Soybean Production in Uganda. Determinants, Problems and Improvements

TABLE OF CONTENTS

DEDICATION

ACKNOWLEDGEMENT

LIST OF ACRONYMS

LIST OF TABLES

LIST OF FIGURES

ABSTRACT

CHAPTER ONE
INTRODUCTION
1.1 Introduction
1.2 Background to the study
1.3 Statement of the problem
1.4 Objective of the study
1.4.1 General objective
1.4.2 Specific objectives
1.5 Research hypothesis
1.6 Scope of the study
1.6.1 Geographical scope
1.6.2 Content scope
1.6.3 Time scope
1.7 Justification of the study
1.8 Significance of the study
1.8 Conceptual framework

CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
2.2 Growth and development of soybean
2.3 Fertilizer and soybean production
2.4 Farm size and soybean production
2.5 Labour units and soybean production

CHAPTER THREE
METHODOLOGY
3.1 Introduction
3.2 Area of study
3.3 Study population
3.4 Sample size and selection
3.5 Data collection instruments
3.5.1 Questionnaire
3.5.2 Interview
3.6 Research procedure
3.7 Data analysis
3.8 Specification of the model

CHAPTER FOUR
PRESENTATION AND INTERPRETATION OF FINDINGS
4.1 Introduction
4.2 Univariate analysis
4.3 Bivariate analysis

CHAPTER FIVE
CONCLUSION AND RECOMMENDATION
5.1 Introduction
5.2 Conclusion
5.3 Recommendation
5.4 Recommended areas for further research

REFERENCES

Survey questionnaire

DEDICATION

I dedicate this research report to my precious parents Mr. Oruro Patrick and Mrs. Milly Oruro, my brothers Obongo Ronald, Okabo Innocent and Omach Abel and my lovely sisters Acipa Robinah and Achieng Ritah.

ACKNOWLEDGEMENT

Above all, the almighty God receives the highest appreciation and acknowledgement for sparing my life and for providing me with sufficient energy, time and wisdom to write up this report.

I wish to deeply convey my special and sincere gratitude to my supervisor Miss. Atuhaire Ruth who tirelessly and patiently guided me in research work by critically reading through every bit of the report giving constructive pieces of advice and recommended it for submission.

I am also indebted to convey my thanks to my constructive friends Solomon, Daniel, Phionah, Lawrence, Daniel and Vanson for their contribution, advice as well as support through the encouragement during discussions, which has tremendously contributed to the completion of this course.

Special thanks to the Chief Administrative Officer – Nwoya district for granting me time and permission to carry out data collection. Special thanks go to all respondents for being flexible in giving me enough information. I also thank all the soybean farmers of Nwoya district, for their cooperation during my data collection.

LIST OF ACRONYMS

MT metric tons

MAAIF Ministry of Agriculture, Animal Industry and Fisheries

VODP Vegetable Oil Development Project

AGRA Alliance for a Green Revolution in Africa

RUFORUM Regional Universities Forum for Agricultural Development

UBOS Uganda Bureau of Statistics

SSA Sub-Saharan Africa

NARO National Agricultural Research Organization

STATA Statistics and Data

SPSS Statistical Package for Social Scientists

LIST OF TABLES

Table 4.2.1: Education level of respondent

Table 4.2.2: Age bracket of respondent

Table 4.2.3: Size of family labour

Table 4.2.4: Quantities of seeds used in soybean farming

Table 4.2.5: Farm size used in soybean farming

Table 4.2.6: Quantities of fertilizer used in soybean farming

Table 4.3.1: Farm size and soybean output

Table 4.3.2: Quantities of seeds and soybean output

Table 4.3.3: Relationship between family labour units and soybean output

Table 4.3.4: Quantities of fertilizer and soybean output

LIST OF FIGURES

Figure 4.2.1: Gender of respondent

Figure 4.2.2: Marital status of respondent

Figure 4.2.3: Education level of respondent

Figure 4.2.4: Age bracket of respondent

ABSTRACT

The purpose of the study was to investigate the determinants of soybean production with a view of recommending on how to improve the level of output of soybean in Nwoya district. The study was conducted in that particular area because many people over the years have picked interest in growing soybean but the best is yet to be realized from it relative to improvement in the household livelihood. Specifically, the study was intended to achieve the following objectives: to investigate whether farm size influences soybean output, to examine whether quantities of seeds influence soybean output, to establish the relationship between family labour units and soybean output and to find out whether quantities of fertilizer used influence soybean output. The study adopted cross-sectional survey research design. Quantitative and qualitative approaches of data collection were employed. A total of 80 respondents constituted the sample of the study equally spread throughout the eight sub counties. Two types of research instruments were used, that were questionnaires and interviews. The conclusions were that there was significant correlation between family labour units and soybean output, soybean output depends on farm size, soybean output depends on quantity of seeds and finally soybean output depends on quantities of fertilizer.

CHAPTER ONE

INTRODUCTION

1.1 Introduction

This study investigated the determinants of soybean production in Nwoya district. Determinants in this study were regarded as the independent variable whereas soybean output as the dependent variables.

This section introduced the background to the study, statement of the problem, general objective, specific objectives, research hypothesis, scope of the study, significance of the study, justification of the study and the conceptual framework.

1.2 Background to the study

Globally, soybean is an important crop, providing oil and protein. From 1980 to 2017, soybean production in the world increased from 80.9 million metric tons (MT) to 336.82 million MT, and the soybean consumption also reached 337 million MT. The increase in demand of a diet rich in animal protein is the product of an increasing standard of living resulting from long-term rapid economic development. Whether used as direct feed for livestock, poultry, and fish or for direct human consumption, there has been a corresponding increase in the demand of soybean. In short, the production and consumption sides of value chains have necessitated the demand for more soybeans. (Shiwei Liu, 2019)

There is rapid production and utilization of soybean. Global soybean production at the beginning of the 20th century was only about 22 million tons while this value escalated to more than ten times and now in the first decade of the 21st century it is about 223 million tons (Ali, 2010).

Global soybean production is assumed to rise from 2.4 million tons to 341.8 million, mainly on a 1 million-ton increase for both Argentina (54 million) and Brazil (to 126 million). Argentina’s larger crop is due to overall favorable conditions in higher yielding central and northwestern farming areas. Brazil is projected to be the largest producer of soybeans in the world followed by the United States & Argentina. The United States is projected to produce 96.84 million metric tons of soybeans, unchanged from last month. (Beef2live, 2020)

African producers generally supply less than 1% of global soybeans. Production of soybean has grown at a compound annual growth rate of 4.68% since 1961, while African production levels are rising 48% faster at a rate of 6.84% per year. Both world and Africa’s growth in production mostly result from an increase in soybean acres planted and not from yield. South Africa, Nigeria, and Zambia are the top three soybean producers on the African continent. South Africa’s 3-year (2015-2017) production average was 38.3 million bushels, which is 39% of the African continent’s production. Nigeria’s 3-year production average was 23.7 million bushels, which is 25% of the continent’s production. Zambia’s 3-year average was 10.4 million bushels, which is 11% of the continent’s production. The average national production is 4,026,969 bushels, and the smallest producer is Madagascar, whose three-year production average is 1,470 bushels. Annual production remains below 1,000,000 bushels for 15 of the 24 countries for which data exists. (Margaret Cornelius, 2019)

Soybean production in Uganda has been steadily growing. However, production had come to a standstill because of the outbreak of soybean leaf rust disease which was devastating soybean crops throughout the country in the past years. The Soybean Breeding and Seed Systems Program with support from MAAIF - VODP, AGRA and RUFORUM successfully bred, developed and released better yielding, early maturing and rust resistant soybean varieties between 2004 and 2013. The new varieties are Maksoy 1N and Namsoy 4M (2004), Maksoy 2N (2008), Maksoy 3N (2010), Maksoy 4N and Maksoy 5N (2013). These improved soybean varieties have been adopted for commercial production in Uganda, and have led to soybean yield increase of up to 2,000 - 3,000 kg per hectare, providing income and an affordable source of protein for the country’s rural population. (Tonny Obua, 2015)

Soybean is an important legume crop, with potential for expansion in Uganda and Africa at large. Soybean accounts for about 84.5% of the grain legumes trade in the world. Sub-Saharan Africa (SSA) accounts for about 1.3% of total land area under soybean and 0.6% of production in the world. In SSA the average yield has also remained very low at 1.1 t ha−1 in the past four decades; this is below the world average of 2.4 t ha−1. For instance, the average soybean yields in 2016/2017 in South Africa, Nigeria, Zambia, and Uganda was 2.29, 0.96, 1.94 and 0.6 t ha−1 respectively (Khojely, 2018).

Globally, two traits make soybean hugely popular; its high protein content (40%) and oil (20%), all derived from processed seed. In fact, soybean produces the highest amount of protein per unit area among crops. Indeed, In Uganda science-led interventions have resulted into steady increase of soybean production from an estimated 158,000 hectares in 2004 to 180,000 hectares in 2010, with corresponding annual production of 158,000 tons 181,000 tons (UBOS,2010).

Soybean oil contributes an important role in cooking, baking and food processing as well as in biodiesel production and other industrial applications. Soymeal the plant matter left over once the oil has been extracted is a reliable source of livestock feed throughout the world. Like other legumes, soybeans also function as nitrogen fixers, improving soil health by restoring the nitrogen lost during the cultivation of other major crops such as maize, cassava, potatoes. (Phinehas Tukamuhabwa, 2018)

1.3 Statement of the problem

Soybean production is significantly low in Nwoya district and Uganda at large given its enormous nutritional importance. The government and other research organizations for instance NARO, Makerere University Agricultural Research Institute and others have deliberately developed high yielding varieties such as Maksoy 1N and Maksoy 4N, provided extension services country wide, removed import tax on most agricultural inputs such as fertilizers and machinery like tractors. Despite the numerous efforts by the government, soybean production has remained low amidst large chunks of land, abundant labour force and favourable climatic conditions.

1.4 Objective of the study

1.4.1 General objective

The general objective of the study was to investigate the determinants of soybean production in Nwoya district .

1.4.2 Specific objectives

i To investigate whether farm size influences soybean output.
ii To examine whether quantities of seeds influence soybean output.
iii To establish the relationship between family labour units and soybean output.
iv To find out whether quantities of fertilizer used influence soybean output.

1.5 Research hypothesis

The hypothesis of the study included the following;

H01: Soybean output does not depend on farm size.
Ha1: Soybean output depends on farm size.
H02: Soybean output does not depend on quantities of seeds.
Ha2: Soybean output depends on quantities of seeds.
H03: There is no significant relationship between amount of family labour units and soybean output.
Ha3: There is a significant relationship between amount of family labour units and soybean output.
H04: Soybean output does not depend on quantities of fertilizers.
Ha4: Soybean output depends on quantities of fertilizers.

1.6 Scope of the study

The scope of the study was availed in three areas as follows;

1.6.1 Geographical scope

The research study was carried out in Nwoya district, Uganda. Nwoya district is a district in northern Uganda. Like most districts in Uganda, it is named after its main commercial center, Nwoya, the location of the district headquarters. Nwoya district is bordered by Amuru district to the north, Gulu district in the north-east, Oyam district in the east, Kiryandongo district to the south-east, Masindi district to the south-west. Nwoya, the main political, administrative and commercial center in the district is approximately 44 kilometers by road, south-west of the city of Gulu, the largest metropolitan is the sub region. This location is approximately 330 kilometers by road, north of the city of Kampala, Uganda’s capital and largest metropolitan area.

1.6.2 Content scope

The study investigated whether farm size influences soybean output, examine whether quantities of seeds influence soybean output, and establish the relationship between family labour units and soybean output as well as finding out whether quantities of fertilizers used influence soybean output in Nwoya district.

1.6.3 Time scope

The research was carried out for a period of three months. During this time, enough data about the study topic was obtained, analysed and presented as findings of the study.

1.7 Justification of the study

Low productivity of soybean has been shown to be common in some communities of Nwoya district. There is limited information on soybean and its determinants. This study therefore, determined the core determinants of soybean production in this community. This information allowed for intervention measures to be formulated and also formulation of a policy to address the problem. The study exposed other gaps that existed within the field and prompted more investigations by other scholars. Finally, the study contributed to the literature on the determinants of soybean production in Uganda and the world at large.

1.8 Significance of the study

The findings of this study and recommendations would be an eye opener to soybean farmers. The findings would enable the Ministry concerned to come out with proper policy strategies to overcome the problem. The research would also add new knowledge on the existing one on soybean. Therefore, it will be an important source of reference knowledge for scholars later on.

1.8 Conceptual framework

A conceptual framework relating soybean output and its determinants.

Abbildung in dieser Leseprobe nicht enthalten

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

This chapter presents the reviewed literature by other scholars and researchers in accordance to this study about determinants of soybean production.

2.2 Growth and development of soybean

Soybean’s yield formation commences with the planting of the seed in the ground and ends when the grain is harvested. Soybean yield potential depends not only on seed selection, but also on the soybean’s genetics and the environmental characteristics in which the plant was grown during development until harvest. Understanding how a soybean plant grows, develops, and produces grain is essential for understanding the factors that affect plant growth and maximize final yield production. The seed is a living organism that remains dormant until conditions for germination are achieved. The most important seed components are: testa (seed coat); cotyledons (first leaves to emerge); hypocotyl (first stem of the plant after germination, this tissue is between the cotyledons and radicle); epicotyl (section of the small plant that presents the stem); unifoliate leaves (the two primary leaves); and the apical meristem. mouth or nose after sneezing or coughing, employees with long hair must wear hats, hair nets or other Soybeans are planted in soil with an adequate moisture level and warm temperatures (mid-50 degrees Fahrenheit or higher) to allow rapid germination and emergence. Rapid germination occurs when soil temperatures are above 65 degrees Fahrenheit. Planting dates have shifted in the last 34 years to earlier dates at a rate of 0.5 day per year. The change in planting date may be attributable to changes in: genetics (e.g., improved germination and cold tolerance of modern soybean varieties); environmental conditions (e.g., warmer temperatures in spring); and management practices (e.g., tillage system, rotation, fertility, inoculation, and machinery). (Stewart, 2016)

Optimum planting depth is around 1 to 2 inches, depending on the soil temperature and moisture. For early planting dates, as placement depth increases, the emergence period is extended, primarily related to the soil temperature. For late planting dates, soil moisture plays a critical role in determining the time until emergence. Shallow planting (less than 1 inch) without adequate moisture is not recommended. Placing the seed into moist soil is the best strategy when planting late. Soil conditions will affect the time taken to achieve successful germination and emergence. Soil compaction or crusting produces a delay in soybean emergence, slowing the plant’s emergence and/or the root’s proliferation in the soil. Flooded soils have lowered oxygen levels, an obstacle for seed respiration and growth. During germination, a soybean absorbs an equivalent of 50 percent of its seed weight. As the soil temperature approaches an optimum for growth, the first organ to emerge from the seed is the radicle or primary root. (Stewart, 2016)

2.3Fertilizer and soybean production

Soybeans high yields are possible only when the crop meets its nutritional requirements. In many cases, mismanagement of nitrogen application prevents the soybeans grower from achieving the yield potential. Soybeans do not require high fertilizer application rates but an accurate nutritional plan is necessary for increasing yields. The crop classified as moderately sensitive to saline, with a salinity threshold of 2.0 dS/m. Soil pH of 6 – 6.8 are ideal. Banding fertilizers and foliar feeding are common application methods but should be considered only when conventional methods are not satisfying. Soybeans grains have a nitrogen content of 40%, therefore an adequate fertilization of nitrogen is a key factor in achieving high quality yields. (David, 2020)

A high yield of soybean requires a large amount of N, and soybean plants should continue to assimilate nitrogen during both vegetative and reproductive stages. Many field data showed that the total amount of N assimilated in a plant shoot is highly correlated with the soybean seed yield. The relationship between total amount of N in soybean shoot at R7 stage and seed yield is shown in a rotated paddy field in Niigata. A linear correlation (r = 0.855) between seed yield and the amount of nitrogen accumulation in the shoot was observed. Salvagiotti, 2008 reviewed the relationships among seed yield, nitrogen uptake, nitrogen fixation, and nitrogen fertilizer based on 637 published data sets. A mean increase of 13-kg soybean seed yield per kg N increase in aboveground part was obtained from these data, which is equivalent to the 77 kg N required for 1 ton of seed production.

2.4 Farm size and soybean production

Globally population is consistently increasing, and it is over 7 billion in 2012, while the land area for agricultural use is limited. Therefore, the increase in crop production per area is very important. Soybean (Glycine max (L.) Merr) originates from East Asia, and soybean seed is one of the most important protein sources for human and livestock all over the world. Annual production of soybean (262 M (million) t in 2010) is the fourth of the major grain crops, after maize (844 M t), paddy rice (672 M t) and wheat (650 M t). In the whole world, over 85% of soybean is used for oil and the residue is used for animal feed. Annual soybean seed production has been steadily increasing for recent decades (91 M t in 1980, 109 M t in 1990, 161 M t in 2000, 262 M t in 2010). The cultivation area of soybean is 102 M ha in 2010. Major soybean production countries (annual production in 2010) are USA (90.6 M t), Brazil (68.5 M t), Argentina (52.7 M t), China (15.1 M t), and India (9.8 M t) in this sequence. Soybean production in Japan in 2010 was only 223,000t and it accounted for 5% of the total consumption in Japan. The world average seed yield is 2.56 t ha-1 in 2010, and is higher in the USA (2.92 t ha-1), Brazil (2.94 t ha-1), and Argentina (2.90 t ha-1) compared with China (1.77 t ha-1), Japan (1.62 t ha-1) and India (1.07 t ha-1) and other countries. (Takuji Ohyama, 2020)

2.5 Labour units and soybean production

Technology is continuously improving the technical efficiency of agriculture. Advances in seeds, chemicals, machinery and other inputs are allowing farmers to produce more than it was before with low inputs. As quality labour becomes more expensive and difficult to obtain, producers will want to know how best to allocate their resources in order to obtain maximum labour efficiency. In a study conducted about seven years ago on soybean production, a model was created which showed how effective each factor is for reducing labour and whether the factor is more important for soybean. (John Anderson, 2007).

CHAPTER THREE

METHODOLOGY

3.1 Introduction

This chapter presents the area and population of the study, sample size and sample selection technique, data collection instruments, procedure of study and data analysis.

3.2 Area of study

The study was carried out in Nwoya district. This locality had eight sub counties, which were Alero sub county, Anaka sub county, Anaka Payra sub county, Got Apwoyo sub county, Koch-Goma sub county, Lii sub county, Lungulu sub county and Purongo sub county.

3.3 Study population

This study focused on determinants of soybean production in Nwoya district. The target population included both small and large-scale farmers of soybean regardless of their religions, beliefs, ethnicity and race. The target population was accessible and hence information was easily extracted from them.

3.4 Sample size and selection

A sample of 80 respondents was selected. This particular sample size was selected using stratified sampling technique. The eight sub counties formed eight strata from which 10 samples were randomly drawn from each using equal proportion whereby the sample size to be chosen was the same for all the strata. At each sub county, questionnaires were administered face-to-face to the heads of households. Respondents were asked about their location, gender, marital status, education level, age bracket, size of family labour, quantity of seeds used in soybean farming, appropriate land area under soybean production, quantities of fertilizers applied in soybean farming, amount of soybean output during harvest and possible solutions to low soybean output.

3.5 Data collection instruments

The researcher used both structured questionnaire and interviews to collect data for the study.

3.5.1 Questionnaire

The researcher used structured questionnaires to gather data on the determinants of soybean production in Nwoya district. Open ended and close ended questions were used. Close ended questions gave respondents options to avoid time wastage. Open ended question was used and gave a chance for respondents to explain in details, their views.

3.5.2 Interview

Interview was conducted with each individual respondent to elaborate more on the possible solutions to low soybean production.

3.6 Research procedure

The research took two days to respond and fill the questionnaires after which the collected data was analysed and interpreted as findings of the study.

3.7 Data analysis

Data collected was analysed using StataIC 12 and SPSS. The findings of the study were presented using contingency tables which contain frequencies of actual observations and percentages. Graphical presentation was used as well to make it simple and amazing.

3.8 Specification of the model

Regression models

Dummy variables were created to easily allow fitting the models that showed the relationship between a particular independent variable and the dependent variable. This was because the independent variables say; farm size, quantities of seeds, labour units and quantities of fertilizer had data created in groups that were qualitative in nature.

Dummy variables were created in STATA and one variable dropped and taken as “comparison group”.

Abbildung in dieser Leseprobe nicht enthalten

CHAPTER FOUR

PRESENTATION AND INTERPRETATION OF FINDINGS

4.1 Introduction

This chapter presents an analysis and interpretation of the findings from the study. The data collected was analyzed using Stata/IC-12.0 and SPSS.

4.2 Univariate analysis

The univariate analysis was conducted and the results displayed as shown below.

Figure4.2.1: Gender of respondent

Abbildung in dieser Leseprobe nicht enthalten

52.38% of the respondents were males whereas 47.62% of the respondents were females. Therefore, this shows that the majority of the respondents are male.

Figure4.2.2: Marital status of respondent

Abbildung in dieser Leseprobe nicht enthalten

52.68% of the respondents reported to be married, 29.46% of the respondents reported to be divorced and lastly 17.86% of the respondents reported to be single. Therefore, this shows that the majority of the respondents are married followed by divorced and finally single.

Figure 4.2.3: Education level of respondent

Abbildung in dieser Leseprobe nicht enthalten

Graph indicates that majority of respondents attained primary level of education followed by secondary level of education then followed by below primary level of education and lastly followed by tertiary level of education.

Figure 4.2.4: Age bracket of respondent

Abbildung in dieser Leseprobe nicht enthalten

Graph indicates that majority of respondents are aged between 41-50 years followed by above 50 years followed by 26-30 years then followed by 31-40 years and lastly followed by 18-25 years.

Table 4.2.1: Education level of respondent

Abbildung in dieser Leseprobe nicht enthalten

40% of the respondents reported their education level to be primary, 27% of the respondents reported their education level to be secondary, 27% of the respondents reported their education level to be below primary and lastly % of the respondents reported their education level to be tertiary. Therefore, this shows that the majority of the respondent’s attained primary level of education followed by secondary level of education followed by below primary level of education and finally tertiary level of education.

Table 4.2.2: Age bracket of respondent

Abbildung in dieser Leseprobe nicht enthalten

31.25% of the respondents are between the age bracket 41 and 50, 21.25 % of the respondents are above 50 years, 18.75% of the respondents are between the age bracket 26 and 30, 17.5% of the respondents are between the age bracket 31 and 40 and lastly 11.25% of the respondents are between the age bracket 18 and 25. Therefore, this shows that the majority of the respondents are between the age bracket of 41 and 50.

Table 4.2.3: Size of family labour

Abbildung in dieser Leseprobe nicht enthalten

Majority (28.75%) of the respondent’s size of family labour is between 1 and 2 people, 27.5% of the respondent’s size of family labour is between 3 and 5 people, 17.5% of the respondent’s size of family labour is above 10 people, 16.25% of the respondent’s size of family labour is between 9 and 10 people and finally 10% of the respondent’s size of family labour is between 6 and 8 people.

Table 4.2.4: Quantities of seeds used in soybean farming

Abbildung in dieser Leseprobe nicht enthalten

38.75% of the respondents use between 3 and 5 kg in soybean farming, 26.25% of the respondents use between 6 and 10 kg in soybean farming, 16.25% of the respondents use both between 11 and 50 kg and less than 2 kg in soybean farming and finally 2.5% of respondents use more than 50kg in soybean farming. Therefore, majority of respondents use between 3 and 5 kg in soybean farming.

Table 4.2.5: Farm size used in soybean farming

Abbildung in dieser Leseprobe nicht enthalten

43.75% of the respondents use less than 1 acre in soybean farming, 36.25% of the respondents use between 2 and 5 acres in soybean farming, 11.25% of the respondents use between 6 and 10 acres in soybean farming, 6.25% of the respondents use between 11 and 20 acres in soybean farming and finally 2.5% of respondents use more than 20 acres in soybean farming. Therefore, majority of respondents use less than 1 acre in soybean farming.

Table 4.2.6: Quantities of fertilizer used in soybean farming

Abbildung in dieser Leseprobe nicht enthalten

76.25% of the respondents do not use any fertilizer in soybean farming, 10% of the respondents use between 11 and 20 kg of fertilizer in soybean farming, 6.25% of the respondents use between 6 and 10kgof fertilizer in soybean farming, 3.75% of the respondents use between 1 and 5kg and above 20 kg of fertilizer in soybean farming. Therefore, majority of respondents hardly apply any fertilizer in soybean farming.

4.3 Bivariate analysis

Bivariate analysis was carried out to establish the relationship between the independent variable and the dependent as follows.

Table 4.3.1: Farm size and soybean output

H01: Soybean output does not depend on farm size.
Ha1: Soybean output depends on farm size.

Abbildung in dieser Leseprobe nicht enthalten

Model

Output = 187.4857 + 1262.169*farmsize_dum2 + 5999.625* farmsize_dum3 + 6127.114* farmsize_dum4 + 11248.51* farmsize_dum5.

If farmsize_dum2, farmsize_dum3, farmsize_dum4 and farmsize_dum5 = 0, the farmsize_dum1 would on average yield187.4857kg of soybean output.

Planting soybean on farmsize_dum2 would on average yield 1449.6547 kg of soybean output (187.4857+1262.169), that is when farmsize_dum3, farmsize_dum4 and farmsize_dum5 = 0.

Planting soybean on farmsize_dum3 would on average yield 6187.1107 kg of soybean output (187.4857+5999.625).

Planting soybean on farmsize_dum4 would on average yield 6314.5997 kg of soybean output (187.4857+6127.114).

Planting soybean on farmsize_dum5 would on average yield 11435.9957 kg of soybean output (187.4857+11248.51).

Where;

farmsize_dum1 is farm size less 1 acre, farmsize_dum2 is farm size 2-5 acres, farmsize_dum3 is farm size 6-10 acres, farmsize_dum4 is farm size 11-20 acres, farmsize_dum5 is farm size above 20 acres.

Interpretation of coefficients

The coefficient for farmsize_dum2 (dummy for farm size 2-5 acres) is positive implying that farming on farm size 2-5 acres would yield more soybean output than farming on farm size less 1 acre. But this is statistically insignificant at 5% level of significance since P-value (0.096) >0.05.

The coefficient for farmsize_dum3 (dummy for farm size 6-10 acres) is positive implying that farming on farm size 6-10 acres would yield more soybean output than farming on farm size less 1 acre. This is statistically significant at 5% level of significance since P-value (0.000) < 0.05.

The coefficient for farmsize_dum4 (dummy for farm size 11-20 acres) is positive implying that farming on farm size 11-20 acres would yield more soybean output than farming on farm size less 1 acre. This is statistically significant at 5% level of significance since P-value (0.000) < 0.05.

The coefficient for farmsize_dum5 (dummy for farm size above 20 acres) is positive implying that farming on farm size above 20 acres would yield more soybean output than farming on farm size less 1 acre. But this is statistically insignificant at 5% level of significance since P-value (0.711) >0.05.

Since the F-probability is statistically significant (0.000<0.05), the null hypothesis is rejected and conclude that soybean output depends on farm size.

Table 4.3.2: Quantities of seeds and soybean output

H02: Soybean output does not depend on quantities of seeds.
Ha2: Soybean output depends on quantities of seeds.

Abbildung in dieser Leseprobe nicht enthalten

Model

Output = 398.5385 + 703.8486*quantseeds_dum2 + 787.7473*quantseeds_dum3+ 5372.846* quantseeds_dum4 + 9319.462*quantseeds_dum5.

If quantseeds_dum2, quantseeds_dum3, quantseeds_dum4 and quantseeds_dum5 = 0, the quantseeds_dum1 would on average yield 398.5385 kg of soybean output.

Planting quantseeds_dum2 would on average yield 1102.3871 kg of soybean output (398.5385 +703.8486), that is when quantseeds_dum3, quantseeds_dum4 and quantseeds_dum5 = 0.

Planting quantseeds_dum3 would on average yield 1186.2858 kg of soybean output (398.5385 +787.7473).

Planting quantseeds_dum4 would on average yield 5771.3845 kg of soybean output (398.5385 +5372.846).

Planting quantseeds_dum5 would on average yield 11435.9957 kg of soybean output (398.5385 +9319.462).

Where;

quantseeds_dum1 is quantity of seeds less 2 kg, quantseeds_dum2 is quantity 3-5 kg, quantseeds_dum3 is quantity of seeds 6-10 kg, quantseeds_dum4 is quantity of seeds 11-50 kgand quantseeds_dum5 is quantity of seeds above 50kg.

Interpretation of coefficients

The coefficient for quantseeds_dum2 (dummy for quantity 3-5 kg) is positive implying that planting3-5 kg would yield more soybean output than planting less 2kg. But this is statistically insignificant at 5% level of significance since P-value (0.528) >0.05.

The coefficient for quantseeds_dum3 (dummy for quantity of seeds 6-10 kg) is positive implying that planting 6-10 kg would yield more soybean output than planting less 2kg. But this is statistically insignificant at 5% level of significance since P-value (0.508) > 0.05.

The coefficient for quantseeds_dum4 (dummy for quantity of seeds 11-50 kg) is positive implying that planting 11-50 kg would yield more soybean output than planting less 2kg.This is statistically significant at 5% level of significance since P-value (0.000) < 0.05.

The coefficient for quantseeds_dum5 (dummy for quantity of seeds above 50 kg) is positive implying that planting above 50 kg would yield more soybean output than planting less 2kg. This is statistically significant at 5% level of significance since P-value (0.000) < 0.05.

Since the F-probability is statistically significant (0.0000<0.05), the null hypothesis is rejected and conclude that soybean output depends on quantity of seeds.

Table 4.3.3: Relationship between family labour units and soybean output

Abbildung in dieser Leseprobe nicht enthalten

H03: There is no significant relationship between amount of family labour units and soybean output.

Ha3: There is a significant relationship between amount of family labour units and soybean output.

The correlation coefficient 0.2876 shows a strong positive correlation between family labour units and soybean output of a particular farmer. This means that as family labour units increase, output of soybean increases, the relationship is significant at 5% level of significance. Since the p-value (0.0097) <0.05, thus the null hypothesis is rejected and conclusion made that there is significant correlation between family labour units and soybean output.

Table 4.3.4: Quantities of fertilizer and soybean output

H02: Soybean output does not depend on quantities of fertilizer.

Ha2: Soybean output depends on quantities of fertilizer.

Abbildung in dieser Leseprobe nicht enthalten

Model

Output=1345.115-1228.781*quantfertilizer_dum2+2123.485*quantfertilizer_dum3+ 2223.76*quantfertilizer_dum4 + 8800.219*quantfertilizer_dum5

If quantfertilizer_dum2, quantfertilizer_dum3, quantfertilizer _dum4 and quantfertilizer _dum5= 0, the quantfertilizer_dum1 would on average yield 1345.115 kg of soybean output.

Plantingquantfertilizer_dum2 would on average yield 2573.896 kg of soybean output (1345.115+1228.781), that is when quantfertilizer_dum3, quantfertilizer_dum4and quantfertilizer_dum5 = 0.

Planting quantfertilizer_dum3 would on average yield 3468.6 kg of soybean output (1345.115+2123.485).

Planting quantfertilizer_dum4would on average yield 3568.875 kg of soybean output (1345.115+2223.76).

Planting quantfertilizer_dum5 would on average yield 10145.334 kg of soybean output (1345.115+8800.219).

Where;

quantfertilizer_dum1is quantity of fertilizer0 kg, quantfertilizer_dum2 is quantity 1-5 kg, quantfertilizer_dum3 is quantity of fertilizer 6-10 kg, quantfertilizer_dum4 is quantity of fertilizer 11-20 kg and quantfertilizer_dum5 is quantity of fertilizer above 20 kg.

Interpretation of coefficients

The coefficient for quantfertilizer_dum2 (dummy for quantity of fertilizer1-5 kg) is negative implying that planting 1-5 kg would yield less soybean output than planting 0 kg. This is statistically insignificant at 5% level of significance since P-value (0.562) >0.05.

The coefficient for quantfertilizer_dum3 (dummy for quantity of fertilizer1-5 kg) is positive implying that planting 6-10 kg would yield more soybean output than planting 0 kg. But this is statistically insignificant at 5% level of significance since P-value (0.205) >0.05.

The coefficient for quantfertilizer_dum4 (dummy for quantity of fertilizer1-5 kg) is positive implying that planting 11-20 kg would yield more soybean output than planting 0 kg. But this is statistically insignificant at 5% level of significance since P-value (0.102) < 0.05.

The coefficient for quantfertilizer_dum5(dummy for quantity of seeds above 20 kg) is positive implying that planting above 20 kg would yield more soybean output than planting less 0 kg. This is statistically significant at 5% level of significance since P-value (0.000) < 0.05.

Since the F-probability is statistically significant (0.0009<0.05), the null hypothesis is rejected and conclude that soybean output depends on quantities of fertilizer.

CHAPTER FIVE

CONCLUSION AND RECOMMENDATION

5.1 Introduction

This chapter presents the conclusions and recommendations made from the findings of the study.

The study was done purposely to investigate the determinants of soybean production in Nwoya district. It also attempts to propose various solutions to low soybean production.

5.2 Conclusion

From the study findings, it’s revealed that the majority of respondents who carryout soybean farming are males, most soybean farmers are married, most soybean farmers have at least attained primary level of education, majority of soybean farmers are between the age bracket 41-50 years as compared to other age groups, most family size involved in soybean farming is between 1 and 2, fewer farmers have a family size between 6 and 8 people, majority of farmers use between 3 and 5 kg of soybean, majority of farmers use less than an acre, fewer farmers plant soybean on larger pieces of land and most farmers hardly apply any fertilizer in soybean farming.

There is significant correlation between family labour units and soybean output.

Basing on the F-probability;

Soybean output depends on farm size.

Soybean output depends on quantity of seeds.

Soybean output depends on quantities of fertilizer.

On the issue of improving output of soybean. During the course of interviews, various measures were cited out and these include the following;

Starting with a clean field. Early season weed control is another essential component to high-yielding soybeans. Anyone wouldn’t want to lose any yields to weeds, so it’s important to start with a clean field.

Plant on time. Planting in early April also carries a yield-reduction risk. Also, for farmers with poorly draining soils, soybeans don’t like ‘wet feet,’ so tiling can be helpful to ensure early planting for consistently higher yields.

Choose the right varieties. Variety selection is the most important factor that will determine soybean yields.

Higher plant population. Planting from 140,000 to 175,000 seeds per acre is the right population range to maximize leaf area production and optimize yields.

5.3 Recommendation

There is need to educate and encourage more farmers to plant soybean on a large scale so as to produce more output. Efforts should also be done to expand labour beyond family labour size.

5.4 Recommended areas for further research

The results of this research revealed that there is significant correlation between family labour units and soybean output, soybean output depends on farm size, soybean output depends on quantity of seeds and soybean output depends on quantities of fertilizer. Therefore, more research should be done on the followings:

1. Relationship between family labour and soybean output.
2. Soybean output and farm size.

REFERENCES

Shiwei Liu, 2019.The Factors Affecting Farmers Soybean Planting Behavior.

Beef2live, 2020.World Soybean Production. Soybean production ranking by country

Margaret Cornelius, 2019.FarmdocDAILY: The state of soybean in Africa.

Tonny Obua, 2015. Soybean Production Guide in Uganda.

Khojely, 2018.History, current status and prospects of soybean production.

UBOS, 2010. Statistical Abstract.

Phinehas Tukamuhabwa, 2018.Growing new soybeans for Uganda.

Stewart, 2016.K-STATE research and extension, Soybean production.

David, 2020.Smart fertilizer, Soybean Nutrient Needs.

Takuji Ohyama, 2020.Soybean Seed Production.

Salvagiotti, 2008. Nitrogen Uptake, Fixation and Response to Fertilizer N in Soybean.

Ali, 2010.The importance of soybean production worldwide.

H.M.Murithi, 2015. Soyben production in Eastern Africa and threat of yield loss due to soybean rust.

Survey questionnaire

I wish to request your permission to participate in the study that will form part of my degree work. I assure you safety and confidentiality.

Please tick where appropriate.

Abbildung in dieser Leseprobe nicht enthalten

Thank you!

[...]