Corona virus disease (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was first discovered in 2019 in Wuhan, the capital of Hubei, China and since then it has spread all over the world causing deaths of thousands people. This deadly virus has become nightmares for people worldwide and has been declared as pandemic on 11 March 2020 by the World Health Organization. Presently, there are over 7,000,930 confirmed cases, 402,578 deaths and 3,422,396 recovered cases of COVID-19 have been reported globally and this has been considered as the most dreadful situation that the world is ever facing after World War II. The COVID-19 virus is unique among human coronaviruses which has capacity of high transmissibility, substantial fatal deaths in some high risk age groups (children and older) and ability to cause huge societal, religious and economic disruption throughout the world. As COVID-19 is a newly identified pathogen, there is no known pre-existing immunity in humans. Based on the scenario of increasing COVID-19 confirmed cases and deaths throughout the world so far, everyone is seemed to be susceptible to this deadly virus.

The COVID-19 pandemic is a global crisis which is already affecting the agricultural sector. Farming households in Nigeria are apparently in a state of overwhelming fears concerning their mainstay with the recent clampdown as a result of the new coronavirus pandemic. This global crisis is envisaged to have more tangible effects on crop production in widespread States and Nigeria at large [1]. The country risks a looming food crisis unless measures are taken to protect the most vulnerable rural dwellers who constitute the farming majority, keep food supply chains alive and mitigate the pandemic’s effects across the food system. Thus, the ability of rural farmers to increase food production is pertinent in this pandemic and beyond.

Nigerian Government efforts at poverty reduction over the years have not yielded the desired results as expected. The Human Development Index Report for 2013 ranked Nigeria 153rd poorest out of 186 poorest countries in the world. Evidence in literature [2], shows clearly that poverty incidence in Nigeria is higher among the rural-folks, that is, households that rely mainly on agricultural income. The poverty situation in Nigeria is quite disturbing. Both the quantitative and qualitative measurements attest to the growing incidence and depth of poverty in the Country [3]. This situation however, presents a paradox considering the vast human and physical resources that the country is endowed with. It is even more disturbing that despite the huge human and material resources that have been devoted to poverty reduction by successive governments, actual evidence suggests that the depth and severity of poverty is still at its worst in Nigeria, Sub-Saharan African and Asia [4].

Apart from death due to starvation and other health hazards that the poor people are daily faced with, poverty induced hunger and malnutrition are known to impair Intelligence Quotient (IQ) development in children, leading to large loss in quality of life, and contributes to the declining productivity and poor economic growth in developing countries like Nigeria [5]. Evidences abound that among the rural poor, the farming households are poorer. It was for these and many other reasons that poverty reduction was made the first among the Millennium Development Goals (MDGs) adopted by world leaders in September 2000. One therefore begin to wonder what becomes of the rural farming households during this pandemic when businesses in urban areas are packing up. It is against this background that this study was designed to specifically: determine income lost due to Covid-19 pandemic and ascertain the poverty status of the farming households in the study area.      

The study was carried out in Kwara State, Nigeria. The State is located in the North Central part of the country, between latitude 8^o5^’ – 10^o 4^’ N and longitude 4^o55^’ – 6^o5^’ E, and covers an estimated land area of 36,825 square km with a population of about 2.37 million [6]. The State has 16 Local Government Areas (LGAs) and shares national boundaries with Niger, Oyo, Kogi, and Osun States and international boundaries with the Republic of Benin. Kwara State is within the rain forest and the woody savannah areas. The major occupation of the inhabitants is agricultural activities complemented by trade, artisanal, and traditional medicine.

Multi-stage sampling technique was used in data collection. Kwara State has 4 zones in Kwara State ADP namely: Zone A has Kaiama as the Zonal Headquarters, Zone B has Pategi as the Zonal Headquarters, Zone C has Malete and Zone D has Igbaja as Zonal Headquarters respectively. The first stage involves a random selection of zone C and D. In the second stage, two Local Government Areas were randomly selected from each of the two selected zones (Zone C: Asa and Moro; Zone D: Irepodun and Offa) while the third stage involves a random selection of two communities each from the two selected Local Government Areas, making a total of 8 communities. The communities were Ogbondoroko and Aboto from Asa LGA; Bode-saadu and Shao from Moro LGA; Ijan-otun and Esie from Irepodun LGA; and Alabere and Ajelanwa from Offa LGA. In the fourth stage, 40 respondents were selected from each community using systematic random sampling technique. Thus, a total of Three Hundred and Twenty (320) respondents were selected for the study. However, only Two Hundred and Eighty-nine (289) interview schedules signifying 90.3% response rate were retrieved and used for the study.

Data were collected using semi-structured interview schedule and analyzed using descriptive statistics such as means, frequency, standard deviation, and Foster-Greer and Thorbecke (FGT) poverty index. The respondents were disaggregated into poor and non-poor categories. It has become customary to use the so-called Pα measures in analyzing poverty. The measures relate to different dimensions of the incidence of poverty P₀, P₁, and P₂ were used for head count (incidence), depth and severity of poverty respectively. Regression analysis was used to determine the effect of covid-19 pandemic on income and productivity of farming households. In the regression analysis, the total income of the respondents represents the dependent variable

(Y) i.e.

• YA                   :               Total annual income of respondents in Naira

• AGE                :               Age in years

• HHS                :               Household size

• FMS                :               Farm size in hectares

• TDA                :               Total days of incapacitation due to covid-19 pandemic

• FDE                :               Food expenditure in Naira

• NFE                :               Non-food expenditure in Naira

• YT                   :               Total income lost due to covid-19 in Naira

The implicit function is thus stated as:

• YA   :               f (AGE, HHS, FMS, TDA, FDE, NFE, YT)

The different functional forms fitted were Semi-log, double log, exponential and linear, and eventually, the best functional form was chosen on the basis of number of significant variables, signs of the coefficients, value of coefficient of determination (R2 or R-2), F-value or economic reasoning and expectation.

Socio-economic Characteristics of Farming Households

Table 1 presents the socio-economic characteristics of the respondents which revealed that the average age of the respondents was 44.7 years. Most of the respondents are within the age range of 31-50 years and accounted for 71.6% indicating that they were still in their active and productive age, and so, have high energy to carry out agricultural activities. The distribution of respondents by household size presented in Table 1 shows that majority of the respondents have family sizes ranging from 1-10 which accounted for 68.9%, and only 31.1% of the respondents had over 10 persons per household. From the analysis, household size in the study area is fairly large with an average of 7 members.

Distribution of respondents by farming experience as depicted in Table 1 shows that most of the respondents had 11-20 years of farming experience accounting for 36.0%, with an average of 16 years of experience. The results further revealed that 35.3% of the respondents had farm sizes ranging between 1 - 5 hectares. This implies that most of the respondents are subsistence-oriented farmers. In terms of gender, Table 1 reveals that an overwhelming majority of the household heads were male representing about 70.0%. This is an indication that the males dominated agricultural activities. This agrees with the findings of [7] who revealed that the males dominated the work force in Nigeria’s agricultural communities.

In terms of level of education, Table 1 reveals that 15.9% of the household heads had no formal education, 24.5% had primary education, 29.7% had secondary education with a significant proportion (24.5%) having tertiary education in the study area. It can be seen that the literacy level of farm households in the study area was relatively high. This result contradicts the findings of [8] who pointed out that there is a low level of education among the rural farming households and this has implications for their income-earning capacity as the respondents’ may lack the required skill to secure well paid jobs. Majority (61.9%) of the respondents are married with only 12.5% being single. The marital status of famers has a direct implication on the household size and subsequently on the availability of family labour to assist on the farm [9].

Table 1: Distribution of Respondents’ Socio-Economic Characteristics (N = 289)

Source: Field Survey, 2020

Table 2: Linear Regression Functional Form Showing the Effect of Covid-19 on Farm Income

YA = - 24367.3 – 392.46 – 2722.32 (16992.72)                      (319.22)    (1243.485) +11023.31FM 92.531TDA+1.402FDE+0.443NFE+5.296YT (2208.49)*(670.442)                        (0.284)*     (0.148)*     (0.491) * R2 = 0.795 R2 = 0.802 * - Statistically significant at 1% level **- Statistically significant at 5% level

Regression Analysis of Effects of Covid-19 on Agricultural Productivity

For the regression analysis, linear functional form gave the best fit and was chosen as the best functional form that explains the causal relationship between productivity proxy (Farm income) and covid-19 incidence, also proxy by the days of incapacitation. Based on the consideration of statistical and economic criteria the results are presented below Table 2:

From the implicit function given above, the R squared value is 0.795, showing that 79.5% of the change that occurred in the dependent variable can be explained by the explanatory variables. This also shows that the model produces a good fit for the data.

Farm size, food expenditure, non-food expenditure and total income lost due to covid-19 pandemic are statistically significant at 1% while household size is statistically significant at 5%. However, age and days of incapacitation are not statistically significant at 10% in explaining the variation in the annual income realized from the farm which is a proxy used to measure productivity of farmers.

However, the negative beta coefficient of age implies that farmers’ income decreases with increase in age. This is expected because productivity of farmers decreases as they approach old age because of loss of agility and strength. The negative beta coefficient of household size is also expected as increase in households’ size increases expenditure and this decreases farmers’ annual income. Annual income also decreases with increase in days of incapacitation because the more the number of days, the greater the loss incurred and the lesser the annual income.

Furthermore, farm size has a positive beta coefficients indicating that annual income increases with increase in acreage of land cultivated. The positive beta coefficients of food and non-food expenditure show increase in expenditure as income of farmers’ increases. This is expected because the greater the income of the farmers, the more they tend to spend on food and non-food items. Also, total income lost due to covid-19 pandemic also has a positive beta coefficient, which means that as income increases; income lost due to covid-19 also increases. This is true because high income earning farmers tend to lose more of their income since farming activities were disrupted as a result of the lockdown, and also because of income lost during the period of incapacitation which tends to be more compared to the low income earning farmers.

Table 3: Poverty Status of Farm Household Heads

Source: Field Survey, 2020

Poverty Status of Farm Household Heads

The farm households’ poverty status in the state were analyzed using the three indicators- prevalence of poverty (Po), poverty depth (P₁) and severity of poverty (P₂). Prevalence of poverty indicate the percentage of the households falling below the poverty line; poverty depth shows the amount by which the poor fall short of the poverty line and severity of poverty is the sum of the square of poverty depth divided by the number of poor households in the sample. As shown in Table 3, the prevalence of poverty among the farm households in the state was (0.6810) representing 68 percent of the farm households with consumption expenditure level below the poverty line. The poverty depth was 0.61 representing 61 percent whose average consumption expenditure was below the poverty line. This gap represents the percentage of 

Conclusion and Recommendations

Covid-19 is a serious pandemic that is both a health and economic problem eating deeply into the financial base of rural farm households. Due to the lockdown that lasted about 90 days recently, families with large household experience low income, which in turn increases their poverty status. Furthermore, increase in covid-19 incidence increases days of incapacitation, this in turn increases the total income that is lost due to Covid-19, and finally there is a significant reduction in the productivity and also the income of the farmers. Health risks and poverty thus become a vicious cycle in the rural areas. Therefore, it is recommended that provision should be made by the government in ensuring that despite the lockdown and restrictions in movement, farm households are able to go about their farming activities unhindered. Also, effort should be made at helping the rural farmers reduce the incidence, depth and severity of poverty through conditional cash transfer programmes targeted towards rural farmers, as well as distribution of necessary farm inputs at highly subsidized rate to enable rural farmers benefit maximally from agricultural activities.

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6. National Population Commission (NPC). Report on 2006 population census Kwara State, Nigeria. 2007, pp. 44–62.

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