the municipality is located in the northern part of the Ashanti region. It defines the boundaries of the Atebubu-Amantin District in the northwest, Mampong Municipality in the east, Sekyere South Municipality in the south, and Offinso Municipality in the west.

Agriculture, business, administration, and industry employ many people in the municipality. Agriculture is the municipality's largest industry in terms of employment and earnings. The region employs around 69.7% of the Municipality's population (Ejura-Sekyedumase Municipal Assembly, 2012). In the Municipality, a variety of crops are grown. Maize, sweet potato, beans, rice, plantain, cassava, and groundnuts, to name a few, are notable among them.

Abbildung in dieser Leseprobe nicht enthalten

Figure 3.1 was removed by the editors for copyright reasons.]

Figure 3.1: Guide of the Ejura-Sekyedumase Region

Source: Ejura-Sekyedumase Metropolitan Gathering, 2012

3.4 Sample and Sampling Technique

The Ejura - Sekyedumase Municipality is Ghana's largest maize producer (MoFA, 2010). The municipality was chosen for the study because it is known for its large-scale maize production and dedication to Ghana's food security.

The sample size was calculated to be 306 based on Yamane (1967). After agricultural groups have been stratified, a multistage random selection approach will be utilized to choose maize farmers under the jurisdiction of the Ejura-Sekyedumase Municipal.

Five (5) communities were chosen at random in the primary stage based on the metropolitan profile obtained from the municipal MoFA office. Ejura, Aframso, Sekyedumase, Hiawoanwu, and Kasei are the communities chosen. The next step involved a simple random selection of 80 farmers each from Ejura, Sekyedumase, and Hiawoanwu, as well as 33 farmers from Aframso and Kasei, for a total of 306. The decision was made based on the towns' topographical size.

Calculating the sample size;

n = Abbildung in dieser Leseprobe nicht enthalten

n = Abbildung in dieser Leseprobe nicht enthalten = 306.158

where;

n = the sample size

N = the population size for maize farmers

e = the sampling error

3.5 Data Collection Instrument and Procedures

The study relied on primary data, which was gathered through interviews (self-administered questionnaires). Farmers participating in maize production were interviewed using a standardized interview schedule that includes open and closed-ended questions about warehouse usage. Specific data collected include the age of the respondents, educational levels, awareness of warehouse facilities and what farmers expect to inform their decision to patronage the new warehouse constructed.

3.6 Data Analysis

The data obtained was organized and investigated, which aided in answering the research questions and achieving the aforementioned objectives. The information from closed-ended questions and open-ended questions was handled using both quantitative and qualitative data analysis methods, following which the findings were interpreted and a report was written.

Quantitative data was edited, coded, and analyzed using SPSS. The descriptive and inferential statistical analyses were carried out using SPSS. The extent to which farmers use storage facilities in Ejura-Sekyedumase was investigated using descriptive analysis with frequencies. The logit model was used to examine the factors influencing farmers' adoption of contemporary preservatives. In addition, qualitative content analysis was performed to determine what farmers should expect from the newly built warehouses. Farmers' opinions on the government's 1D1W policy were also analyzed using a Five-Likert scale.

The results of the qualitative and quantitative data analysis were evaluated and debated to answer the research questions and meet the study's objectives.

Objective 1: The extent of warehouse usage among maize farmers

The study's first objective was to figure out how many maize growers utilize warehouses. The participants in this study were asked how much access to the warehouse they had. Descriptive analysis was used to do this. The degree of warehouse utilization among maize producers was analyzed using descriptive statistics with frequencies.

Objective 2: To determine the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipality

The logit model was used to examine the second objective, which was to determine the characteristics that impact farmers' decisions to use warehousing facilities in the Ejura – Sekyedumase municipality. The factors that influence maize farmers' decisions to use the warehousing facility were estimated using the logit model. The choice of the logit model for analysis is compatible with adoption literature (see, for example, Griliches, 1957; Rogers, 1983), which defines adoption as a logistical process. Slope and Kau (1973) and Pindyck and Rubinfeld (1973) suggested the edge dynamic hypothesis, which was used in this study (1998). When farmers are confronted with the option of whether or not to embrace technology, the theory states that there is a response threshold that is based on a set of criteria. As a result, no adoption is observed at a stimulus value below the critical threshold, but a reaction is triggered above the critical threshold value. The relationship: Yі = βXі + uі is commonly used to model such occurrences. Where Yi is equal to one (1) when a choice is made to adopt and zero (0) otherwise; this implies that Yi = 1 if Xi is higher than or equal to a critical value, X*, and Yi = 0 if Xi is less than a critical value, X*.

At the threshold level, X* reflects the combined impact of the independent variables (Xi).

Equation 1 depicts a binary choice model in which the likelihood of a specific technology (Y) being adopted is estimated as a function of independent factors (X). This is expressed mathematically as:

Abbildung in dieser Leseprobe nicht enthalten (2)

Abbildung in dieser Leseprobe nicht enthalten (3)

Where Yi is the observed response for the ith observation of the response variable, Y, and n is the number of observations. This indicates that for an adopter (farmers who use a warehousing facility), Yi equals 1 and for a non-adopter, Yi equals 0. (i.e. farmers who do not use a warehouse facility). Xi is a collection of independent factors connected with the ith individual, such as farm size, that influence the likelihood of adoption (P). A normal, logistic, or probability function can be used to represent F. The logit model estimates P using a logistic cumulative distributive function (Pindyck and Rubinfeld, 1998):

Abbildung in dieser Leseprobe nicht enthalten (4)

Abbildung in dieser Leseprobe nicht enthalten (5)

The probability model, according to Greene (2008), is a regression of Y's conditional expectation on X, yielding:

Abbildung in dieser Leseprobe nicht enthalten (6)

The parameters are not necessarily the marginal effects of the individual independent variables because the model is non-linear. Differentiating equation (6) for Xij yields equation (7) (Greene, 2008), which represents the relative influence of each independent variable on the likelihood of adoption:

Abbildung in dieser Leseprobe nicht enthalten (7)

The parameters were estimated using the maximum likelihood approach. The application of the logit model in the study implies that the farmer will choose to use a warehouse facility when the cumulative impact of certain circumstances outweighs his or her natural reluctance to change at a particular moment in time. In assessing the factors influencing maize farmers' decisions to use warehouse facilities, the probability model (logit) is preferred over standard linear regression models since the farmer's parameter estimations are asymptotically consistent and efficient.

The estimating technique will also address the issue of heteroscedasticity, limiting the conditional probability of opting to utilize a warehouse to a range of zero (0) to one (1). In this investigation, the logit model was chosen over the probit model because of its mathematical convenience and simplicity (Greene, 2008), as well as the fact that it has been used in comparable studies by Kato (2000), Boahene et al. (1999), Feder et al. (1985), and Rogers (1985). (1995).

The empirical model for estimating the logit model is as follows:

Abbildung in dieser Leseprobe nicht enthalten (8)

Where Xi is the sum of the impacts of X explanatory factors that encourage or discourage farmers from using a storage facility.

Abbildung in dieser Leseprobe nicht enthalten

Table 3.1. Summary of the variables used to analyze the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipality.

Abbildung in dieser Leseprobe nicht enthalten

The log-odds in favor of farm households adopting contemporary agricultural production technology are X1... Xi are elements that encourage or discourage farm households from using a warehouse.

Objective 3: Farmer’s perception of the 1D1W policy

The third objective of the study assessed farmers’ perception of the 1D1W policy. Carefully constructed statements on the government’s policy were made.

This was achieved by using a five-point Likert scale (1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, 5 = strongly disagree).

The perception index (I.P) is a mean score of the ranks that are represented as: Abbildung in dieser Leseprobe nicht enthalten

P.I = Abbildung in dieser Leseprobe nicht enthalten

Where;

1. P.I = Perception Index
2. x = Number of maize farmers who responded to the specific perception statements
3. fsa = frequency of strongly agree
4. fa = frequency of agree
5. fn = frequency of neutral
6. fd = frequency of disagree
7. fsd = frequency of strongly disagree

Objective 4: To understand what farmers expect from the newly constructed warehouse

The fourth objective was to understand what farmers expect from the newly constructed warehouse to inform their decision to patronize warehouse facilities under the 1D1W, this objective was analyzed using qualitative content analysis. Texts from the data collected were coded into manageable code categories for the analysis. After the text was coded into code categories, the codes were further categorized into “code categories” after that the data was summarized.

3.7 Explanatory Variables in the Logit Regression Model

AGE: the age of a farmer is stated as the actual years lived by a farmer in the study. This was measured in years and as a continuous variable. Age has a mixed effect on the likelihood of a farmer to employ or not to employ a warehouse facility. Age is expected to have either a positive or a negative relationship with the idea to employ or not to employ a warehouse facility. Older people are normally reported to be conservative than young people and rarely adopt to new agricultural technologies.

GENDER: the gender of respondent in the model was measured using dummy variable. A value of 1 is assigned to a male farmer and 0 if otherwise. Gender is expected to positively influence farmers to use warehouse facilities because male farmers have better access to knowledge and are more likely to employ warehouse facilities as compared to their female counterparts who are more influenced by cultural norms and traditions. (Mahadi et al., 2013). These relates to the fact that females lack control over economic resources and the nature of their economic activity.

EDUCATION: the farmers’ educational level is measured by the years spent in school. This is expected to have a positive relationship with the usage of warehouse facilities. Studies from past literature attributed this to the fact that the higher an individual advances in formal education, the more likely to adopt to new agricultural technology (Shiferaw, 2014). This is because, they are more aware of more information sources and more efficient in assessing and understanding information regarding new agricultural technology.

DISTANCE: it is measured as how far or near the warehouse facilities is from the farmers in the communities. It is measured in kilometers. Distance is expected to have a positive influence on the dependent variable because the closer a farmer is to a warehouse facility, the more likely is for him or her to adopt the facility because of lower transportation cost it goes with (Tefera, 2016).

ACCESS TO CREDIT: access to credit by respondent in the model was measured using dummy variable. A value of 1 is assigned to a maize farmer who has access to credit and 0 if otherwise. Access to credit is expected to positively influence maize farmers to use warehouse facilities because farmers who have access to financing have a higher likelihood of adopting agricultural new technologies. According to Daniel and Keple (2011), access to credit can improve the likelihood of famers adopting agricultural new technologies by compensating financial shortfalls.

ACCESS TO EXTENSION SERVICE: it is measured using dummy variable. A value of 1 is assigned to a maize farmer who gets access to extension services and 0 if otherwise. Access to agricultural extension agent refers to the contact between the farmer and extension agent in order to provide agricultural information, agricultural advises and disseminating of new technologies and innovations. The expected sign is positive because farmers with access extension officers are more likely to use a technology (Tefera, 2016).

With the table describing the variables used in the Logit model, a prior expectation of positive sign shows a likelihood of using a warehouse facility and the possibility of the variable having positive influence on the dependent variable whilst a negative sign shows a likelihood of not using a warehouse facility and the tendency of the variable having negative influence on the dependent variable.

FBO PARTICIPATION: it is measured as a dummy variable. A value of 1 is assigned to a maize farmer being a member of a farmer-based organization and 0 if otherwise. The expected sign is positive because it is believed that maize farmers who are part of a farmer-based organization have certain goals in common hence the likelihood of adopting a new technology becomes easy.

FARMER CATEGORY: it is measured using dummy variable. A value of 1 is assigned to a maize farmer being a small-scale farmer and 0 if otherwise. The expected sign is negative because small scale farmers should not have a problem with storage space because they don’t produce many bags of maize as compared to large scale producers hence the likelihood of using not using a modern warehouse facility. This is because the smaller the bags of maize produced, the smaller a space is required for storage.

QUANTITY OF PRODUCE HARVESTED: the quantity of produced harvested as far as maize cultivation in number bags is concerned is expected to have a positive influence on the probability that a farmer will use a warehouse facility. Farmers with the commercial farming objective will need a bigger space for storage hence using a warehouse facility.

AWARENESS OF A WAREHOUSE FACILTY: it is measured using dummy variable. A value of 1 is assigned to a maize farmer being aware of the availability of a modern warehouse facility and 0 if otherwise. The expected sign is positive because a maize farmer is likely to utilized a warehouse facility if he or she knows it has been made available for him or her.

ACCESS TO READY MARKET: it is measured using dummy variable. A value of 1 is assigned to a maize farmer who has access to ready market and 0 if otherwise. The expected sign is positive. This is because a farmer who has a ready market is more likely to use a warehouse facility for storage for storage so that he or she can sell his or produce at premium whenever there are maize shortages in the market.

LEVEL OF IMPORTANCE OF A WAREHOUSE: it is measured using dummy variable. A value of 1 is assigned to a maize farmer who finds warehouse facility important and 0 if otherwise. The expected sign is positive because the more a maize farmer finds a warehouse facility important to him or her the more likely he or she will use it.

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 Introduction

The findings of the study are analyzed and discussed in this chapter. This chapter also includes thorough explanations of some of the elements that contribute to warehouse utilization as indicated in the results. In addition to the study's findings, the chapter eloquently describes them.

4.2 Demographic characteristics of respondents

The demographic features of the respondents in the research are presented in this section. The gender, age, and level of education of the respondents were all investigated by the researcher. Because demographic variables are important in determining respondents' ability to use contemporary storage facilities, the researcher focused on them. The gender distribution of the responders is seen in Table 4.1.

Table 4.1: Gender of respondents

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

Table 4.1 reveals that males made up 69 percent of farmers interviewed, while females made up 31 percent. This might imply that farming is dominated by males and that farming is viewed as a man's work. Domestic duties, difficulty in obtaining resource inputs, land, and financial loans may all contribute to the disparity. Domestic tasks such as fetching water, cooking, washing, and child-care are solely the responsibility of women in a typical Ghanaian environment like the study region, decreasing their time spent on-farm operations. These social obligations may limit a woman's time and contribute to gender disparity in agriculture. Adam (2010) proposed that women get permission from their spouses before obtaining credit, which may be denied or accepted on their behalf by their husbands. Similarly, Goetz and Gupta (1996) argued that money acquisition is mostly controlled by males in the home rather than women.

Table 4.2. Descriptive characteristics of the respondents

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

From the data collected, the average age of the respondents is approximately 41 years with 20 and 76 being the youngest and oldest age of the respondents respectively. Also, the average years of formal education is approximately 6 with 18 years being the maximum years of formal education although some of the respondents had no formal education. Again, the respondents average household size is approximately 6. The minimum and maximum household is 1 and 21 respectively.

According to the study, the majority of farmers interviewed are between the ages of 31 and 40, accounting for 32.4 percent, followed by those between the ages of 41 and 50, accounting for 28.1 percent, followed by those between the ages of 20-30, accounting for 23.2 percent, followed by those between the ages of 51-60, accounting for 9.8 percent, followed by those between the ages of 61 and above, accounting for 6.5 percent. The fact that the majority of farmers are young (under 40 years old) indicates that they can enhance productivity since they are viewed as strong, energetic individuals who can participate in agricultural operations for an extended period. This contradicts Adam's (2010) conclusions that the youth aren't interested in farming because they want fast money, therefore they leave Ejura-Sekyedumase to go for non-farm occupations. However, those aged 60 and older are the least likely to engage in farming, as they lack the muscle to engage in intense farming tasks at this age.

Also, majority of respondents (71.2 percent) had households with more than four persons, with the smallest household size (1-2 people) accounting for 7.5 percent of the overall proportion. Because most farmers make extensive use of family labor, labor costs are kept low.

Again, a person's degree of education is essential in determining their level of knowledge and, as a result, their capacity to comprehend and adapt to a new system, such as modern warehousing facilities for farmers. According to the findings, 22.5 percent of farmers had elementary education, 24.5 percent JHS (Junior High School), 11.4 percent SHS (Senior High School), 5.2 percent higher education, and 36.3 percent had no formal education. Farmers' educational levels are significant in assessing the use of storage facilities because their educational level influences extension services and access to financing, both of which are crucial factors that influence farmers' capacity to adopt improved maize production technology. In India, Badal and Singh (2001) had a similar viewpoint. Small-scale farmers need basic education to accept new technology. The study also determined the number of respondents that keep their product in a warehouse, and it was discovered that just 47 (15.4 percent) of respondents do so with maize. The remaining 259 (84.7%) respondents do not have their items stored in a warehouse. This is shown in Table 4.3.

Table 4.3. Usage of Warehouse Facilities

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

The researcher also looked into how long the respondents had been using the warehouse and provided the results in Table 4.4.

Table 4.4. Number of years of utilizing a warehouse by respondents who uses a warehouse.

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.1 Years of warehouse usage by respondents

According to the study, the majority of respondents (259) had never utilized a warehouse facility, accounting for 84.6 percent of all respondents. In the other category of respondents, 12.4 percent (38) have used a warehouse for 1 to 10 years, 7 (2.3%) have used a warehouse for 11 to 20 years, and 2 (0.7%) have used a warehouse for more than 20 years. These data reveal an intriguing fact: farmers use warehouse facilities at a lower rate in the municipal. This is feasible because most farmers possess their store rooms or sheds, and they often choose to keep their agricultural produce in their store rooms or sheds.

On the other hand, because these warehouses are located far from the farmers' homes or farms, they would like to retain their corn in their store rooms or sheds for the ease of not having to bring it back to the warehouse.

The researcher went a step further and asked the respondents if they were aware of a contemporary warehousing facility in the municipality. Beliefs that farmers' awareness of a contemporary warehouse can be an indicator of their capacity to utilize it, whereas farmers' unawareness can be an indicator of no utilization.

Table 4.5. Awareness of modern warehouse availability by respondents

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.2 Warehouse awareness by respondents

According to the findings, 119 respondents (38.9%) are unaware of a modern warehouse in the Ejura-Sekyedumase Municipal, whereas 187 (61.1%) are aware of one or more modern warehouses in the municipality. The researchers went on to look at the opinions of individuals who claimed they were aware of a contemporary warehouse in the municipality, whether they had utilized it or not, and their reasons (for those who had not used it) for not utilizing it.

Table 4.6. Respondents who are aware of a modern warehouse availability in the municipal and have used the facility before.

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

From Table. 4.6, only 0.5 percent of the 187 respondents who were aware of a contemporary storage facility in the municipality had used it, according to the research. The survey discovered that the remaining 99.5% of people who haven't used the facility are doing so for a variety of reasons, including having their own storage space, the cost of storing their food in a warehouse, and inadequate management services.

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.3 Respondents who are aware of a warehouse and have used it.

Table 4.7. Readiness to patronize a modern warehouse if made available

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.4 Respondents readiness to patronize a modern warehouse facility

The majority of farmers polled (93.8 percent) said they would utilize a modern warehouse if one were made accessible to them, with only 6.2 percent saying they would not.

The study went one step further and looked at the respondents' thoughts on the availability of contemporary warehouses. Again, the perception that the systems are available can be a pointer or determinant of the farmers' capacity to embrace the use, but the notion that the warehouses are poorly accessible to clients can be an indicator of low adoption. The researchers inquired about the availability of modern warehouses to the respondents.

Table 4.8. Availability of warehouse in the municipality

Abbildung in dieser Leseprobe nicht enthalten

Source : Field survey,2021

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.5 Warehouse availability in the municipal

According to the findings, 64.7 percent of respondents believe warehouse availability in Ejura-Sekyedumase is very poor, 32.4 percent believe warehouse availability is low, and 2.9 percent believe warehouse availability in the municipal is pretty good (medium).

Post-harvest losses

Table 4.9. Experience of high storage losses in the municipality

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

Abbildung in dieser Leseprobe nicht enthalten

Figure 4.6 Post-harvest losses experienced by respondents

In the Ejura-Sekyedumase Municipal, Table 4.11 demonstrates how losses occurred during storage among selected maize growers. There were no high storage losses in 56.5 percent of cases and high storage losses in 43.5 percent of cases, respectively. Storage losses among farmers are still substantial, although no high storage losses exceeded high storage losses by 13 percent.

4.3 The extent of warehouse usage among maize farmers

The study's initial goal was to determine the extent to which maize growers used warehouses. This was accomplished through the use of descriptive analysis. The degree of warehouse utilization among maize producers was investigated using descriptive analysis using frequencies.

Table 4.10. Extent of warehouse usage in the municipality (months)

Abbildung in dieser Leseprobe nicht enthalten

Source: Field survey (2021)

According to the research, the respondent's average duration of use was four months. Farmers, on average, store their products in a warehouse for up to four months. They said that they don't keep their products for longer periods since they don't have enough funding for production. Some also stated that not storing their food for extended periods allows them to fulfill some of their family's demands, such as paying fees, utilities, and so on.

4.4 To determine the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipality

The logit model was used to examine the second objective, which was to discover the characteristics that impact farmers' decisions to use warehousing facilities in the Ejura – Sekyedumase municipality.

The Pseudo R2[] of the logit model estimation was 0.255 (Table 4.11), implying that the variables in the model can explain roughly 25.5 percent of the likelihood of farmers using or not using a warehouse. At the 5% level, the Log-likelihood Ratio (LR) was also shown to be significant (Table 4.11). This indicates that the model's explanatory factors together influenced farmers' likelihood of utilizing a storage facility. The results of the model also indicated a chance of utilization of 0.859. This indicates that farmers in Ejura-Sekyedumase are likely to employ warehouse facilities if some social, economic, and institutional barriers to technology adoption are overcome. Based on the aforementioned goodness of fit metrics, it was determined that the logit model used had integrity and was hence suitable.

Financial assistance was found to have a positive association with the likelihood of use. At the five percent level, this was shown to be significant (Table 4.11). Also, from Table 4.11, it is observed that the estimated odds ratio 3.168 indicates those farmers who gets access to credit and are 3.168 times more likely to use a warehouse facility compared to those farmers who don’t any access credit. This means that financing is a big help when it comes to adopting agricultural production techniques. This is in agreement with the popular belief that farmers' high levels of poverty and lack of access to finance make it nearly impossible for them to buy technology (Ministry of Food and Agriculture, 2010). Also, FBO participation was found to have a positive relationship with the likelihood to use a warehouse facility. It was found to be significant at five percent (Table 4.11) and this agrees with Rosenzweig (1995) while considering allocation of Green Revolution progresses in India who stated farmers inside a social affair of individuals acquire from each other the benefits and use of another innovation. The estimated odds ratio 6.205 (Table 4.11) indicates those farmers who belongs to a farmer based organization and are 6.205 times more likely to use a warehouse facility compared to those farmers who don’t belong to any FBO. Also, the maximum level of education among the farmer was found to have a negative relationship with the probability of adoption. It was however, significant at five percent level (Table 4.11). The estimated odds ratio was also found to be 0.807 (Table 4.11). The implication of this is that farmers who are well educated are 0.807 more likely to adopt modern agricultural production technologies than those without formal education. This is consistent with the literature that education creates a favourable mental attitude for the acceptance of new practices especially of information-intensive and management-intensive practices (Waller et al, 1998; Caswell et al, 2001). Also, the age of the farmers was found to be significant at the 10 percent level (Table 4.11). At the younger age, farmers may not be able to adopt modern agricultural production technologies, especially capital intensive ones because of the fact that they might not have adequate resources to do so. At an older age, farmers’ volume of economic activities reduced hence they may be unable to pay for technologies. Besides, older farmers have accumulated years of experience in farming through experimentation and observations and may find it difficult to leave such experiences for new technologies. In addition, farmers’ perception that technology development and the subsequent benefits, require a lot of time to realize, can reduce their interest in the new technology because of farmers’ advanced age (Caswell et al, 2001; Khanna, 2001). Elderly farmers often have different goals other than income maximization, in which case, they will not be expected to adopt an income – enhancing technology (Tjornhom, 1995). Also, farmer category was found to have a negative relationship with the likelihood to use a warehouse facility. However, it was found to be significant ten percent (Table 4.11). The estimated odds ratio 0.386 (Table 4.11) indicates small scale maize farmers and are 0.386 times more likely to use a warehouse facility compared to those farmers produce on a large scale. Also, from Table 4.11, the level of importance of warehouse facility was found to have a negative relationship with the likelihood to use a warehouse facility. Nonetheless, it was found to be significant ten percent (Table 4.11). The estimated odds ratio 0.167 (Table 4.11) indicates farmers who finds warehouse facilities to be important and are 0.167 times more likely to use a warehouse facility compared to those farmers who don’t find warehouse facilities to be important.

Table 4.11. Factors influencing warehouse usage by maize farmers in Ejura-Sekyedumase

Number of observations = 306; LR Chi Square (10) = 48.679; Prob.> Chi2 = 0.0000; Log likelihood = -213.804; Pseudo R2 = 0.255; Predicted Prob. (Usage) = 0.859

Abbildung in dieser Leseprobe nicht enthalten

Source: Authors’ computation, 2021. *p<0.1, **<0.05

4.5 Farmer’s perception of the 1D1W policy

The third objective of the study assessed farmer’s perception of the 1D1W policy. Carefully constructed statements on the government’s policy were made.

This was achieved using a five-point Likert scale (1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, 5 = strongly disagree).

The mean score of the ranks was estimated as the perception index (I.P) which is expressed as:

P.I = Abbildung in dieser Leseprobe nicht enthalten

Where;

1. P.I = Perception Index
2. = Number of maize farmers who responded to the specific perception statements
3. fsa = frequency of strongly agree
4. fa = frequency of agreeing
5. fn = frequency of neutral
6. fd = frequency of disagree
7. fsd = frequency of strongly disagree

Results of farmers’ perception about the 1D1W policy as listed to them are presented in Table 4.12. Farmers’ perception that there is a need for the construction of new warehouses was measured. Majority of the farmers (226) strongly agree that there is a need for the construction of warehouses in the various districts. The mean score of 1.36 signifies that farmers strongly belief that the is the need for the construction of warehouses in the various districts under the policy. Again, the policy’s potential of solving post-harvest losses among farmers was also assessed. Majority of the farmers (185) believe that the policy has the potential of solving post-harvest losses among farmers. The mean score of 1.46 nonetheless signifies that farmers moderately agree that the 1D1W policy has the potential of solving post-harvest losses among farmers. Also, farmers moderately agree that the policy will improve the usage of warehouse facilities in the municipal, will enable farmers to get access to credit through warehouse receipts, make the cost of warehouse storage affordable, bring fair and equitable accessibility of the storage facilities, improve warehouse management, enable farmers get access to their produce anytime from the warehouse and benefit farmers. Again, the perception of farmers on whether the storage facilities under the policy is only for commercial farmers was also assessed. Majority of the farmers (116) strongly agrees that storage facilities under is for commercial farmers only. The mean score of 3.81 nonetheless signifies that farmers are somehow in disagreement that the storage facilities under the 1D1W policy is only for commercial farmers.

Table 4.12 Perception index table of respondents

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Source: Field survey (2021)

The mean score for these perceptual characteristics is between 1.35 and 1.77, indicating that most farmers agree with the assertions reflected by these indices. Farmers were dissatisfied with the declaration that the 1D1W policy's storage facilities are reserved for commercial farmers.

4.6 What do farmers expect from the newly constructed warehouse?

The fourth objective is to study what farmers expect from the newly built warehouse so that they may make an informed decision about whether or not to use warehouse facilities under the 1D1W. To do this, the study asked farmers what they anticipate from a newly constructed or yet-to-be constructed warehouse to inform their decisions to use the facility under policy 1D1W.

Interaction with maize farmers in the Ejura-Sekyedumase Municipality provided data for this objective. The observations are divided into sub-themes as shown below.

1. The capacity of the warehouse

The study discovered that the lack of functioning of the newly constructed warehouse under the 1D1W policy is the primary reason why most farmers in Ejura-Sekyedumase are not patronizing it. Owing to the municipality's post-harvest loss problem, farmers in this region have always had to cheaply sell off their agricultural products for fear of having to dispose of the product due to a lack of storage facilities.

Farmers expected the newly constructed warehouse to be larger (capacity) than the one that the government built in response to the farmers' demands.

Farmers describe the policy's capacity for the newly constructed storehouse.

“The warehouse that has been built is a pretty tiny space. This is not what we expected because the one created under the policy can only hold one or a few individuals. We expected a large one that could accept the output of the majority of the farmers in the municipality because those of us who grow maize in the municipality has a large population” [Farmers].

2. Warehouse receipt systems

Farmers also anticipate the implementation of warehouse receipt systems. According to the study, most farmers have difficulty obtaining bank loans to finance their farming activities, which allows middlemen and women who buy their products to take advantage of them with their loans. From the standpoint of the farmers, the majority of them are handicapped due to a lack of financial resources, which leads to deception by middlemen and women who rely on their financial assistance. Farmers listed some of the benefits of the warehouse receipt systems when they were first introduced by the newly built warehouse and others that will be built;

“It will assist us if they install warehouse receipt systems because most of us don’t have money and don't produce in huge quantities.” This method will allow us to store our products more efficiently and use the warehouse receipt to acquire much-needed financing. It would also allow us to sell in organized marketplaces, where we will be able to make more money than in informal markets.” [Farmers].

3. Accessibility

Farmers also anticipate the newly constructed warehouse to provide fair and equitable access to all farmers in the municipality, regardless of where they come from or which political party they support. The majority of farmers anticipate the newly constructed warehouses, or those that will be developed, to be located within their towns, according to the survey. This is because they believe they will be able to quickly reach the warehouse from their different villages, rather than having to travel from their many settlements to one location to keep their produce.

The farmers said:

“We don't want any politics to be linked with the warehouse building. This is because it will have an impact on farmers. We also don't want any prejudice. If I have ten bags and someone else has, say, 1000 or 100 bags, they should accept both of our products since everyone has their power. Also, having these warehouses near our towns would lower our transportation and labor costs, which we will appreciate.”

4. Good management practices and Affordability

“If the warehouse people arrive with appropriate payment terms and circumstances, we are willing to cooperate with them.” Some farmers also stated that “if the costs of storing produce in a warehouse are very affordable, we will love to patronize their services because we don't have a lot of money” and that “if they (warehouse managers) will buy our produce at correct prices as the Accra women pay us, we will sell our products to them.”

5. Inclusion of dryers

Most farmers in the Ejura-Sekyedumase Municipality find it difficult to dry their harvested maize during the rainy season, according to the research, therefore the addition of dryers in the newly constructed and yet to be constructed warehouses will be extremely useful to them. Farmers' expectations were as follows:

“We want the government to install dryers in the warehouse so that constant drying is no longer a problem. When we dry our product in the sun and it becomes almost dry or completely dry during the rainy season, the raindrops fall on the grains, moistening them anew. Because we can't anticipate when it will rain, we have to dry the maize several times. If they can add dryers to the newly constructed warehouse, it will greatly assist us in that we will be able to send our products directly to the dryer, which will dry them for us in a short period, allowing us to get a better price for our maize because most maize buyers want it very dry before they buy it.” [Farmers].

CHAPTER FIVE

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

5.1 Introduction

This chapter presents a summary of the research findings and gives the conclusions of the research findings. This chapter further gives recommendations based on the research findings and lastly, it suggests a further study.

5.2 Summary

The purpose of the study was to assess maize farmers’ usage of warehousing facilities in Ejura Sekyedumase municipality. Specifically, it sought to:

1. Determine the extent of usage of warehousing facilities in Ejura-Sekyedumase by maize farmers.
2. Determine the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipalities.
3. Assess farmer’s perception of the 1D1W policy.
4. Understand what farmers expect from the newly constructed warehouse to inform their decision to patronize the warehouse facilities under the 1D1W.

A descriptive survey design was used for the study and a total of 306 maize farmers were randomly chosen as the sample size. Maize farmers who participated in the study were randomly selected from the Municipal. Primary data was used for the study. Both quantitative and qualitative data analysis descriptive statistics were employed to achieve the objectives of the study. All analyses were carried out in SPSS software.

5.3 Conclusions

The following conclusions are made based on the study objectives and concerning the various findings:

1. Maize farming in the Municipal is male-dominated. The production of maize is undertaken by the less educated farmers and the youths (people with ages less than 40). Most of the farmers do not have access to financial support.
2. Majority of maize farmers in the Municipal do not store their products in a warehouse.
3. FBO participation, farmer category, age, level of education (years), level of warehouse importance and access financial support were the important determinants that influence farmers’ decision to use a warehouse.
5. Major reason why farmers are not using warehousing facilities in the municipal is a result of poor management services.

5.4 Recommendations

Based on the study findings, the following recommendations were made;

1. The local government authority should raise funds to pay off the private partner, own the facility, and decide on the management and pricing policies. These will help in the sustainability of the warehouses.
2. Also, the Ministry of Food and Agriculture should prioritize giving incentives such as fertilizer and weedicides to farmers who store their produce in modern warehouses. This is a fast way that can help farmers to adopt modern warehouses. This will boost yield also.
3. The government and IPEP under the Ministry of Special Development Initiatives should introduce dryers and warehouse receipts systems in the plans of warehouse constructions under the 1D1W policy as this will help farmers dry their maize efficiently and access credit for their production respectively.
4. The Ministry of Food and Agriculture and the Municipal Assembly should ensure that there will be proper management of all warehouses constructed and yet to be constructed.

5.5. Limitations of the study

This study, like research works, is not without limitations. The following are some of the study's limitations:

1. The other constraint stemmed from the Ejura killing episode, which hampered the administration and collecting of the questionnaire. Some of the respondents refused to fill out the questionnaire out of concern that we were police officers conducting an investigation into them. This prompted us to recruit a member of the study area to assist us with administration, putting a strain on our already stretched budget.

5.6 Suggestions for further study

According to the findings, similar research should be done in the future to see if maize growers in Ejura-Sekyedumase Municipality are using warehouses more effectively. The study also suggests that a similar study be conducted in different agricultural regions that produce other farm crops such as tomato, yam, and so on, to compare the extent of the use of warehousing facilities in different areas of Ghana and determine whether the factors influencing usage are similar or divergent in those areas.

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APPENDIX

QUESTIONNAIRE FOR ASSESSMENT OF THE USAGE OF WAREHOUSE FACILITIES BY MAIZE FARMERS IN EJURA-SEKYEDUMASE MUNICIPALITY.

The questionnaire is part of a study being conducted by a student of Kwame Nkrumah University of Science and Technology (KNUST) on the above topic. You are therefore respectfully required to complete the questionnaire by providing honest and objective responses. You are assured that responses will be treated with strict confidentiality.

BASIC INFORMATION

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Interview Section

Section A

PERSONAL DATA OF RESPONDENT

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Section B

Demographics- Warehousing customers

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For those who said Yes in Question 16

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SECTION C

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SECTION D

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THANK YOU FOR YOUR PARTICIPATION

[...]

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Details

Title: Assessment of Maize Farmers' Usage of Warehousing Facilities in Ejura -Sekyedumase Municipality
College: Kwame Nkrumah University of Science and Technology
Grade: 2.0
Author: David Addo (Author)
Publication Year: 2021
Pages: 75
Catalog Number: V1173908
ISBN (PDF): 9783346595089
ISBN (Book): 9783346595096
Language: English
Tags: assessment maize farmers usage warehousing facilities ejura municipality factors affecting the usage of warehouse facilities

Assessment of Maize Farmers' Usage of Warehousing Facilities in Ejura -Sekyedumase Municipality

Excerpt

TABLE OF CONTENTS

DECLARATION

DEDICATION

ACKNOWLEDGEMENT

ABSTRACT

LIST OF TABLES

TABLE OF FIGURES

CHAPTER ONE

1.0 INTRODUCTION

1.1 Chapter overview

1.2 Background

1.3 Problem statement

1.4 Research questions

1.5 Research objectives

1.6 Justification

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

2.2 History and approaches of warehousing

2.3 Nature and significance of warehousing

2.4 Purposes behind warehousing of maize

2.5 Warehousing Philosophy

2.6 Conceptual framework of adoption of agricultural new technologies

2.7 Factors affecting Adoption of Agricultural New Technologies in Ghana

2.7.1. Demographic factors

2.7.2. Socio-Economic factors

2.7.3. Institutional factors

2.8. Farmers' perceptions of new agricultural technology

CHAPTER THREE

METHODOLOGY

3.1 Introduction

3.2 Research Design

3.3 Study Population

3.4 Sample and Sampling Technique

3.5 Data Collection Instrument and Procedures

3.6 Data Analysis

3.7 Explanatory Variables in the Logit Regression Model

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 Introduction

4.2 Demographic characteristics of respondents

4.3 The extent of warehouse usage among maize farmers

4.4 To determine the factors that influence farmers’ decision to use warehousing facilities in the Ejura-Sekyedumase municipality

4.5 Farmer’s perception of the 1D1W policy

4.6 What do farmers expect from the newly constructed warehouse?

CHAPTER FIVE

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

5.1 Introduction

5.2 Summary

5.3 Conclusions

5.4 Recommendations

5.5. Limitations of the study

5.6 Suggestions for further study

REFERENCES

APPENDIX

Details