Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 1 Gusau Journal of Accounting and Finance (GUJAF) Vol. 2 Issue 1, April, 2021 ISSN: 2756-665X A Publication of Department of Accounting and Finance, Faculty of Management and Social Sciences, Federal University Gusau, Zamfara State -Nigeria Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 2 ACCOUNTING FOR BIOLOGICAL ASSETS AND AGRICULTURAL PRODUCE: DETERMINANTS OF COMPLIANCE WITH IAS 41 DISCLOSURES BY LISTED AGRICULTURAL FIRMS IN NIGERIA Muhammad Mustapha Bagudo PhD Department of Accounting Business School Ahmadu Bello University, Zaria. +2348036057525, mmbagudo@gmail.com Muhammad Yusuf Shuaibu Department of Accounting Business School Ahmadu Bello University, Zaria. +2348066299551, ysmkafi22@gmail.com Abstract The study investigated the impact of some firm specific attributes on compliance with International Accounting Standard (IAS) 41 of all 5 listed agricultural firms in Nigeria from 2012 to 2019. The study used secondary source of data collected from annual reports published by the firms and the data was analyzed using multiple regression. Findings from the study showed that biological assets intensity and firm size are positively and significantly related to compliance with IAS 41 disclosures, while leverage is negatively and significantly related to compliance with IAS 41 disclosures. Based on the findings the study recommends that listed agricultural firms should increase their biological assets as this leads to increase in compliance with international accounting standard issued by IASB to enhance the value of the firms. The firms should also make their capital structure less geared with focus more on equity than debts as more leverage leads to decrease in compliance with the standard. Furthermore, as much as possible, firms should enlarge the size of their firms as this leads to higher level of compliance with IAS 41 disclosures. Key words: Biological Assets Intensity, IAS 41, Agriculture firms and Nigeria. 1. Introduction With globalization of business and finance today, there is the need for uniform financial reporting framework to be used across the globe to aid comparability of accounting information (Tamosiunas, 2012). In response to this need, Federal Executive Council of Nigeria in its meeting on 28 th July, 2010 approved the 1 st January, 2012 as day for adoption of International Financial Reporting Standard with International Accounting Standard on agriculture as a new standard. In spite of the importance of Agriculture to global economy, accounting for agriculture related activities had got negligible attention from researchers until the adoption of International Accounting Standard 41 (Herbohn & Herbohn, 2006). This is because there had been no local standard that specifically dealt with accounting for agriculture. Agriculture has been identified as mainstay of Nigerian economy. It occupies a strategic position in Nigerian economy as it is one of the largest contributors to Nigerian growth domestic product (GDP). Until early 2016, there had been no significant attention to the sector. Today following the efforts by federal government to diversify the economy, market capitalization of agricultural sector on the Nigerian Stock Exchange stood at N103.017 billion as at September 2019 (Leadership, 2019). Prior to adoption of International accounting standard on agriculture, mailto:mmbagudo@gmail.com mailto:ysmkafi22@gmail.com Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 3 accounting for agriculture did not attract the attention of accounting and finance researchers in Nigeria. Accounting for agricultural activities was based on historical costs method which had limitation of not catering for the unique characteristics of biological assets; one of the important components of accounting for agriculture. There is now International Accounting Standard on agriculture (hereinunder referred to as IAS 41) which is expected to be used in reporting the financial matters of agricultural firms. Additionally, there is strong compliance with the standard by all listed agricultural firms in Nigeria (Ibrahim & Kurfi, 2019) Specifically, the standard covers biological assets, agricultural produce at the point of harvest and government grants. The underlying principle of IAS 41 is that increases in the values of all biological assets owned by reporting entity are recognized as the assets grown not solely on harvest or sales (IASB, 2014). The adoption of IAS 41 represents a radical change from the historical cost model because the standard requires that biological assets be measured at fair value less cost to sell on initial recognition and at subsequent reporting dates. However, there is one notable exception on initial recognition of biological asset. Where there is no available market to be used as a basis for estimation and the reporting entity cannot reliably estimate the fair value, the assets should be measured at cost less depreciation and impairment (Gonçalves & Lopes, 2014). In addition, agricultural produce shall also be measured at fair value less cost to sell but at the point of harvest only Despite the fact that studies have shown that agricultural firms have complied with IAS 41 (Ibrahim & Kurfi, 2019), yet there is little effort to empirically examine those variables that influence the compliance with the standard. Most of the studies to this effect have focused on other sectors. For example, Odia (2016) examined the determinants of IFRS compliance of 50 companies quoted in Nigerian Stock Exchange from 2011 to 2013 while Modugua nd Eboigbe (2017) also studied the determinants of corporate disclosure of 60 companies after the adoption of IFRS in Nigeria and considering the variables studied the sample may not include agriculture ,Segun (2019) studied small and medium scale enterprises (SMSEs) and then Echobu, Okika and Mailafia (2017) Agriculture and Natural Resources sector As can be seen from above, most of the studies on determinants of compliances in Nigeria have been conducted with focus on other sectors of our economy with little or no study that specifically covers agriculture. And given the importance of agriculture to Nigerian economy; there is the need for empirical studies to be carried out to examine those factors that can influence compliance with IAS 41 on agriculture; a standard that had hitherto been nonexistent in our financial reporting framework. Moreover, in studying the determinants of compliance with IFRS in Nigeria, researchers have focused on variables that are mostly universally applicable to all firms operating in all sectors of the economy. For example, while Odia (2016) focused mainly on firm size, leverage, operating cash flow and liquidity, Modugua and Eboigbe (2017) studied firm size and leverage only and Segun (2019) mainly looked at auditor type and company age. The results generated from these studies cannot be specifically applied in agricultural sector. This is because variables that are peculiar to agricultural firms and covered by the standard like biological assets and governments grants are least studied. Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 4 This study intends to contribute to the pool of existing literature on compliance with IAS 41 by examining the influence of one of the least studied variables that is peculiar to agricultural sector to which this standard applies. The study incorporates biological asset intensity as determinant of compliance with IAS 41. The other variables that are examined in addition to biological assets intensity in the study are liquidity, leverage, firm size and firm age. In a nutshell, the study seeks to examine the extent to which biological intensity, leverage, liquidity firm size and firm age determine compliance with IAS 41 by listed agricultural firms in Nigeria. On the basis of the broad objective stated above, the following null hypotheses have been formulated: HO1: Biological asset intensity has no impact on compliance with IAS 41 by listed agricultural firms in Nigeria HO2: Leverage has no impact on compliance with IAS 41 by listed agricultural firms in Nigeria HO3: Liquidity has no impact on compliance with IAS 41 by listed agricultural firms in Nigeria HO4: Firm size has no impact on compliance with IAS 41 by listed agricultural firms in Nigeria HO5: Firm age has no impact on compliance with IAS 41 by listed agricultural firms in Nigeria The result of this study will be beneficial to regulators especially Financial Reporting Council of Nigeria as it will provide useful information on the extent to which agricultural firms in Nigeria comply with IAS 41 and how some specific characteristics of the firms determine the compliance with the standard 2.1 Literature Review Compliance has been defined as an act of transmitting economic, financial, non-financial, quantitative and non-quantitative data in a way that reflects the realities on ground (Gonçalves & Lopes, 2014a) Conceptual framework Below is the pictorial graph of the relationship between the variables Review of empirical studies Under this heading, some relevant and related empirical studies are reviewed as seen below; Martanti, Lestari, Zarkasyi (2019) studied the impact of biological assets intensity and firm size on the financial performance of listed agricultural firms on the Indonesian and Malaysian Stock Exchange. Using a sample of 35 firms and cross-sectional data with focus on the year 2018, the regression results showed that both biological assets intensity and firm size had no impact on financial performance both in Indonesia and in Malaysia. The study was conducted in different countries and due to some institutional factors, the result cannot be applied in Nigeria. Biological asset intensity Leverage Compliance Index Liquidity Firm Size Firm Age Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 5 Furthermore, Cyril, Elizabeth and Chukwuemeka (2019) studied the impact of fair value accounting on biological assets of listed firms in Nigerian agricultural sector with Okomu Oil Palm Plc a case study. The study used time series data for the period of 2009-2018. Using Ordinary Least Squares (OLS) multiple regression, the study found that fair value accounting has insignificant positive effects on biological assets. The study focused on only one agricultural firm out of only five that could have been studied So also, Segun (2019) studied determinants of compliance with compliance with International Financial Reporting Standards of Small and Medium Scale Enterprises (SMSEs) in Ondo state. The study covered the period of year IFRS was adopted for SMSEs which is 2014 to 2018. Using primary source of data and standard multiple regression, the study revealed that compliance with IFRS disclosures is significantly influenced by auditor type, company age, firm indices and industry indices. However, the study covers only SMSEs only whose institutional framework is still not robust enough. Echobu, Okika and Mailafia (2017) studied the determinants of financial reporting qualities among listed agricultural and natural resources firms in Nigeria using a sample of seven out of nine firms for the period of seven years from 2008 to 2015. Using regression and residuals from the modified Jones model by Dechow, Sloan and Sweeney (1995) as a measure of financial reporting quality, the study found that there is a positive significant relationship between leverage, liquidity, board size and financial reporting quality. The data used is old having been gathered in 2015 and may not reflect the current reality. Modugua and Eboigbe (2017) also studied the determinants of corporate disclosure after the adoption of IFRS in Nigeria with focus on firm size and leverage. The study covered the period of three years (2012 – 2014). The study used, as sample, 60 companies listed on the Nigerian Stock Exchange from the various sectors of the Nigerian economy which does not cover agriculture. Using ordinary Least Square regression, the study found that leverage and firm size have a significant positive relationship with total disclosure. However, the study focused on only two variables. In addition, Abdirahman (2017) assessed the determinants of corporate disclosures among 64 firms listed on Nairobi Stock Exchange in Kenya. The study covered the period of five years from 2012 to 2016 and used secondary data. Using multiple panel regression model, the study found that firm size and profitability have negative but significant and negative but insignificant impact on the disclosure respectively. However, the study concluded that firm size and profitability are the only determinants of corporate disclosures. Odia (2016) examined the determinants of IFRS compliance of only 50 companies quoted in Nigerian Stock Exchange covering the year 2011 to 2013. The determinants examined are firm size, leverage, operating cash flow, liquidity, profitability, turnover, growth in turnover, earnings quality, board size, audit type and board independence. He used ordinary Least Square regression model where he found all of them to have significant positive relationship with IFRS adoption with exception of profitability and earnings quality. The study used overlapping variables. That is to say turnover and change in turnover. The effects of the two variables are likely to be same. Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 6 Additionally, Alfraih and Alanezi, (2015) investigated firm specific attributes of listed firms in Kuwait Stock Exchange in relation to corporate disclosure. The study used 181 firms as sample of the study for the 2010. Using self-constructed disclosure as dependent variable which was regressed against firm age, firm liquidity, firm leverage, firm size, firm profitability, audit quality and firm industry type. The multivariate regression results showed that older, highly leveraged, larger, and profitable listed firms in Kuwait are associated with high levels of disclosures. The period coverage is matter of concern and there is the need for fresh study. Furthermore, Gonçalves and Lopes (2014) studied the determinants of compliance with international accounting standard on agriculture of 270 firms nationwide. Using cross sectional data, the study focused on the year 2011 only. The study categorized the determinants into firm- level (biological assets intensity, ownership concentration, firm size, auditor type, internationalization level, listing status, profitability and sector) and country-level (legal status). The study found the disclosure of biological assets by agricultural firms is influenced by biological assets intensity, ownership concentration, firm size, sector and legal status. The study covers firms across the globe and the study may not have taken into consideration the divergent institutional arrangements that affect all the firms. Ibrahim (2014) also examined the effects of firm characteristics on corporate disclosures of 76 firms in Nigeria cutting across all sectors including agriculture with focus on segments reporting (IFRS 8). The study covered the year 2011 only. Using ordinary least square regression model, the result of the study revealed that that firm size and industry type are positively associated with voluntary segments disclosure. In addition, the study also found that firm listing age, growth, return on investment and ownership diffusion are negatively associated with voluntary segments disclosure. The study is somewhat old and the result may not be applicable to present situations. This study used agency theory as theoretical basis. The theory was developed by Jensen and Meckling (1976). Agency theory is deemed appropriate for this study because compliance with reporting regulations released by appropriate regulatory agency is expected from managers who are seen as agents. This much is expected by shareholders who are seen as principal in the principal-agent relationship in which power to take decisions is vested in the agents by the principal. The relationship breeds conflicts as a result of which it cannot be said with certainty that the principal would do as expected by the agents. As a result of this, measures are put in place including monitoring like IFRS reporting framework that would reduce agency costs and by extension reduce the opportunistic tendencies of managers (Jensen & Meckling, 1976). Since financial reporting standard is one of the monitoring mechanisms put in place to properly monitor the behavior of agents with a view to reducing agency costs as opined by Jensen and Meckling, it follows, therefore, that agency theory can be used for study that revolves around compliance with accounting standards. 3. Methods and Techniques The research design is correlational in nature. This is informed by the paradigm on which the research is based which is positivism approach. The data to be used is panel because the data cut across different firms at different times. Therefore, panel regression is used for the purpose of analyzing the relationship between the dependent variable and independent variables. This study Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 7 covers the period of eight years (2012-2019). The decision to take this period has been anchored on the fact that IFRS was adopted in 2012 and data relevant to the study can only be obtained within this period. The population of the study is made up of all agricultural firms listed on the floor of Nigerian stock exchange as at 31th December, 2019. The study takes all the listed agricultural firms as at aforementioned date because it is concerned with post IFRS adoption era. The firms are: Ellah Lakes Plc, FTN cocoa processors Plc, Livestock Feeds Plc, Okomu Oil Palm Plc, Presco Plc. 3.1 Model specification and variables measurement For the purpose of this study, compliance with IAS 41 is to be proxied using self-constructed disclosure index from IAS 41 The model to be used will then be: DINDEXit=β0+β1BAIit+β2LIQit+β3LEVit+β4FSZit+β5FAGit+ εit Where: DINDEX= Disclosure Index β0= Constant BAI= Biological Asset Intensity LIQ= Liquidity LEV= Leverage FSZ= Firm Size FAG= Firm Age β1-β5= Coefficients of the variables ε=Error terms i= individual firm t=time dimension The variables captured in the model above are to be measured as follows: Compliance index: Dummy of 1 for item disclosed and 0 for item not disclosed. Then, the ratio of total items disclosed to total items to be disclosed as required by the standard will be taken for each firm Biological asset intensity: Biological Assets divided by total assets Leverage: Long term liabilities divided by total equity Liquidity: Current liabilities divided by current assets Firm Size: Total assets of the firm Firm Age: Number of years the firm had been in existence as at 31 th December, 2019 4. Results and Discussion Under this section the results found by the study were presented and discussed, from which conclusions were drawn. it began by presenting descriptive statistics, then correlation matrix, multicollinearity tests, heteroskedasticity test and finally regression results. 4.1 Descriptive Statistics The descriptive statistics highlights the basic features of the data collected for the purpose of this study in relation to both the dependent, independent variables as reported in the below table: Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 8 Table 1: Summary of Descriptive Statistics Variables Obs Mean Minimum Maximum Standard deviation DINDEX 40 0.69 0.58 0.85 0.6 BAI 40 0.44 0.10 0.68 0.22 LEV 40 2.99 -5.6 99.69 15.74 LIQ 40 1.81 0.12 15.51 2.62 FSZ 40 9.93 9.05 10.92 0.61 FAG 40 33.9 17 56 11.54 Source: output of descriptive statistics from STATA 13 From the table 4.1 above, it can be seen that the average compliance with international standard on agriculture is 0.69 meaning that on average the compliance with this standard by listed agricultural firms between 2012 to 2019 stands at 0.69. The minimum level of compliance is 0.58 and the maximum is 0.85 with 0.6 as standard deviation signifying that the compliance is not far away from the mean. The table also shows that the biological asset intensity on average is 0.44 among all the listed agricultural firms between 2012 to 2019. The minimum biological asset intensity is 0.10 and the maximum is 0.68 showing that the lowest intensity of biological assets is 0.10 and the highest intensity is 0.68 within the period covered by the study. The standard deviation of 0.22 shows that the dispersion is not that much. The table also shows that the leverage on average is 2.99 among all the listed agricultural firms between 2012 to 2019. The minimum leverage is -5.6 suggesting that there is a firm that reported negative equity during the period covered by the study. The maximum leverage is 99.69 and the dispersion of the observation from the mean is 15. 74 suggesting that the of the data from the mean is high . Furthermore, the table shows that the average liquidity among the firms considered by the study is 1.81 with 0.12 as minimum liquidity and 15.51 as maximum. The deviation from the average is signified by standard deviation of 2.62 showing that the mean is not far away from the observations. The table shows that the average size of firm proxied by total assets is 9.93 with 9.05 and 10.96 and minimum and maximum firm size respectively. The deviation of the variables from the observations is 0.61 signifying that the variation of size among the listed agricultural firms within the period of the study is very high. Finally, the table also shows that the average age of firm among the listed agricultural firms in Nigeria is 33.9. The minimum age of the firm in the sector is 17 and the maximum age is 56. The standard deviation of 11.54 suggested that the deviation from the mean is not high. 4.2 Correlation Matrix The correlation matrix table shows the relationship between all explanatory variables individually with explained variable and the relationships among the independent variables themselves. Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 9 Table 2: Correlation Matrix DINDEX BAI LEV LIQ FSZ FAG DINDEX 1.0000 BAI 0.87 1.0000 LEV -0.77 -0.07 1.0000 LIQ -0.32 -0.45 -0.077 1.0000 FSZ 0.87 0.84 -0.78 0.30 1.000 FAG -0.32 -0.15 -0.17 0.07 -0.24 1.000 Source: output of correlation matrix from STATA 13. From the above table it can be seen that the correlation between the independent variables and the dependent variables have both positive and negative values signifying that there is both positive and negative correlation among the variables. There are some correlation values that are more than 0.8 which shows the likelihood of existence of multicollinearity. However, it cannot be concluded except a multicollinearity test is conducted. Gujarati (2004) states that a correlation of value greater than 0.8 may amount to multicollinearity. However, this cannot be ascertained until after the test for multicollinearity has been conducted. 4.3 Multicollinearity Test To test for multicollinearity, variance inflation factors (VIF) and tolerance tests were carried out. The results are presented below. Table 3: Multicollinearity Test Variable VIF 1/VIF BAI 4.13 0.241888 FSZ 3.84 0.260417 LIQ 1.29 0.775120 FAG 1.12 0.890525 LEV 1.07 0.936145 VIF mean 2.29 Source: output from STATA 13 From the table above, the tolerance value (1/VIF) of the individual variables are all greater than 10% and less than 1. So also, the highest value of VIFs is 4.16 (less than 10), confirm the absence of multicollinearity among the variables (Gujarati, 2004). Heteroskedasticity To test for heteroskedasticity, the study employs breusch-pagan/cook-weisberg test. The test shows a chi2 value of 5.26 and the prob> chi2 of 0.219 (insignificant). This indicates the absence of heteroscedasticity. 4.4 Regression Results Table 4: Robust Regression Result Variables Coefficient T- value P>(Z) BAI 0.1622627 4.02 0.000 LEV -0.000283 -2.75 0.010 LIQ 0.0009741 0.98 0.333 Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 10 FSZ 0.046427 2.73 0.010 FAG -0.0009054 -1.83 0.56 Constant 0.1889464 1.18 0.247 R Squared: 0.8411 f-Statistics: 67.17 Prob.: 0.000 Source: Output from STATA 13. After running the fixed effect and random effect regressions, Hausman test for fixed effect was conducted and the probability of the chi2 was not significant. This informed the study to conduct Langrangian Multiplier test for random effect, it was also not significant, the researcher moved further to run robust regression. Based on the robust regression, the results of robust OLS are as interpreted below. A careful examination of the table above shows that the R 2 is 0.8422 at 1% level of significance signifying that all the five independent variables studied put together explain the dependent variable by 84% which is the remaining percentage is accounted for by other variables that have not been incorporated into the mode. When the individual effects of the independent variables on the dependent variable are looked at in the table above it can be inferred that the coefficient of biological assets intensity is 0.17 at 1% level of significance showing that there is a significant positive relationship between biological assets intensity and compliance. For this reason, we reject the null hypothesis which has it that biological asset intensity has no impact on compliance with IAS 41 by listed agricultural firms in Nigeria. However, the coefficient of leverage which is -0.0028 at 5% level of significance signifies that there is negative but significant relationship between leverage and compliance with IAS 41 by listed agricultural firms in Nigeria thus rejecting the null hypothesis in this direction. For liquidity with coefficient of 0.0097, the results show that there is positive but insignificant relationship between liquidity and compliance with IAS 41. We therefore fail to reject the null hypothesis. Furthermore, the results from the table show that firm size with 0.046 coefficient at 5% level of significance has positive and significant relationship with compliance with the standard by listed agricultural firms in Nigeria. We therefore reject the null hypothesis in that direction. For firm age, the coefficient is negative which is -0.0091 but is insignificant going by the level of significance. We therefore fail to reject the null hypothesis. This shows that increase in firm age leads to decrease in the level of compliance with the standard by listed agricultural firms in Nigeria within the period covered by the study 5.1 Summary, Conclusion and Recommendation The study centered on how some firm level attributes of listed agricultural firms in Nigeria influence compliance with IAS 41. To achieve this, the study made use of data available from the annual reports of the firms. The data sourced was analyzed using OLS. The study used Gusau Journal of Accounting and Finance, Vol. 2, Issue 1, April, 2021 11 constructed compliance index as dependent variable and took biological assets intensity, leverage, liquidity, firm size and firm age as independent variables. The study concluded that biological assets intensity and compliance are positively and significantly related, leverage and compliance index negatively and significantly related, compliance and liquidity positively but insignificantly related, compliance and firm size positively and significantly related and finally firm age is negatively but insignificantly related with compliance. Based on the above findings, the study recommends that listed agricultural firms should increase their biological assets as this leads to increase in compliance with standard aimed at enhancing the value of the firms. The firms should also make their capital structure less geared with focus more on equity than debts as more leverage leads to decrease in compliance with the standard. Furthermore, as much as possible, firms should enlarge the size of their firms as this leads to higher level of compliance with IAS 41. References Abdirahman, O. (2017). 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