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Revamping agricultural sector and its implications on output and employment generation: Evidence from Nigeria

  • Abiola John Asaleye EMAIL logo , Henry Inegbedion , Adedoyin Isola Lawal , Oluwayemisi Kadijat Adeleke , Uche Abamba Osakede EMAIL logo and Elizabeth Bolatito Ogunwole
Published/Copyright: January 7, 2023
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Open Agriculture
From the journal Open Agriculture Volume 8 Issue 1

Abstract

The Nigerian government has implemented a comprehensive spectrum of policies and programmes to diversify the economy and encourage broad-based growth through investment in the agricultural sector. However, the steady increase in the poverty and unemployment rate has raised controversial issues among scholars. In light of this, the study investigates the impact of selected macroeconomic variables on Nigeria’s agricultural performance using two models for output and employment. The Error Correction Model (ECM) approach was used to establish the short and long-run behaviours. In the first model, output in the agricultural sector was used as the independent variable, while in the second model, employment in the agricultural sector was used as the independent variable. The study’s findings showed that output positively relates to credit to the agricultural sector and exchange rate. However, it was depicted that output and employment in the agricultural sector in both the short-run and the long-run are not statistically significant. The implication drawn from the study is that credit granted to the agricultural sector can foster aggregate output in the sector, which will promote long-term employment. The study suggests considerable investment in the agricultural sector and the need to strengthen institutions for proper management of resources to ensure effective evaluation of funds disbursed for improving the agricultural sector, among others.

Keywords: agricultural sector; financing; employment; production
JEL Classification: Q10; G20; E24; E20

1 Introduction

The agricultural sector has been recognised worldwide to improve aggregate employment and welfare [1]. Likewise, the United Nations Sustainable Development Goals (SDG) emphasise the importance of the agricultural sector in improving social well-being. SDG No. 1, No Poverty stressed the need to reduce poverty by improvement in agricultural production and SDG Goal No. 2, pointed out that investment in agriculture is crucial to increasing productivity and supporting the sustainable food systems necessary to alleviate hunger worldwide. Likewise, the agricultural sector has been envisaged as a means of living, especially for developing economies to achieve SDG No. 4 and finally SDG Goal No. 12, which can be achieved by enhancing sustainable production in the agricultural sector.

Over time, the Nigerian government has introduced a wide spectrum of policies and programmes to diversify the economy and promote inclusive growth through investment in the agricultural sector. However, a steady increase in the poverty rate and employment has raised questions about the efforts [2]. A few of the programmes include the National poverty eradication programme, Operation Feed the Nation, and green revolution. Before the oil boom era in the 1970s, the agricultural sector served as the major revenue source in Nigeria. The low contribution of agricultural productivity to GDP has led to a decline in the welfare of low-income earners in Nigeria. Likewise, the recent reduction in the international market’s oil price has raised a question about economic sustainability. The agriculture sector in Nigeria in the 1960s contributed about 80% of receipts on exports, 65% of GDP and about 50% of the government revenue. This contribution to Nigerian economic growth has taken a downward turn over the years. The contribution of agriculture to GDP was about 26.84% in 2021 [3]. Although in recent times, agriculture’s contribution to the output and employment has declined [4].

The level of unemployment rate in Africa compared to other European countries, North and South America showed a higher rate in Africa [5]. According to the official statistics as of 2020, the unemployment rate is 6.01, 5.66, 9.3, and 8.40% in Belgium, Denmark, Honduras and Jamaica respectively; whilst in Nigeria and Rwanda is 33 and 16.5%, respectively [4]. The manufacturing sector contributed significantly to employment generation in most developed countries; this is due to the abundance of capital relative to labour. The manufacturing sector for employment creation may not solve the unemployment problems in developing economies, especially Nigeria, with the rapid population increase. In this regard, agriculture has been presumed as one of the sectors to promote employment generation and income. Despite all the attempts made by the Nigerian government to revamp the sector, the country has not been able to meet the internal demand and has to import a considerable amount of food products. Growth in the activities and exploration of wider markets in the agricultural sector is required to promote productivity, which will increase the value-adding capacity of agriculture for economic production [6].

Consequently, in most developing countries like Nigeria, a large percentage of the population reside in rural areas and is mainly poor with more than 40% overall classified as poor despite the annual average economic growth of about 3% [7,8]. Nigeria depends on the oil sector as the major revenue source. The Nigerian government has emphasised the diversification of the economy and promoted employment through the agricultural and manufacturing sectors. Nigeria has 90.4 million hectares of land, and more than the average of the population lives in a rural environment where agriculture serves as a means to meet their needs. The agriculture sector’s importance to promote employment and income generation in rural areas cannot be undervalued.

There are various debates among scholars on the inability of the agricultural sector to generate income, promote employment and reduce poverty. Likewise, growth-output literature has received more attention with divergent views, especially for developing economies [9]. The divergent views among scholars may be attributed to a result of the relationship between output and employment has not been examined in time frame perspectives, examining the long and short-run and long-run effects. Also, most of the previous studies have focused on the impact of agricultural subsidies, emissions, poverty, and rainfall, among others [10,11,12]. Examining the impact of the agricultural sector to promote employment has not been fully ascertained in the literature [13,14].

Theoretically, the classical theory of unemployment stressed that employment and output are influenced by the demand for labour and the production function’s labour supply. There is a direct relationship between the total number of labour available and other input factors, such as technology and capital stock; this implies that production’s total output is a function of capital, technology, and labour. Labour demand is also dependent on the marginal productivity of labour. In addition, the success of the agriculture sector depends on factors that affect the inputs and environment [15]. Similarly, the Keynesian theory emphasised that aggregate demand changes, whether anticipated or unanticipated, have their greatest short-run effect on real output and employment, not on prices; this implies that effects or impacts in the short-run differ from impacts in the long-run. From the theoretical perspective, it can be depicted that employment has a positive relationship with output, and these effects are subject to a period perspective.

Empirically, most of the studies focused on agricultural subsidies, emissions in the agricultural sector, poverty, and population growth with studies relating to agricultural employment and output in time frame perspectives are still growing [10,11,12,16,17,18,19,20,21]. Using a dynamic computable general equilibrium model [11] documented that agricultural policies on investment and subsidies positively influence economic development, women’s employment, poverty, and inequality. However, it was stressed by ref. [11] that investment subsidies in agricultural industries have a greater long-term impact on eliminating gender disparity and poverty. In a similar study by ref. [10], it was reported that total subsidies have a positive effect on agricultural wages and employment in Hungary and family labour in Slovenia. More so, ref. [22] revealed that policies implemented to promote the non-farm sector resulted in a positive effect on the rural area’s agricultural sector. Ref. [13] reported that a negative rainfall shock has an adverse effect on agricultural productivity and household consumption. Other studies in Nigeria focused on non-economic factors, and crop production with scanty studies on the effect of agricultural performance [6,9].

The theoretical outlooks have shown that the impact of output on employment is due to the time frame. This study contributes to the existing literature by investigating the effect of selected macroeconomic variables on agricultural employment and output in Nigeria; this is necessary given the diverse efforts by the Nigerian government to revamp the agricultural sector. The study uses the Engle-granger model to examine the long-run and short-run effects.

The rest of the study is structured as; Section 2 discusses the method that explains the theoretical framework and empirical model specification. Section 3 presents the result, while Section 4 discusses the result. Section 5 is the conclusion.

2 Material and method

2.1 Framework of the study

This study uses the growth model, given as

(1) Q = f ( K , L ) .

In equation (1), Q is the output, where the factor inputs are labour (L), and capital (K). This model is based on the assumption: that the production function shows constant returns to scale such that it differs from the original classical thought of scarce land or any resources. Also, there is the flexibility to substitute labour for capital and vice, which implies that any amount of capital can be efficiently used with the right amount of labour. Therefore, this assumption explains that capital–output ratio can take a non-negative value and the function exhibits a quasi-concave relationship. The factors of production grow at constant rates; marginal labour and capital are given as follows:

(2) L 1 t = n L t ,

(3) K 1 t = s K f ( K , L ) .

In equation (3), s K is the saving rate, which is exogenous parameter. The marginal productivity of the inputs is given as:

(4) V t = d Q t / d L t ,

(5) R t = d Q t / d K A t .

In equations (4) and (5), “V” and “R” are the marginal productivity of the input’s labour and capital. Equation (1) describes an output function using two inputs, and the condition of production fulfils the Inada assumption. That is, the elasticity of substitution must have asymptotically resulted in unity.

(6) Y ( t ) = K ( t ) α A ( t ) L ( t ) 1 − α ,

where t denotes time, Y ( t ) represents total production, 0 < α < 1 is the elasticity of output with respect to capital, and A is labour-supplementing technology in order words AL is effective labour.

2.2 Model specification and techniques of estimation

We used two alternative variables to proxy agricultural performance measures as the dependent variable, that is, agricultural output and employment. These metrics for agricultural performance are usually used in the existing literature [8,23]. Furthermore, ref. [24] states that under specific conditions, flexible exchange rates initially improve output and employment while decreasing them afterwards. Also, ref. [25] report agricultural credit enhances production in West African countries. Other macroeconomic variables selected in this study include interest rate and consumer price index. Reference [26] pointed out interest rate is important in structural models when examining productivity growth. Likewise, ref. [27] report that global demand shocks induce a positive co-movement between foreign production and inflation, which are the principal drivers of consumer price inflation. As a result, the observed exchange rate pass-through after these shocks is the opposite sign of what is often predicted. Finally, in many other structural macroeconomic models, external shocks to the exchange rate are found to be less relevant for exchange rate volatility. In the context of Nigeria, with high demand for foreign goods, the consumer price index is introduced into the model. Therefore equation (6) is modified and adjusted slightly to achieve the objective of this study, specified as follows:

(7) AGDP = f ( AEMP , ACAP , INT , EXC , CPI ) .

In equation (7), AGDP represents output in the agricultural sector, AEMP is the employment in the agricultural sector, INT is the interest rate, ACAP is a credit to the agricultural sector, and EXCR is the exchange rate, and CPI is Consumer Price Index. The preliminary test was carried out to check for stationarity properties of the data using the Augmented Dickey–Fuller and Philips Peron tests. Afterwards, the Engle–Granger approach was used to determine the short-run dynamics and the long-run relationship using agricultural output and employment in the agricultural sector as the dependent variables to establish output and employment equations. The approach to cointegration by ref. [28] involves a two-step procedure. Given the long-run equation as:

(8) y t = δ 0 + δ 1 x 1 + μ t .

In equation (8), y t is the dependent variable while x 1 is the independent variable(s). The symbols δ 0 and δ 1 are the intercept and the slope, respectively. The residual in equation (8) is obtained through the least square estimator, this measures the disequilibrium in the model, and it is given as:

(9) μ ˆ t = y t − δ ˆ 0 − δ ˆ 1 x t .

The next stage is to ascertain if the residual obtained is stationary. This study employed the Augmented Dickey–Fuller and used ref. [29] critical value to determine the residual stationarity. In the presence of residual stationarity in the model, the least square estimator can be used to explain the long-run behaviour between agricultural output and agricultural employment in Nigeria. The short-run dynamics with the error correction term (ECM) can be tested given the equation as follows:

(10) Δ y t = θ 0 + ∑ j = 1 θ i Δ t − j + ∑ h = 0 θ h Δ X t − h + α μ ˆ t − 1 + ε t

Equation (10) is estimated using the least square method. The error correction term is the equation given by α , which must be negative, less than one and statistically significant to validate cointegration in the models. Equation (10) is used to examine the short-run dynamics. This study uses both agricultural employment and output as dependent variables to achieve the objective of the study. The models are further tested to ensure it is correctly specified. For a model to be specified correctly, the residual must not be serially correlated; the variance must be constant over time. The residuals must be normally distributed. Finally, it must be stable [7].

2.3 Information about the data and source

This study uses annual data set from the period 1981–2018. The series is acquired from the Central Bank of Nigeria (CBN), National Rolling Plan and the Nigerian National Bureau of Statistics (NBS). Credit given to the agricultural sector (ACAP), (EXC), Consumer price index (CPI), Interest rate (INT) and Agricultural contribution to GDP (AGDP) is obtained from CBN statistical bulletin various years while Agricultural employment was compiled from National Rolling Plan, NBS and NMB.

3 Result

3.1 Presentation of the unit root test

This section shows the various results of the tests carried out. Foremost, the Augmented Dickey–fuller and Phillip Peron are presented.

Table 1 presents a summary of the stationarity result. All series have stochastic behaviour in the ordinary form. However, series at their first difference form is stationary. The outcome of the ADF is similar to the PP. Based on the conclusion of the stationarity result, the article proceeds to estimate the long-run relationship. Two models are estimated for the long-run and short-run behaviours referred to as model 1 and model 2 for using output and employment as dependent variables.

Table 1

Summary of the unit test

Series Augmented Dickey–Fuller Order of integration Phillips Peron Order of integration
Level First Diff.
AGDP −2.205918 −6.324824** I(1) 2.805787 −6.813738** I(I)
ACAP −2.379439 −10.62356* I(1) −1.926354 −7.111038* I(I)
EXC 1.320067 −3.644593** I(1) 1.501720 −3.925510** I(I)
CPI −2.402132 −6.032624** I(1) −2.168542 −6.057202** I(I)
INT −2.915904 −6.708129** I(1) −2.826061 −6.350561** I(I)
AEMP −1.138906 −4.336505** I(1) −1.202051 −4.377034** I(I)

Source: Authors’ computation using Eviews 10.

  1. * and ** show significance at levels of 1 and 5%, respectively.

3.2 Presentation of the long-run and short-run behaviours

3.2.1 Presentation of model 1 (using output as the dependent variable)

Table 2 shows the short-run dynamics and long-run relationship using output in the agricultural sector as the dependent variable. In the long-run, credit to the agricultural sector (ACAP), exchange rate (EXC) and consumer price index (CPI) are statistically significant at the level of 1%. However, interest rate (INT) and employment in the agricultural sector (AEMP) are not statistically significant. There is a positive relationship between the dependent variable (AGDP) and the independent variables ACAP and EXC, likewise, a negative relationship with CPI.

Table 2

Long-run and short-run dynamics for model 1

Variable Coefficient Standard error t-Statistics Probability
Long-run behaviour
 Dependent variable: AGDP
  C 0.196261 0.052665 −3.726603 0.0009
  ACAP 0.085620 0.026717 3.204689 0.0039
  EXC 0.022409 0.004566 4.907784 0.0000
  CPI −0.072881 0.033165 −2.197536 0.0367
  INT −0.005183 0.060727 −0.085357 0.9326
  AEMP 0.028623 0.046063 0.621381 0.5390
 Short-run Dynamics
  C −0.187435 1.183623 −0.158357 0.8753
  D(ACAP) 0.034459 0.029645 1.162401 0.2549
  D(EXC) 0.014895 0.001826 8.157119 0.0000
  D(CPI) −0.068051 0.026834 −2.535973 0.0173
  D(INT) 0.006508 0.002906 2.239310 0.0351
  D(AEMP) 8.338201 37.31686 0.223443 0.8248
  U(−1) −0.497305 0.165191 −3.010483 0.0055
Durbin Watson: 1.796245 F-Stat: 2.469277 Prob. 0.054789
R-Squared: 8.363215 Adjusted R-squared: 7.173483

Source: Authors’ Computation using Eviews 10.

In the short, run, EXC is statistically significant at the 1% level while CPI and INT are statistically significant at 5%. ACAP and AEMP are not statistically significant. INT and EXC have a positive relationship with the independent variable (AGDP) while CPI has a negative relationship. The positive relationship between output in the agricultural sector and interest rate contradicts the “a prior” expectation. Theoretically, an increase in the interest rate will lower investment spending, which, on the other hand, will cause a fall in aggregate output. The error correction term (ECM) measures the speed of adjustment to equilibrium. The ECM value is −0.497305 with a probability value of 0.0055, indicating about 50% adjustment annually to the initial equilibrium.

Table 3 presents the diagnostic check and the residual unit root results for model 1. The results indicate that the residual of the model is stationary at the level of 5%, which validates the presence of long-run behaviour. The Diagnostic check shows no serial correlation among the residuals, the residual has constant variance over time, and the residuals are normally distributed.

Table 3

Residual unit root result and diagnostic check

Variables t-Statistics Probability Order of integration
Residual unit root result
 Residual (U) −3.277366 0.0238 I(0)
Diagnostic check
Breusch–Godfrey serial correlation LM test
Obs. × R-Squared 2.055169 Prob. Chi-square 0.3579
Heteroskedasticity test: ARCH
Obs. × R-Squared 0.201223 Prob. Chi-Square 0.9043
Histogram normality test
Jargue–Bera 7.741965 Prob. 0.070838

Source: Authors’ computation.

3.2.2 Presentation of model 2 (using employment as the dependent variable)

Table 4 presents the long-run and short-run dynamics using employment in the agricultural sector as the dependent variable. In the long-run, ACAP and CPI are statistically significant at 1% while EXC is significant at 10%. Both INT and AGDP are not statistically significant. In the short run, EXC and AGDP are statistically significant at the level of 1%. INT is significant at the level of 5%. Both ACAP and CPI are not statistically significant. The error correction term (ECM) has a value of −0.602171 and a probability value of 0.0017; this shows that the model will adjust at an average of 60% annually to the initial equilibrium. In the long run, the independent variable (AEMP) has a positive relationship with credit to the agricultural sector (ACAP) and a negative relationship with exchange rate (EXC) and consumer price index (CPI).

Table 4

Long-run and short-run dynamics for model 2

Variable Coefficient Standard Error t-Statistics Probability
Long-run behaviour
 Dependent variable: AEMP
  C 1.529830 0.662779 2.308208 0.0226
  ACAP 0.080741 0.028427 2.840252 0.0052
  EXC −0.000375 0.000194 −1.929197 0.0651
  CPI −0.077940 0.027389 −2.845671 0.0087
  INT 0.013401 0.058801 0.227911 0.8214
  AGDP 0.008210 0.028749 0.285582 0.7778
Short-run dynamics
  C 0.009955 0.022467 0.443101 0.6618
  D(ACAP) −0.000130 0.000146 −0.891535 0.3811
  D(EXC) −0.056978 0.020263 −2.811964 0.0057
  D(CPI) −0.000190 0.000296 −0.641270 0.5277
  D(INT) −0.047014 0.023176 −2.028569 0.0533
  D(AGDP) 0.082433 0.027948 2.949572 0.0038
  U(−1) −0.602171 0.169188 −3.559178 0.0017
Durbin Watson: 1.947436 F-Stat: 7.503101 Prob. 0.009251
R-Squared: 0.891215 Adjusted R-squared: 0.783462

Source: Authors’ computation using Eviews 10.

Table 5 indicates the diagnostic check and the residual unit root results for model 2. The results indicate that the residual of the model is stationary at the level of 5% given the value of t-statistics as −4.425331 and a probability value of less than 5%, which validates the presence of long-run behaviour. The Diagnostic check shows no serial correlation among the residual; the residual has constant variance over time and is normally distributed. Figures A1 and A2 in the appendix section present the stability graph of the two models. It can be deduced from the figures that the two models are stable for forecast; the CUSUM line falls in between the lower boundary and the upper boundary.

Table 5

Residual unit root result and diagnostic check

Variables t-Statistics Probability Order of integration
Residual unit root result
 Residual (U) −4.425331 0.0014 I(0)
Diagnostic check
Breusch–Godfrey serial correlation LM test
Obs. × R-Squared 3.217415 Prob. Chi-Square 0.2001
Heteroskedasticity test: ARCH
Obs. × R-Squared 6.649455 Prob. Chi-Square 0.4663
Histogram normality test
Jargue–Bera 0.312461 Prob. 0.855362

Source: Authors’ computation.

4 Discussion

This study examines the relationship between agricultural performance and selected macroeconomic variables. Two models were estimated using the Engel and Granger approach to cointegration. The agricultural performance was proxy by output in the agricultural sector and employment in the agricultural sector. In the first model referred to as Model 1, output in the agricultural sector was used as the dependent variable. The result indicates that credit to the agricultural sector, exchange rate, and consumer price index is statistically significant in the long run, while interest rate and employment in the agricultural sector are not statistically significant. The relationship between output in the agricultural sector and credit to the agricultural sector is in line with the theory proposition; this finding is in line with the studies of refs [8,23]. The relationship between output in the agricultural sector and the exchange rate can be either negative or positive. According to the Marshall–Lerner Condition, the exchange rate’s implication on the economy can be either positive or negative. However, trade improvement can only guarantee when the absolute sum of the long-term export and import demand elasticities is greater than one [30].

In addition, the findings from this study show agricultural output and employment are not significant both in the short and in long run. These findings contradict previous studies [11,10,15,6]. The insignificant relationship between output and employment both in the short-run and in long-run despite Nigerian government's attempts through different policies and programmes to revamp the agricultural sector confirmed the decline in the employment rate in the agricultural sector. The result’s outcome is in line with ref. [31]. Many factors may be attributed to this, including mismanagement of resources, low-capacity utilisation in the agricultural sector and wrong perspective about the nature of the agricultural sector’s job compared to other sectors.

Evidence from these findings shows that credit granted to the agricultural sector can foster aggregate output in the sector. So, the study encourages the need for proper management of funds given to the agricultural sector. In the long-term, this may help to shift dependence on the oil sector as the main revenue source. The result also confirms that the exchange rate policy favours production in the agricultural sector in the long run. However, the exchange rate may not be considered a policy instrument because it is determined in the foreign exchange market. The real exchange rate tends to determine the shift in demand either for foreign or local goods. Nevertheless, to maximise the agricultural sector’s benefit due to its comparative advantage, more priority needs to be given to the agricultural sector. The study shows a negative relationship between output and consumer price index. The consumer price index is restricted to the household’s spending; the negative relationship indicates that tightening the monetary policy has adversely affected the agricultural sector’s output. Besides, the exchange rate, consumer price index, and interest rate are statistically significant in the short run. In the second model, credit to the agricultural sector, exchange rate and Consumer price index are statistically significant.

5 Conclusion

The Nigerian government has undertaken a wide range of policies and programs to diversify the economy and stimulate broad-based growth through agricultural investment. However, the persistent rise in poverty and unemployment has led to debates among scholars and policy-makers. Empirical studies on the output-employment nexus have documented diverse results, most especially in developing economies. The divergent views among scholars may be attributed to the relationship between output and employment has not been explored in time frame perspectives, examining the long- and short-run effects. Furthermore, most recent research has concentrated on the effects of agricultural subsidies, pollution, poverty, rainfall, and inequality. In light of this, we use two models for output and employment equations to proxy agricultural performance. And investigate the effect of selected macroeconomic variables on Nigeria’s agricultural performance. The Error Correction Model (ECM) technique was utilised to examine the short and long-run behaviours. Prior to the estimation, the stationarity analysis was carried out using Augmented Dickey Fuller (ADF) and Phillips Perron (PP). All series have stochastic behaviour in the ordinary form. However, series at their first difference form is stationary. The outcome of the ADF is similar to the PP. Based on the conclusion of the stationarity result, the article proceeds to estimate the long-run relationship.

With regard to cointegration results, based on the stationarity of the residual of the two models, this validates the presence of long-run behaviour. In other words, there is a long-run relationship between agricultural sector performance and the selected macroeconomic variables. In the output equation, credit to the agricultural sector, exchange rate, and consumer price are statistically significant in the long run, while interest rate and employment in the agricultural sector are not statistically significant. In the short run, exchange rate, consumer price index, and interest rate are statistically significant while employment is not significant. In the employment equation, exchange rate and consumer price index are statistically significant in the long run while the exchange rate, interest rate and output to the agricultural sector are statistically significant in the short run.

Evidence from these findings shows that credit granted to the agricultural sector can foster aggregate output in the sector. So, the study encourages the need for proper management of funds given to the agricultural sector. In the long-term, this may help to shift dependence on the oil sector as the main revenue source. The result also confirms that the exchange rate policy favours production in the agricultural sector in the long run. However, the exchange rate may not be considered a policy instrument because it is determined in the foreign exchange market. The real exchange rate tends to determine the shift in demand either for foreign or for local goods. Nevertheless, to maximise the agricultural sector’s benefit due to its comparative advantage, more priority needs to be given to the agricultural sector. In addition, the result indicates that tightening the monetary policy has adversely affected the agricultural sector’s output. More attention needs to be given to the monetary measure to promote growth in the agricultural sector as one of the macroeconomic objectives. Along with this, the Nigerian government needs to properly manage resources and effective evaluation of funds disbursed to improve the agricultural sector. Finally, sector-specific incentives such as subsidies, credit facilities for innovation and proper risk management should be introduced to promote long-term growth and employment concerning the agricultural sector.

  1. Funding information: The authors state no funding is involved.

  2. Conflict of interest: The authors state no conflict of interest.

  3. Data availability statement: All data generated or analysed during this study are included in this published article

Appendix

Figure A1 Stability test for model 1.
Figure A1

Stability test for model 1.

Figure A2 Stability test for model 2.
Figure A2

Stability test for model 2.

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Received: 2021-01-31
Revised: 2022-09-10
Accepted: 2022-09-10
Published Online: 2023-01-07

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

  1. Regular Articles
  2. The impact of COVID-19 pandemic on business risks and potato commercial model
  3. Effects of potato (Solanum tuberosum L.)–Mucuna pruriens intercropping pattern on the agronomic performances of potato and the soil physicochemical properties of the western highlands of Cameroon
  4. Machine learning-based prediction of total phenolic and flavonoid in horticultural products
  5. Revamping agricultural sector and its implications on output and employment generation: Evidence from Nigeria
  6. Does product certification matter? A review of mechanism to influence customer loyalty in the poultry feed industry
  7. Farmer regeneration and knowledge co-creation in the sustainability of coconut agribusiness in Gorontalo, Indonesia
  8. Lablab purpureus: Analysis of landraces cultivation and distribution, farming systems, and some climatic trends in production areas in Tanzania
  9. The effects of carrot (Daucus carota L.) waste juice on the performances of native chicken in North Sulawesi, Indonesia
  10. Properties of potassium dihydrogen phosphate and its effects on plants and soil
  11. Factors influencing the role and performance of independent agricultural extension workers in supporting agricultural extension
  12. The fate of probiotic species applied in intensive grow-out ponds in rearing water and intestinal tracts of white shrimp, Litopenaeus vannamei
  13. Yield stability and agronomic performances of provitamin A maize (Zea mays L.) genotypes in South-East of DR Congo
  14. Diallel analysis of length and shape of rice using Hayman and Griffing method
  15. Physicochemical and microbiological characteristics of various stem bark extracts of Hopea beccariana Burck potential as natural preservatives of coconut sap
  16. Correlation between descriptive and group type traits in the system of cow’s linear classification of Ukrainian Brown dairy breed
  17. Meta-analysis of the influence of the substitution of maize with cassava on performance indices of broiler chickens
  18. Bacteriocin-like inhibitory substance (BLIS) produced by Enterococcus faecium MA115 and its potential use as a seafood biopreservative
  19. Meta-analysis of the benefits of dietary Saccharomyces cerevisiae intervention on milk yield and component characteristics in lactating small ruminants
  20. Growth promotion potential of Bacillus spp. isolates on two tomato (Solanum lycopersicum L.) varieties in the West region of Cameroon
  21. Prioritizing IoT adoption strategies in millennial farming: An analytical network process approach
  22. Soil fertility and pomelo yield influenced by soil conservation practices
  23. Soil macrofauna under laying hens’ grazed fields in two different agroecosystems in Portugal
  24. Factors affecting household carbohydrate food consumption in Central Java: Before and during the COVID-19 pandemic
  25. Properties of paper coated with Prunus serotina (Ehrh.) extract formulation
  26. Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
  27. Molecular and phenotypic markers for pyramiding multiple traits in rice
  28. Natural product nanofibers derived from Trichoderma hamatum K01 to control citrus anthracnose caused by Colletotrichum gloeosporioides
  29. Role of actors in promoting sustainable peatland management in Kubu Raya Regency, West Kalimantan, Indonesia
  30. Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
  31. Optimization of extraction using surface response methodology and quantification of cannabinoids in female inflorescences of marijuana (Cannabis sativa L.) at three altitudinal floors of Peru
  32. Production factors, technical, and economic efficiency of soybean (Glycine max L. Merr.) farming in Indonesia
  33. Economic performance of smallholder soya bean production in Kwara State, Nigeria
  34. Indonesian rice farmers’ perceptions of different sources of information and their effect on farmer capability
  35. Feed preference, body condition scoring, and growth performance of Dohne Merino ram fed varying levels of fossil shell flour
  36. Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia
  37. Analysis of trade potential and factors influencing chili export in Indonesia
  38. Grade-C kenaf fiber (poor quality) as an alternative material for textile crafts
  39. Technical efficiency changes of rice farming in the favorable irrigated areas of Indonesia
  40. Palm oil cluster resilience to enhance indigenous welfare by innovative ability to address land conflicts: Evidence of disaster hierarchy
  41. Factors determining cassava farmers’ accessibility to loan sources: Evidence from Lampung, Indonesia
  42. Tailoring business models for small-medium food enterprises in Eastern Africa can drive the commercialization and utilization of vitamin A rich orange-fleshed sweet potato puree
  43. Revitalizing sub-optimal drylands: Exploring the role of biofertilizers
  44. Effects of salt stress on growth of Quercus ilex L. seedlings
  45. Design and fabrication of a fish feed mixing cum pelleting machine for small-medium scale aquaculture industry
  46. Indicators of swamp buffalo business sustainability using partial least squares structural equation modelling
  47. Effect of arbuscular mycorrhizal fungi on early growth, root colonization, and chlorophyll content of North Maluku nutmeg cultivars
  48. How intergenerational farmers negotiate their identity in the era of Agriculture 4.0: A multiple-case study in Indonesia
  49. Responses of broiler chickens to incremental levels of water deprivation: Growth performance, carcass characteristics, and relative organ weights
  50. The improvement of horticultural villages sustainability in Central Java Province, Indonesia
  51. Effect of short-term grazing exclusion on herbage species composition, dry matter productivity, and chemical composition of subtropical grasslands
  52. Analysis of beef market integration between consumer and producer regions in Indonesia
  53. Analysing the sustainability of swamp buffalo (Bubalus bubalis carabauesis) farming as a protein source and germplasm
  54. Toxicity of Calophyllum soulattri, Piper aduncum, Sesamum indicum and their potential mixture for control Spodoptera frugiperda
  55. Consumption profile of organic fruits and vegetables by a Portuguese consumer’s sample
  56. Phenotypic characterisation of indigenous chicken in the central zone of Tanzania
  57. Diversity and structure of bacterial communities in saline and non-saline rice fields in Cilacap Regency, Indonesia
  58. Isolation and screening of lactic acid bacteria producing anti-Edwardsiella from the gastrointestinal tract of wild catfish (Clarias gariepinus) for probiotic candidates
  59. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia
  60. Design of smart farming communication and web interface using MQTT and Node.js
  61. Assessment of bread wheat (Triticum aestivum L.) seed quality accessed through different seed sources in northwest Ethiopia
  62. Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan
  63. Agronomic performance, seed chemical composition, and bioactive components of selected Indonesian soybean genotypes (Glycine max [L.] Merr.)
  64. The role of halal requirements, health-environmental factors, and domestic interest in food miles of apple fruit
  65. Subsidized fertilizer management in the rice production centers of South Sulawesi, Indonesia: Bridging the gap between policy and practice
  66. Factors affecting consumers’ loyalty and purchase decisions on honey products: An emerging market perspective
  67. Inclusive rice seed business: Performance and sustainability
  68. Design guidelines for sustainable utilization of agricultural appropriate technology: Enhancing human factors and user experience
  69. Effect of integrate water shortage and soil conditioners on water productivity, growth, and yield of Red Globe grapevines grown in sandy soil
  70. Synergic effect of Arbuscular mycorrhizal fungi and potassium fertilizer improves biomass-related characteristics of cocoa seedlings to enhance their drought resilience and field survival
  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
https://doi.org/10.1515/opag-2022-0140
Keywords for this article
agricultural sector; financing; employment; production
Creative Commons
BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. The impact of COVID-19 pandemic on business risks and potato commercial model
  3. Effects of potato (Solanum tuberosum L.)–Mucuna pruriens intercropping pattern on the agronomic performances of potato and the soil physicochemical properties of the western highlands of Cameroon
  4. Machine learning-based prediction of total phenolic and flavonoid in horticultural products
  5. Revamping agricultural sector and its implications on output and employment generation: Evidence from Nigeria
  6. Does product certification matter? A review of mechanism to influence customer loyalty in the poultry feed industry
  7. Farmer regeneration and knowledge co-creation in the sustainability of coconut agribusiness in Gorontalo, Indonesia
  8. Lablab purpureus: Analysis of landraces cultivation and distribution, farming systems, and some climatic trends in production areas in Tanzania
  9. The effects of carrot (Daucus carota L.) waste juice on the performances of native chicken in North Sulawesi, Indonesia
  10. Properties of potassium dihydrogen phosphate and its effects on plants and soil
  11. Factors influencing the role and performance of independent agricultural extension workers in supporting agricultural extension
  12. The fate of probiotic species applied in intensive grow-out ponds in rearing water and intestinal tracts of white shrimp, Litopenaeus vannamei
  13. Yield stability and agronomic performances of provitamin A maize (Zea mays L.) genotypes in South-East of DR Congo
  14. Diallel analysis of length and shape of rice using Hayman and Griffing method
  15. Physicochemical and microbiological characteristics of various stem bark extracts of Hopea beccariana Burck potential as natural preservatives of coconut sap
  16. Correlation between descriptive and group type traits in the system of cow’s linear classification of Ukrainian Brown dairy breed
  17. Meta-analysis of the influence of the substitution of maize with cassava on performance indices of broiler chickens
  18. Bacteriocin-like inhibitory substance (BLIS) produced by Enterococcus faecium MA115 and its potential use as a seafood biopreservative
  19. Meta-analysis of the benefits of dietary Saccharomyces cerevisiae intervention on milk yield and component characteristics in lactating small ruminants
  20. Growth promotion potential of Bacillus spp. isolates on two tomato (Solanum lycopersicum L.) varieties in the West region of Cameroon
  21. Prioritizing IoT adoption strategies in millennial farming: An analytical network process approach
  22. Soil fertility and pomelo yield influenced by soil conservation practices
  23. Soil macrofauna under laying hens’ grazed fields in two different agroecosystems in Portugal
  24. Factors affecting household carbohydrate food consumption in Central Java: Before and during the COVID-19 pandemic
  25. Properties of paper coated with Prunus serotina (Ehrh.) extract formulation
  26. Fertiliser cost prediction in European Union farms: Machine-learning approaches through artificial neural networks
  27. Molecular and phenotypic markers for pyramiding multiple traits in rice
  28. Natural product nanofibers derived from Trichoderma hamatum K01 to control citrus anthracnose caused by Colletotrichum gloeosporioides
  29. Role of actors in promoting sustainable peatland management in Kubu Raya Regency, West Kalimantan, Indonesia
  30. Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
  31. Optimization of extraction using surface response methodology and quantification of cannabinoids in female inflorescences of marijuana (Cannabis sativa L.) at three altitudinal floors of Peru
  32. Production factors, technical, and economic efficiency of soybean (Glycine max L. Merr.) farming in Indonesia
  33. Economic performance of smallholder soya bean production in Kwara State, Nigeria
  34. Indonesian rice farmers’ perceptions of different sources of information and their effect on farmer capability
  35. Feed preference, body condition scoring, and growth performance of Dohne Merino ram fed varying levels of fossil shell flour
  36. Assessing the determinant factors of risk strategy adoption to mitigate various risks: An experience from smallholder rubber farmers in West Kalimantan Province, Indonesia
  37. Analysis of trade potential and factors influencing chili export in Indonesia
  38. Grade-C kenaf fiber (poor quality) as an alternative material for textile crafts
  39. Technical efficiency changes of rice farming in the favorable irrigated areas of Indonesia
  40. Palm oil cluster resilience to enhance indigenous welfare by innovative ability to address land conflicts: Evidence of disaster hierarchy
  41. Factors determining cassava farmers’ accessibility to loan sources: Evidence from Lampung, Indonesia
  42. Tailoring business models for small-medium food enterprises in Eastern Africa can drive the commercialization and utilization of vitamin A rich orange-fleshed sweet potato puree
  43. Revitalizing sub-optimal drylands: Exploring the role of biofertilizers
  44. Effects of salt stress on growth of Quercus ilex L. seedlings
  45. Design and fabrication of a fish feed mixing cum pelleting machine for small-medium scale aquaculture industry
  46. Indicators of swamp buffalo business sustainability using partial least squares structural equation modelling
  47. Effect of arbuscular mycorrhizal fungi on early growth, root colonization, and chlorophyll content of North Maluku nutmeg cultivars
  48. How intergenerational farmers negotiate their identity in the era of Agriculture 4.0: A multiple-case study in Indonesia
  49. Responses of broiler chickens to incremental levels of water deprivation: Growth performance, carcass characteristics, and relative organ weights
  50. The improvement of horticultural villages sustainability in Central Java Province, Indonesia
  51. Effect of short-term grazing exclusion on herbage species composition, dry matter productivity, and chemical composition of subtropical grasslands
  52. Analysis of beef market integration between consumer and producer regions in Indonesia
  53. Analysing the sustainability of swamp buffalo (Bubalus bubalis carabauesis) farming as a protein source and germplasm
  54. Toxicity of Calophyllum soulattri, Piper aduncum, Sesamum indicum and their potential mixture for control Spodoptera frugiperda
  55. Consumption profile of organic fruits and vegetables by a Portuguese consumer’s sample
  56. Phenotypic characterisation of indigenous chicken in the central zone of Tanzania
  57. Diversity and structure of bacterial communities in saline and non-saline rice fields in Cilacap Regency, Indonesia
  58. Isolation and screening of lactic acid bacteria producing anti-Edwardsiella from the gastrointestinal tract of wild catfish (Clarias gariepinus) for probiotic candidates
  59. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia
  60. Design of smart farming communication and web interface using MQTT and Node.js
  61. Assessment of bread wheat (Triticum aestivum L.) seed quality accessed through different seed sources in northwest Ethiopia
  62. Estimation of water consumption and productivity for wheat using remote sensing and SEBAL model: A case study from central clay plain Ecosystem in Sudan
  63. Agronomic performance, seed chemical composition, and bioactive components of selected Indonesian soybean genotypes (Glycine max [L.] Merr.)
  64. The role of halal requirements, health-environmental factors, and domestic interest in food miles of apple fruit
  65. Subsidized fertilizer management in the rice production centers of South Sulawesi, Indonesia: Bridging the gap between policy and practice
  66. Factors affecting consumers’ loyalty and purchase decisions on honey products: An emerging market perspective
  67. Inclusive rice seed business: Performance and sustainability
  68. Design guidelines for sustainable utilization of agricultural appropriate technology: Enhancing human factors and user experience
  69. Effect of integrate water shortage and soil conditioners on water productivity, growth, and yield of Red Globe grapevines grown in sandy soil
  70. Synergic effect of Arbuscular mycorrhizal fungi and potassium fertilizer improves biomass-related characteristics of cocoa seedlings to enhance their drought resilience and field survival
  71. Control measure of sweet potato weevil (Cylas formicarius Fab.) (Coleoptera: Curculionidae) in endemic land of entisol type using mulch and entomopathogenic fungus Beauveria bassiana
  72. In vitro and in silico study for plant growth promotion potential of indigenous Ochrobactrum ciceri and Bacillus australimaris
  73. Effects of repeated replanting on yield, dry matter, starch, and protein content in different potato (Solanum tuberosum L.) genotypes
  74. Review Articles
  75. Nutritional and chemical composition of black velvet tamarind (Dialium guineense Willd) and its influence on animal production: A review
  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
  77. The long-crowing chickens in Indonesia: A review
  78. A transformative poultry feed system: The impact of insects as an alternative and transformative poultry-based diet in sub-Saharan Africa
  79. Short Communication
  80. Profiling of carbonyl compounds in fresh cabbage with chemometric analysis for the development of freshness assessment method
  81. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part I
  82. Non-destructive evaluation of soluble solid content in fruits with various skin thicknesses using visible–shortwave near-infrared spectroscopy
  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
  84. Traditional agri-food products and sustainability – A fruitful relationship for the development of rural areas in Portugal
  85. Consumers’ attitudes toward refrigerated ready-to-eat meat and dairy foods
  86. Breakfast habits and knowledge: Study involving participants from Brazil and Portugal
  87. Food determinants and motivation factors impact on consumer behavior in Lebanon
  88. Comparison of three wine routes’ realities in Central Portugal
  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
  90. Environmentally friendly bioameliorant to increase soil fertility and rice (Oryza sativa) production
  91. Enhancing the ability of rice to adapt and grow under saline stress using selected halotolerant rhizobacterial nitrogen fixer
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