Soil Fertility Management Practices among Smallholder Cereals Producing Farmers in Dutsin-Ma Local Government Area of Katsina State, Nigeria

doi:10.21203/rs.3.rs-3599496/v1

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Soil Fertility Management Practices among Smallholder Cereals Producing Farmers in Dutsin-Ma Local Government Area of Katsina State, Nigeria

https://doi.org/10.21203/rs.3.rs-3599496/v1

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Scientific methodologies have been employed to assess soil fertility in Nigeria and other African nations. Nevertheless, there are discrepancies between the scientific procedures employed and the farmers' perceptions regarding soil fertility. This study examined the soil fertility management strategies employed by smallholder farmers engaged in grain production in the Dutsin-Ma Local Government area of Katsina State, Nigeria. The data was obtained by a random sampling method, consisting of 120 cereal producers. This dataset was then subjected to analysis using both descriptive statistics and a logit regression model. The results of the study indicate that a significant majority (91%) of farmers who are married possess land, with a substantial proportion (78.3%) acquiring it mostly through inheritance. The findings indicate that a significant proportion (77.5%) of farmers surveyed do not possess membership in cooperative groups, while a relatively small percentage (15.8%) have established contact with extension personnel. The findings additionally demonstrate that the mean age of farmers is 48 years, and the average farm size is 3 hectares. Furthermore, most of the farmers, approximately 52%, possess formal education. The findings indicate that a limited proportion (37.5%) of farmers possess knowledge regarding soil fertility, primarily relying on observations related to soil color and texture. The research region revealed that most farmers employed crop rotation, fallow farming, and manuring as management strategies to mitigate the reduction in soil fertility. The statistical analysis reveals that the level of education (p < 0.01), farming experience (p < 0.1), and Government source of input (p < 0.1) are important variables that have an impact on farmers' understanding of soil fertility within the research region. Farmers were identified as facing various challenges, including soil degradation, limited financial resources, drought and flooding, and the high cost of fertilizer for soil management. There is a necessity to offer novel approaches for educating farmers about soil fertility and its associated management techniques within the designated study region.

Fertility

Management

Soil

Cereals farmers

Smallholder

Practices

There are various types of soil in Nigeria, each one existing in its own unique ecosystem and possessing unique fertility status (Olorunfemi eta al, 2018). It is common knowledge that soils with a high fertility level also have a high concentration of nutrients (Hodges, 2010). As far as the nation is concerned, the requirement for food sufficiency has brought with it a commensurate need to harness the fertility of the soil as it has demonstrated the ability to be used throughout the entire year (Reijneveld et al, 2019). Due to crop cultivation year after year, soils are depleted of their fertility status, therefore for a succeeding crops to survive successfully in the same environment, depleted nutrients must be supplemented (Nesme, 2011). Fertile soils can provide enough nutrients to the crop throughout the growing season. As a result, higher yields per unit area are easier to achieve in fertile soils than in low-fertility soils (Van and Rabbinge, 1997).

The relationship between the growth and productivity of plants is contingent upon the level of soil fertility present within a certain geographical region (Waide et al, 1999). The variations in soil characteristics mostly arise from the dominant weather conditions, vegetation composition, and topographical features (Western et al, 2004). A multitude of endogenic factors, including soil pH, organic matter content, and texture, as well as primary, secondary, and micro nutrients, together with exogenic factors such as land use and farming systems, technological advancements, and input management practices (Mowo et al., 2006) these factors collectively contribute to the initiation and/or exacerbation of soil fertility lessening. Soil fertility indicators, including crop yields, soil depth, drainage, wetness, slow plant growth, leaf colouring, and other characteristics, are commonly employed by farmers in assessments of soil fertility (Nafi’u et al., 2012).

Despite the limited ability of smallholder farmers to manage most of these factors (Ogbena et al., 2016). The extensive expertise they have acquired in the cultivation of land, it is evident that farmers should assume a crucial role in the stabilization of agricultural land management. There also appears to be a potential prejudice among soil scientists and other experts towards the knowledge possessed by farmers regarding soil fertility. Nevertheless, it has been observed that small-scale farmers possess a considerable aptitude for discerning variations in fertility levels among different types of agricultural areas (Domchang et al., 2014). According to Nesme et al. (2011), there exists a potential prejudice among soil scientists and other experts towards the knowledge of soil fertility possessed by farmers. Nevertheless, there is a significant lack of knowledge regarding farmers' perspectives on, fertilization recommendations, soil function, soil health, and soil fertility in general (Abdulkadir and Nuhu, 2020).

Researches on the fertility of the soil had been conducted in Nigeria and other African countries using scientific methods for evaluating soil. These studies have produced a variety of findings. (Fanuel et al., 2017). However the scientific methods are not always consistent with how farmers perceived the fertility of the soil. Petrescu-Mag (2020) also assert that a comprehensive examination of soil functions necessitates the inclusion of farmers. It’s against this background that this study analyses the perception of cereals farmers on soil fertility management practices. This is imperative to comprehend the level of information possessed by farmers regarding soil quality and health to facilitate the effective dissemination of suitable technology for on-farm evaluations.

Description of the Study Area

Dutsin-Ma Local Government Area (LGA) is among the thirty-four (34) LGAs that constitute Katsina State. The geographical coordinates of the local government are situated at Latitude 12027ʹ18″ N and Longitude 70 29ʹ29″E. The Dutsin-Ma Local Government Area (LGA) encompasses an approximate land area of 552.323 square kilometers and was reported to have a population of 169,829 individuals according to the 2006 national census conducted by the Federal Republic of Nigeria (Federal Republic of Nigeria, 2012). The Local Government Area (LGA) in question is geographically adjacent to six other LGAs within Katsina State. These include Kurfi to the North, Charanci, Kankia, and Matazu to the East, Dan-Musa to the South, and Safana to the West, as documented by the National Population Commission in 2010. The Local Government Area (LGA) is situated inside the Sudan Savanna region of Katsina State. The predominant agricultural produce in the region encompasses a variety of crops, namely sesame, maize, millet, sorghum, cowpea, and groundnut. The types of livestock that are often raised in the area encompass cattle, sheep, goats, poultry, and fish. The Dutsin-Ma region is situated inside a tropical continental climate. The annual precipitation levels in this particular zone exhibit a range of 1000 to over 800mm in the vicinity of Dutsin-ma. This refers to a yearly temperature range of 29°C to 31°C. The climate exhibits significant variations in accordance with the months and seasons.

Sampling Procedure

A multistage sampling strategy was employed in the study. Out of the eleven wards in the Dutsin-Ma LGA, three were chosen at random in the first stage through balloting. The second stage involved selecting two villages or communities on purpose due to the intensification of cereal crop production in the chosen wards, and the third stage involved using the Yamane formula to determine the sample size of the farmers to reach the required number of respondents, as shown in Table 1. The requisite sample size was systematically sample randomly from the list of farmer’s population there by making 120 farmers for the study.

Table 1

Sampling Summary
LGA	Wards	Villages	Population of cereals farmers		Sample size
DUTSIN-MA	Dabawa	Tashar mai alewa	25		17
	Dabawa	Fasada	30		21
	Dutsin-ma A	Dantakiri	23		16
	Dutsin-ma A	Gobirawa	30		21
	Kutawa B	Kutawa	35		24
	Kutawa B	Rumawa	30		21
		TOTAL	173	120
Source: KTARDA, 2023

Method of Data Collection

This study acquired primary information through the administration of both closed and open ended structured questionnaire central to achieving already predetermined objectives by the researcher and trained enumerators that understand language of the respondents. The collected data contained information’s on the socioeconomics characteristics; perception of the farmers on soil fertility, attributes of soil fertility known by the farmers; soil management practices used by farmers and challenges faced by farmers on using soil management practices

Tools of Data Analysis

Descriptive statistics such as frequency and percentages and logit regression model were used in achieving objectives of the study.

Logit Regression Model

A logit regression model is a statistical analysis method that can be used to model the relationship between an ordinal response variable and one or more explanatory variables. The dependent variable in this case is a dummy variable carrying 0 for the farmers that are have no awareness on soil fertility and 1 for the farmers that have awareness on soil fertility.

\(P\left(Yi=1\right)=\) 1/ 1+e^{−β0+ βiXi}…………………….……………………………………. (i)

Where P (Yi = 1) is the probability that farmers have awareness on soil fertility and P(Xi = 0) is the probability that farmers are have no awareness on soil fertility. Z_i are the function of a vector of the independent variables. The model can therefore be expressed as;

1-P (Yi = 1) = 1-[1/(1 + e^− zi)] = 1/(1 + e^+ z)……………………………………………... (ii)

P(Yi = 1)/1- P(Yi = 1) = (1 + e^zi)/(1 + e^− zi) = e^z………………….…………..…………...(iii)

The explicit form of the model can be written as;

Y = F(X₁,X₂,X₃,X₄,X₅,X₆, X₇, X₈,X₉…X_n……….…………………………………..(iv)

The Explicit form can be written as

Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₄ + β₅X₅ + β₆X₆ + β₇X₇ + β₈X₈ + β₉X₉ + β₁₀X₁₀ + e_i

Y= (Awareness of farmers on soil fertility) (Yes = 1, No = 0, β0 = the intercept, e_i= the error term)

X₁ = Educational level (Primary = 1, Secondary = 2, Tertiary = 3)

X₂ = Age (Years)

X₃ = Major occupation (Trading = 1, Framing = 2, Tailoring = 3)

X₄ = Household size (numbers)

X₅ = Farming experience (years)

X₆ = Farm size (Hectares)

X₇ = Contact with extension agents (Yes = 1, No = 0)

X₈ = Land ownership (Borrowing = 1, Rented = 2, Inherited = 3)

X₉ = Source of inputs (Agro dealer = 1, GOV = 2, NGOS = 3)

X₁₀ = Cooperative membership (Yes = 1, No = 0)

Socioeconomic Characteristics of the Respondents

The results of the farmers socioeconomic characteristics was presented in Table 2a and 2b. The result show most of the respondents as married (91%) that own land majorly (78.3%) through inheritance. Majority (77.5%) of the farmers do not belong to cooperative groups and only few (15.8%) of them have contact with extension agents. The result further indicates that a substantial proportion (52%) of the farmers in the study area have formal education ranging from primary (21.7%), secondary (20.8%), and tertiary (9%) levels. The findings also revealed that farmers have some level of formal education, which may hinder their incorporation of new knowledge and technologies in their production activities. Salihu et al. (2023) obtain a similar result, where 19% of respondents are found to hold a Primary, 51.2% a secondary school, and 29.0% a tertiary degree. Result for major occupation of the farmers findings indicate that a respectable percentage of the respondents (49.2%) were farmers, while 44% were dealers and 3.3% were vegetable and fruit vendors. This suggests that the major occupation in the study area is farming. The result further show that most (81.7%) of the farmers source their production inputs through family and relatives.

Table 2

a: Qualitative Socioeconomic Characteristics of the Respondents
Variable	Frequency	Percentage (%)
Education
Primary	26	21.7
Secondary	25	20.8
Tertiary	11	9.2
Non formal education	58	48.3
Major occupation
Trading	44	36.7
Farming	59	49.2
Tailoring	13	10.8
vegetable and fruits sellers 4	4	3.3
Marital status
Married	110	91.7
Single	6	5
Divorced	3	2.5
Widowed	1	0.8
Form of land ownership
Borrowed	1	0.8
Rented	3	2.5
Inherited	94	78.3
Purchased	22	18.3
Source of inputs
Agro dealer	9	7.5
GOS	3	2.5
NGOS	10	8.3
Family	98	81.7
Cooperative membership
Member	27	22.5
None Member	98	77.5
Contact with extension agents
Contact	21	17.5
No Contact	99	82.5
Total	120	100

Result for quantitative socioeconomic characteristics was shown in Table 2b, the result revealed farmers to have average age of 48 years implying farmers to be in their active age of production. Similar to average of farmers found by Abdulkadir et al, (2020). The farmers were also found to have average household of 12 members implying the availability of family labor in the farming household. Moreover, the result shows that the majority of the respondents (91.7%) have 17–64 years of farming experience with the average of 35.36years production experience. Regarding Farm size of the farmers Table 2b shows the result for the respondents farm size indicated that the majority of the respondents (46.7%) have 3.5–4.4 hectares of land allocated for cereal crop production, with an average of 3.29ha allocated for cereal crop production in the study area. This is in line with the study carried out by Bwambale et al, (2015), who stated that variations in land ownership greatly influenced adoption of soil fertility management practices. Farmers with more land (above 3 acres) adopted most of the reported soil fertility management practices compared to farmers with three acres or less.

Table 2

b: Quantitative Socioeconomic Characteristics of the Respondents
Variables	Frequency	Percentage (%)
Age
22–31	13	10.8
32–41	24	20
42–51	32	27
52–61	28	23
62–71	23	19.2
Mean	48.21
Standard deviation	12.88
Household size
1–6	30	25
7–12	31	25.8
13–18	39	32.5
19–24	20	16.7
Mean	12.12
Standard deviation	5.85
Years of farming experience
5–16	10	8.3
17–28	33	27.5
29–40	29	24.2
41–52	25	20.8
53–64	23	19.2
Mean	35.36
Standard deviation	14.48
Farm size (hectares)
< 1-1.4	8	6.7
1.5–2.4	24	20
2.5–3.4	22	18.3
3.5–4.4	56	46.7
4.5–5.4	10	8.3
Mean	3.29
Standard deviation	1.11
Extension Visit
None	99	82.5
2–3	12	10
4–5	9	7.5
Mean	3.43
Standard deviation	1.03
Total	120	100

Farmers Awareness on Soil Fertility

Farmer’s awareness on soil fertility result was shown in Table 3. Findings revealed most (62.5%) of the respondents have no awareness of soil fertility while just few (37.5%) of them have awareness about soil fertility. This result indicates that majority of the respondent have no awareness on soil fertility. According Bwambale, (2015), and Pulido & Bocco (2014) farmers’ awareness of soil fertility degradation is the first step in influencing their decisions about improved soil fertility management practices. As such, farmers become motivated to seek alternative ways to avert current problems based various perceived constraints, including the characteristics of technologies available to them.

Table 3

Farmers Awareness on Soil Fertility
Variables	Frequency (f)	Percentage (%)
Yes	45	37.5
No	75	62.5
Total	120	100

Physical Attributes of Soil Fertility Known by the Farmers

The result finding of physical attributes of soil fertility known by the farmers presented in Table 4 reveals that a significant proportion of respondents 39.2% and 34.2% believe that changes in the color and texture of the soil respectively are the primary factors that can depict fertility of soil in their various farms, while 20.8% and 5.8% believe that water holding capacity and temperature respectively to be the major attribute that signifies soil fertility in their farms. Results implies that most of the farmers in the study area recognize their soil fertility by mere observation of color and texture of the soil. Burek et al. (2015) similarly found that farmers describe soils using physical attribute mostly by the color and water holding capacity

Table 4

Physical Attributes of Soil Fertility Known by the Farmers
Variables	Frequency	Percentage (%)
Texture	41	34.2
Color	47	39.2
Water holding capacity	25	20.8
Temperature	7	5.8
Total	120	100

Soil Management Practices by Farmers in the study Area

The distribution of respondents' beliefs regarding potential solutions to the issue of declining soil fertility presented in Table 5 revealed most respondents (27.5%) expressed the belief that implementing Fallow farming practices can effectively address the problem. Additionally, (16.5%) of respondents suggested that the application of manure can serve as viable solutions. Furthermore, 12.5% and 13.3% of respondents agreed that the utilization of chemical fertilizers and practicing crop rotation as important management practices they use to improve fertility of their soils, while 8.3% of the respondents believes that Tillage, Growing of cover crop and application of green manure can tackle the problem of fertility decrease. This implies that farmers hold the belief that implementing practices such as crop rotation, fallow farming, and the application of manure can effectively address the issue of declining soil fertility in the study area. According to Abdulkadir et al. (2020), their survey found that a majority of participants (68.4%) held the belief that fertility levels were not static, but rather subject to variation as shown by fertility indicators. Also similarly found that a significant majority of the respondents in theirs study (83.3%) expressed their belief that the issue of soil fertility could be effectively addressed by implementing technique of bush fallowing and regular application of manure.

Table 5

Soil Management Used by the Farmers in the Study Area
Variables	Frequency	Percentage	Rank
Application of fertilizer	15	12.5	4th
Application of manure	20	16.7	2nd
Practicing crop rotation	16	13.3	3rd
Practicing fallowing	33	27.5	1st
Tillage	10	8.3	5th
Intercropping	6	5	6th
Growing cover crops	10	8.3	5th
Green manure	10	8.3	5th
Total	120	100

Socioeconomics Factors Influencing Farmers Awareness on Soil Fertility

Socioeconomic factors influencing farmers, awareness on soil fertility result is presented in Table 6. A significant (p < 0.01) value of Log likelihood and Cox & snell R² show the overall fitness of the model. The result findings revealed level of education (p < 0.01), farming experience (p < 0.1), source of input (p < 0.1), and extension visit (p < 0.01) to be the statistical significant factors influencing farmers awareness on soil fertility in the study area.

The coefficient of level of education that is positive and significance, implies that farmers with higher level of education of farmers tend to have more awareness on soil fertility. This implies that the more educated farmers are the more the level of their awareness on soil fertility that will be. This may be due to the fact that educated farmers have more contact with extension agents. Educated people also tend to adopt new technologies more than less educated individuals.

The coefficient of farming experience that is negative and significant at 10% level of confidence implies that less experience farmers have more awareness about soil fertility this may be due to the fact that less experience are younger farmers that started production when most of the farms soil nutrient is depleted by the older farmers that have been cultivating it for years. Farmers with many years of farming experience may not be willing to change their farming behaviour, including the inputs they use (Mutuma, 2013). This may explain the negative awareness about soil fertility among older farmer

Source of input (Government) coefficient that is also positive and significant revealed that farmer that obtained production inputs from government have more awareness on soil fertility. This implies that government source of inputs give out the production inputs with package of extension information that go long way to creating awareness about soil fertility.

Table 6

Socioeconomics Factors Influencing Farmers Awareness on Soil Fertility
Variables	Coefficient	Standard error	P-Value
Education(Primary)	-0.385	0.866	0.656
Education(secondary)	0.493	0.816	0.546
Education(Tertiary)	1.839*	1.106	0.096
Age	0.083	0.061	0.175
Major occupation(trading)	-22.226	13649.700	0.999
Major occupation(farming)	-23.110	13649.700	0.999
Major occupation(tailoring)	22.291	13649.700	0.999
Household size	0.081	0.083	0.332
Farming experience	-0.086*	0.046	0.064
Farm size	0.076	0.322	0.815
Landownership(borrowed)	-18.275	40192.970	1.000
Landownership(rented)	1.770	1.501	0.238
Landownership(inherited)	-0.169	0.723	0.815
Source of input(agro dealer)	1.065	1.256	0.396
Source of input(GOV)	1.764*	0.914	0.053
Source of input(NGOS)	-44.680	23659.821	0.998
Cooperative membership	-0.683	0.738	0.355
-2 Log likelihood	95.557		0.000
Cox & Snell R Square	0.410		0.000
Nagelkerke R Square	0.558		0.000

Constraints Faced by the Farmers in Soil Management Practices

The findings constraints faced by the farmers in soil management practices was presented in Table 7. The result indicated that a significant proportion of the participants (100%) reported encountering difficulties related to insufficient financial resources and the high cost of fertilizer. Additionally, 39.2% of the respondents indicated challenges related to soil degradation, 22.5% reported difficulties with drought and flooding as major challenges affecting soil management practices in their farms.

Table 7

Constraints Faced By the Farmers in Soil Management Practices
Variable	Frequency	Percentage	Rank
Soil Degradation	120	100	1
Lack Of Financial Resources	120	100	2
Drought and Flooding	47	39.2	3
High Cost Of Fertilizer	27	22.5	4
Total	120	100

The findings of the study indicate that most farmers acquire their agricultural land through inheritance, and they do not actively participate in cooperative groups. Additionally, it was observed that only a small proportion of farmers have regular contact with extension agents. Furthermore, it is worth noting that a limited number of farmers possess knowledge on soil fertility, a factor that is influenced by their level of education, farming experience, and reliance on government-provided agricultural inputs. The farmers in the study area were observed employing agricultural techniques such as crop rotation, fallow farming, and manuring as means of mitigating the reduction in soil fertility. To address the limited awareness and enhance the management practices pertaining to soil fertility, it is imperative to introduce novel approaches for educating farmers in the designated region about soil fertility and its associated management practices.

The authors hereby give approval and consent to participate in the “Ethics approval and consent to participate” section of the Declarations.

All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors.

Authors secured informed consent from the village heads and individual that participated in the survey and we would like to acknowledge them for accepting our proposal to carry out the study and commend the effort of the village heads for their support.

Availability of data and material: the data used for the manuscript will be provided upon request.

Competing interests: there are no any competing interests with regard to the study.

Funding: No funding was obtained for this study.

Authors' contributions: Ummul-Kulsum Muhammad carried the field work, Buhari Nazifianalysed the data and supervised the field work and Mohammed Sabawrote the manuscript.

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No competing interests reported.

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Soil Fertility Management Practices among Smallholder Cereals Producing Farmers in Dutsin-Ma Local Government Area of Katsina State, Nigeria

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Abstract

INTRODUCTION

METHODOLOGY

Description of the Study Area

Sampling Procedure

Method of Data Collection

Tools of Data Analysis

Logit Regression Model

RESULTS AND DISCUSSION

Socioeconomic Characteristics of the Respondents

Farmers Awareness on Soil Fertility

Physical Attributes of Soil Fertility Known by the Farmers

Soil Management Practices by Farmers in the study Area

Socioeconomics Factors Influencing Farmers Awareness on Soil Fertility

Constraints Faced by the Farmers in Soil Management Practices

Conclusion and Recommendation

Declarations

References

Additional Declarations

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