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Factors affecting household carbohydrate food consumption in Central Java: Before and during the COVID-19 pandemic

  • Wiwit Rahayu EMAIL logo , Darsono Darsono , Sri Marwanti and Ernoiz Antriyandarti
Published/Copyright: March 30, 2023
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Open Agriculture
From the journal Open Agriculture Volume 8 Issue 1

Abstract

The COVID-19 pandemic has resulted in a change in food demand. In Central Java, during the pandemic (2021), the proportion of expenditure on the grain food group was higher than in 2020; meanwhile, the proportion of the ready-to-eat food group decreased. This study aims to analyze the pattern of food consumption of carbohydrate sources, the influencing factors, and the elasticity of consumption in households in Central Java before and during the COVID-19 pandemic. This study uses data from the National Socio-Economic Survey (Susenas) for 12 districts/cities in Central Java consisting of 9,812 in 2019 and 10,636 households samples in 2021. Data analysis used the Linear Approximation Almost Ideal Demand System (LA-AIDS) method. Results of the study show that the COVID-19 pandemic has caused changes in the household consumption pattern of carbohydrate-source food in Central Java. The proportion of expenditure on rice, wheat flour, shelled corn, cassava, and potatoes has increased. On the other hand, wet corn, instant noodles, and white rice decreased. The price of food sources of carbohydrates and the number of household members positively affect the consumption of food sources of carbohydrates. At the same time, income has a negative effect. There are differences in the effect of the location of the residence on the consumption of food sources of carbohydrates before and during the pandemic. The value of own-price elasticity and income elasticity shows that before the pandemic, rice was a staple good whose consumption was inelastic. Before and during the COVID-19 pandemic, wheat flour, wet-skinned corn, shelled corn, potatoes, white rice, and cassava in the pre-pandemic period was Giffen because the price elasticity was positive, and the income elasticity was negative. Meanwhile, rice during the pandemic, instant noodles before and during the pandemic, and cassava were Veblen goods because their price and income elasticity were positive. Cross elasticity shows that before the pandemic, most of the relationships between food sources of carbohydrates were substitutes, while during the pandemic, most of the relationships between food sources of carbohydrates were complementary.

Keywords: AIDS; elasticity; food consumption pattern; share of food expenditure

1 Introduction

Food consumption is the type and amount of food consumed by individuals or communities that aim to meet the needs of biological, psychological, and social status [1,2]. Food consumption will produce a healthy body if contain element of carbohydrates, proteins, fats, vitamins, and minerals in sufficient and balanced quantities [3].

The calorie demands of the human body are mainly supplied by three dietary macronutrients: carbohydrates, fat, and protein [4]. Carbohydrates are the main source of energy in the diet (55–75%) for most people. Contribution of carbohydrates to total dietary energy consumption: 63% in the world, 53% in developed countries, 67% in the developing world, and 72% in Sub-Saharan Africa [5]. Carbohydrates provide the body with glucose, converted to energy used to support bodily functions and physical activity [6].

Grain products, tubers, roots, and some fruits are rich in complex carbohydrates. Polysaccharides are commonly referred to as complex carbohydrates. They are found in a wide variety of natural and processed foods. Starch is a polysaccharide abundant in cereals (wheat, maize, and rice), potatoes, and processed food based on cereal flour, such as bread, pizza, or pasta [7]. Rice is a staple cereal in social nutrition and the primary food grain for more than a third of the world’s population [8]. Rice is a great source of carbs, with roughly 87% of the grain being carbohydrates. It contains 7–8% protein, which is more digestible, biologically valuable, and nutritious, as well as less crude fiber and fat (1–2%). Rice provides around 20% of the world’s dietary energy, which is more than maize or wheat [9].

Consumption of staple foods such as rice, maize, and cassava is very high in low- to middle-income countries [10–13]. Staple food as a source of carbohydrates has an important role in human life, especially in developing countries such as Indonesia [14]. Rice is the main staple food and the main food source of carbohydrates for most of Indonesia’s population [15–21]. In addition to rice, other carbohydrate sources commonly consumed are corn, cassava, sweet potatoes, and wheat and their derivatives, such as bread and instant noodles [22]. In 2018, rice consumption by Indonesian people was 81.60 kg/capita, a decrease from 2017, which was 86.82 kg. This shows a change in the consumption pattern from rice to other carbohydrate-source foods [22].

Changes in food preferences are dynamic in line with changes in education level, income, number of household members, and age. Price increases or decreases will impact preferences [22]. Changes in the economy, nutrition policies, and food processing methods can affect the macronutrient composition and diet quality at the population level [19,20,21]. Food price positively determines people’s real income for food traders and negatively for buyers, which will affect the distribution of income and investment, thus contributing to poverty, which also influences household access to food [23].

Disasters such as the COVID-19 pandemic can disrupt the food system [24]. The spread of the COVID-19 virus has had a major impact on people’s lives, economically and socially, to food [25,26]. The COVID-19 pandemic is a health and humanitarian crisis threatening the food security and nutrition of millions worldwide. High unemployment, loss of income, and rising food costs make access to food difficult for many [27]. Restrictive measures on daily life that the government has implemented during the COVID-19 pandemic have an impact on behavior and eating patterns [28–32]. In Indonesia, the Large-Scale Social Restrictions (PSBB) policies in various regions directly or indirectly have an impact on food commodity prices at the farmer level as well as changes in food demand at the consumer level. The COVID-19 pandemic has resulted in a decline in food demand. Low-income households reduce the demand for nutritious food and consume more carbohydrate sources [33]. The dynamics of food expenditure based on the food group of the population of Central Java shows that during 2016–2020 the proportion of food expenditure on grains tended to decrease, while for the processed food and beverage group, it tended to increase [34]. However, in 2021, the proportion of expenditure on the grain food group is 5.35%, higher than in 2020, which is 5.20%. Meanwhile, for the ready-to-eat food group, the proportion decreased from 17.03 to 16.40% [33].

Several studies have been conducted to determine the relationship between carbohydrate consumption with health and socio-economic factors. Research to evaluate carbohydrate consumption and their associations with the metabolic disease was conducted by refs. [35–39]. Trends in carbohydrate consumption have been researched by ref. [9] and concluded that from 1999 to 2016, US adults experience a significant decrease in energy intake from low-quality carbohydrates and significant increases in energy intake from high-quality carbohydrates, plant protein, and polyunsaturated fat, and ref. [40] found that parboiled long-grain white rice is the most commonly consumed carbohydrate by urbanized Nigerians. Price change in carbohydrate sources has been proven to be more influential on the food demand in food-insecure provinces in Indonesia [41], the demand for carbohydrate-sourced food differs between rural and urban households which are influenced by price, income, and number of household members [42]; patterns of carbohydrate consumption in the city of Bengkulu showed that total rice consumption expenditures greater than the bulbs and factors affecting are the economic factor [43]. The dietary habits in Poland, Austria, and the United Kingdom have changed as a result of the COVID-19 pandemic. The resulting data revealed an increased frequency of the daily consumption of food products such as dairy, grains, fats, vegetables, and sweets [44]. A study on the impact of the pandemic on nutrients and food intake of children and adolescents in Germany found that no significant changes in either the selected nutrients or food groups were observed. However, children and adolescents recorded a significantly lower TEI during the pandemic [33]. The COVID-19 pandemic negatively impacted the intake of dietary energy, carbohydrates, fats, and proteins, and the dietary intake of the low-income group was significantly affected by the COVID-19 pandemic [45]. During lockdown, there was an increase in carbohydrate intake [46].

Many studies on carbohydrate consumption had been conducted before the COVID-19 pandemic but they were limited during the pandemic. Some studies suggest changes in food consumption during the COVID-19 pandemic. However, most studies do not include objective control data before the pandemic and therefore are based on subjective estimations by the participants themselves. The difference between this study and previous studies is that in this study, carbohydrate consumption patterns and the influencing factors are evaluated based on data before and during the COVID-19 pandemic. The amount and type of food sources of carbohydrates in this study were also different from those of the previous studies [14,41].

This research aims to analyze the consumption pattern of carbohydrate-source food in households in Central Java, the influencing factors, and the response of households in consuming carbohydrate-source foods to income and price food changes before and during the COVID-19 pandemic. This study is important for policymakers to find out the household's behavior in consuming carbohydrate-sources food, before and during the pandemic. The result of the research will provide instructions for implementing appropriate food consumption policies.

2 Materials and methods

2.1 Data

This study uses data from the 2019 and 2021 National Socio-Economic Survey (Susenas). The Susenas data required include the consumption and expenditure module at the household level in the Central Java province for March 2019 and 2021. The data for 2019 represent the conditions before the COVID-19 pandemic, while the data for 2021 represent the conditions for the COVID-19 pandemic. The data are obtained from the Central Statistics Agency (BPS). The sample households in this study were Susenas sample households from 12 districts/cities in Central Java consisting of 9,812 households in 2019 and 10,636 households in 2021. The core data needed in this study include the status of residence (urban and rural) and the number of household members. Meanwhile, the consumption module data needed were household food and non-food expenditure, total expenditure as a proxy for household income, description of quantity and household expenditures for carbohydrate-source food (rice, wheat flour, wet-skinned corn, shelled corn, cassava, instant noodles, and white rice as the carbohydrate-source food), and food commodities suspected of influencing the consumption of carbohydrate-source food (chicken eggs, chicken meat, beef, catfish, processed milkfish, tempeh, tofu, red chilies, shallots, and cooking oil).

2.2 Data analysis

Factors affecting household consumption of carbohydrate sources were analyzed using the Linear Approximation Almost Ideal Demand System (LA-AIDS) model, a modification model developed by [47] by inputting various variables that were theoretically and empirically relevant to affect the demand. The AIDS model has long been used to examine food consumption patterns as has been done by refs [40–42].

The general formulation of the AIDS equation model is as follows [47]:

(1) w i = α i + β i log y ,

where w i is the share of the ith commodity expenditure, and y is the income (expenditure). The AIDS demand model is built on a specifically defined cost or expenditure function to represent the structure of individual preferences.

The preference function (c) as a function of the utility u and the value of p is defined in the following logarithmic form:

(2) ln c ( u , p ) = ( 1 − u ) ln a ( p ) + u ln b ( p ) ,

where c is the total expenditure, u is the utility; and p is the price.

Equation (2) is a function of a(p) and b(p), which is linear positive and homogeneous with degree one concerning price. The function a(p) is between zero and one, so it can be interpreted as a subsistence cost (u = 0). At the same time, b(p) is the cost of satisfaction (u = 1). If it is known that n commodities have the functions log a(p) and log b(p) with the following equation

(3) ln a ( p ) = α 0 ∑ i = 1 N α k ln ( p i ) + 1 2 ∑ i = 1 N ∑ j = 1 N γ ij * ln ( p i ) ln ⁡ ( p j ) ,

(4) ln b ( p ) = ln α ( p ) + β 0 ∏ i = 1 N p i β i .

Using the substitution rule, equations (3) and (4) into equation (2) are obtained:

(5) ln c ( u , p ) = α 0 ∑ i = 1 N α i + ln ( p i ) + 1 2 ∑ i = 1 N ∑ j = 1 N γ ij * ln ( p i ) ln ⁡ ( p j ) + u β 0 ∏ i = 1 N p i β i ,

where α, β, and γ are the parameters. If we use logarithmic differentiation in equation (5), we get the price and utility functions expressed by the budget section as in equation (6).

(6) w i = α 1 ∑ j = 1 N γ i ln ( p j ) + β i u β 0 ∏ i = 1 N p i i ,

where,

(7) γ ij = 1 2 + ( γ ij * + γ ij * ) .

The budget distribution equation (equation (8)) in its basic form can be used for model estimation.

(8) w i = α 1 ∑ j = 1 N γ ij ln ( p j ) + β i ln x p + ε i , dimana i = 1 , … , N .

The AIDS demand function in the form of a budget share used in this research was as follows:

(9) w i = α i + ∑ j γ i j ln P j + β i ln ( Y / P ⁎ ) + α i ART + α i D _ w i l j + u i ,

where i, j are 1, 2, · · ·, n, w i the expenditure share on food commodity as the i-carbohydrate source (%), P j , the estimated price of the j carbohydrate-source commodity (IDR), Y total of household food expenditure (Rp/month), P* Stone price index, namely Ln P = ∑iw i ln P i , ART number of household members, D_w ilj is the dummy location type (0 = rural, 1 = urban), α i , γ ij , β i are AIDS model parameters (intercept parameter, total expenditure, and aggregate price), and the u i error term.

The AIDS model estimation requires restrictions on parameters to be consistent with utility theory. The restrictions in question are Adding Up, Homogeneity, and Symmetry.

  1. adding up, which allows the proportion of expenses to be one or written:

    (10) ∑ i = 1 N α i = 1 , ∑ i = 1 N β i = 0 , ∑ j = 1 N γ ij = 0 .

  2. Homogeneity means that if there is a proportional change in all prices and expenses, it does not affect the number of goods purchased.

    (11) ∑ j = 1 N γ ij = 0 .

  3. symmetry which indicates the consistency of consumer choice is mathematically written as follows:

(12) γ ij = γ ji .

Elasticity analysis was conducted to determine how big the response of household carbohydrate-source food consumption to changes in prices and income was. Elasticity is formulated as follows:

Own-price elasticity:

(13) E ii = y ii − β i w i w i − 1 .

Cross-price elasticity:

(14) E ij = y ij − β j w j w i .

Income elasticity:

(15) E iy = β i w i + 1 .

3 Results and discussion

3.1 Carbohydrate food consumption patterns before and during the COVID-19 pandemic

Household expenditure on food can be expressed as food consumption and is mainly influenced by income received [48]. The pattern of consumption of food sources of carbohydrates is seen based on the proportion of expenditure on food sources of carbohydrates to total expenditure on food (Table 1).

Table 1

Share of expenditure on carbohydrate-sourced food by area of residence in Central Java before and during the COVID-19 pandemic

Food sources of cabohydrates Before COVID-19 pandemic During COVID-19 pandemic
Village City Central Java Village City Central Java
Rice flour 12.285 13.940 12.358 12.714 12.712 12.718
0.418 0.320 0.414 0.544 0.543 0.542
Skinned wet corn 0.064 0.045 0.064 0.061 0.061 0.061
Shelled corn cassava 0.027 0.025 0.026 0.048 0.048 0.048
0.275 0.099 0.267 0.398 0.398 0.398
Potato 0.319 0.305 0.319 0.355 0.354 0.353
Instant noodles 1.808 1.555 1.797 1.780 1.780 1.781
White rice 0.766 0.467 0.753 0.721 0.719 0.718

Table 1 shows the largest proportion of rice expenditure compared to other carbohydrate sources. This shows that rice is the main source of carbohydrates consumed by households in Central Java. These results align with research [15–18,22] in which the rice is the main source of carbohydrates and the main staple food for the Indonesian population. In addition to rice, carbohydrates widely consumed consecutively are instant noodles, white rice, wheat flour, and cassava.

The COVID-19 pandemic has caused a change in the consumption pattern of food sources of carbohydrates in households in Central Java. During the COVID-19 pandemic, the proportion of expenditure on rice, wheat flour, shelled corn, cassava, and potatoes increased. On the other hand, wet corn, instant noodles, and white rice decreased. Research [35] shows that the COVID-19 pandemic has led to an increase in the consumption of food sources of carbohydrates, namely grains, and a decrease in the consumption of processed food.

The analysis results based on place of residence show that during the pandemic, the proportion of expenditure on rice in cities decreased, while instant noodles and white rice increased. This indicates that in urban areas, the consumption of food sources of carbohydrates in the form of food is greater. Limiting activities outside the home makes households prefer food sources of carbohydrates in the form of ready-to-eat foods, namely white rice, or more practical foods, namely instant noodles.

3.2 Factors affecting household carbohydrate food consumption in Central Java before and during the COVID-19 pandemic

Food consumption analysis is conducted to study what and how different factors affect food consumption. In general, the factors that influence food consumption include the price of the goods, the prices of other related goods, income, preferences, technology, population size, and other factors [49]. Table 2 presents the determinants of household food consumption of carbohydrates in Central Java before and during the COVID-19 pandemic.

Table 2

Determinants of carbohydrate-source food consumption in Central Java before and during the COVID-19 pandemic

Wbr Wtr Wjgk Wjgp Wskg Wktg Wmie Wnsi
Before pandemic
Intercept −0.01656*** −0.00027 0.00057*** 0.00156*** 0.00072*** 0.00035 −0.00336*** 0.02498***
Dlr 0.01062*** 0.00007 −0.00006 −0.00011 −0.00021 0.00017 0.00142*** −0.00007
lpart 0.01292*** 0.00022*** 0.00002 0.00005** −0.00004 0.00002 0.00095*** 0.00056***
lpbr 0.10927*** −0.00002 −0.00009*** −0.00028*** −0.00003 0.00007** 0.00026*** −0.00260***
lptr 0.00025*** 0.00967*** −0.000003 0.00002** −0.00002** −0.00001 0.00002 0.00002
lpjgk 0.00012 0.00003* 0.01236*** 0.00001 0.00002 −0.00004* 0.00007 0.00010
lpjgp −0.00026 0.00001 −0.00002 0.03482*** 0.00019*** 0.00004 −0.00002 −0.00024
lpskg −0.00016 0.00001 0.00001 0.00002** 0.01031*** 0.00002** 0.00004 −0.00003
lpktg −0.00006 0.00004*** −0.00001 0.00000 −0.00000 0.01261*** −0.00003 0.00003
lpmie −0.00040*** −0.00001 −0.00001* −0.00002* −0.00003 −0.00006*** 0.02813*** −0.00007
lpnsi −0.00042*** −0.00001 −0.00002** −0.00002** −0.00005 −0.00000 −0.00003 0.05121***
lpmgr 0.00013 −0.00001 0.00002* 0.00008*** 0.00003* 0.00001 −0.00008* −0.00016**
lptlr −0.00016 0.00001 0.00002** 0.00002** 0.00004*** 0.00002 0.000145*** −0.00006
lpaym −0.00055*** −0.00003*** −0.00001** −0.00001 −0.00005*** −0.00004*** −0.00004* −0.00004
lpspi 0.00051*** 0.00003** 0.000002 −0.00001 −0.00002 0.00000 0.00016*** 0.00010
lpthu −0.00003 −0.00001 0.00001 0.00000 0.00000 −0.00000 −0.00004 −0.00006
lptpe 0.00029** 0.00001 −0.00003** −0.00003** 0.00002 0.00000 0.00002 −0.00004
lpcbe −0.00021*** −0.00002** −0.000002 −0.00002*** −0.00004*** −0.00000 0.00001 0.00000
lpbwm 0.00027 0.00001 0.00004** 0.00006*** −0.00002 −0.00007*** −0.00023*** −0.00008
lplle −0.00017** −0.00001 −0.00000 −0.00000 −0.00004*** −0.00005** −0.00001 −0.00000
lpbdg −0.00028* 0.00001 −0.00001 −0.00000 −0.00002 −0.00005** −0.00003 −0.00009
Income −0.06048*** −0.00440*** −0.00527*** −0.01618*** −0.00412*** −0.00583*** −0.01161*** −0.02161***
R 2 0.7859 0.7791 0.5819 0.6091 0.7770 0.7551 0.7154 0.6830
During pandemic
Intercept −0.0244561*** −0.00065** −0.00002 0.00005 0.00098*** −0.00006 −0.00592*** 0.03006***
Dlr −0.00205* −0.00023** −0.00009* −6.25 × 10−6 −0.00042*** −0.00032*** 0.00038* −0.00024
lpart 0.018821*** 0.00055*** 0.00000 0.00007*** 0.00011*** 0.00021*** 0.00200*** 0.00078***
lpbr 0.13038*** −0.00002 −0.00002 −7.59 × 10−7 0.00009** 0.00003 0.00027*** −0.00330***
lptr 0.00052*** 0.01328*** −0.00002*** −1.98 × 10−7 5.23 × 10−6 −0.00002* 0.00005* −0.00007*
lpjgk 0.00008 0.00001 0.01248*** −9.88 × 10−6 −0.00004* −0.00004 2.15 × 10−6 0.00002
lpjgp −0.00157*** −0.00006 0.00000 0.03314*** 0.00019*** 0.00004 −0.00008 −0.00017
lpskg −0.00036*** −0.00000 0.00000 −0.00002** 0.00962*** 6.36 × 10−6 −0.00005* −0.00006
lpktg 0.00013 0.00005*** −0.00000 −9.71 × 10−6 0.00002 0.01741*** −7.99 × 10−6 0.00002
lpmie −0.00024 −0.00002 −0.00000 2.48 × 10−6 −0.00008*** −0.00003** 0.03228*** 0.00005
lpnsi −0.00025 −0.00000 0.00000 −7.93 × 10−6 −0.00003** −1.12 × 10−6 −0.00011*** 0.04885**
lpmgr −0.00019 −0.00003* 0.00002* 6.90 × 10−6 −0.00005** −2.74 × 10−6 −0.00005 0.00002
lptlr 0.000569*** −0.00001 0.00000 −8.00 × 10−6 −0.00006*** −4.88 × 10−6 0.00005 −0.00011*
lpaym −0.00034*** −0.00000 −9.91 × 10−6** −0.00001** −0.00006*** −0.00002** −0.00008*** 0.00003
lpspi 0.00130*** 0.00005*** −0.00000 4.98 × 10−6 −0.00006*** 0.00006*** 0.00004 0.00015***
lpthu −0.00112*** −0.00002 −0.00000 9.70 × 10−7 −7.60 × 10−6 5.90 × 10−6 −0.00005 −0.00005
lptpe 0.00017 0.00000 0.00000 −1.35 × 10−6 −0.00002 −0.00002 −0.00009* −0.00009
lpcbe 0.00079*** 0.00003*** −0.00000 3.31 × 10−7 −0.00005*** −0.00002** 0.00003 0.00010***
lpbwm 0.00121*** −0.00005* 0.00002 −0.00002 0.00006*** −5.90 × 10−6 −0.00008 −0.00028***
lplle −0.00028** −0.00002* −9.21 × 10−6 −2.45 × 10−7 −0.00007*** −0.00005*** −0.00005* 0.00001
lpbdg −0.00042** −0.00001 9.81 × 10−6 −4.19 × 10−6 −0.00002 −0.00003 7.44 × 10−6 0.00015**
Income −0.07450*** −0.00626*** −0.00547*** −0.01582*** −0.00379*** −0.00850*** −0.01370*** −0.02116***
R 2 0.6556 0.7159 0.6225 0.7686 0.6936 0.7696 0.6648 0.6613

*, **, *** are significant at 10, 5, 1% level. Wbr – budget share of rice; Wtr – budget share of wheat; Wjgk – budget share of skinned wet corn; Wjgp – budget share of shelled corn; Wskg – budget share of cassava; Wktg – budget share of potato; Wmie – budget share of instant noodles; Wnsi – budget share of white rice; Dlr – dummy location; lpart – size of household member; lpbr – rice price; lptr – wheat price; lpjgk – skinned wet corn price; lpjgp – shelled corn price; lpskg – cassava price; lpktg – potato price; lpmie – instant noodles price; lpnsi – white rice price; lpmgr – cooking oil price; lptlr – chiken egg price; lpaym – chiken meat price; lpspi – beef price; lpthu – tofu price; lptpe – tempeh price; lpcbe – chilli price; lpbwm – onion price; lplle – catfish price; lpbdg – processed milkfish price.

The results of the estimation of the AIDS model presented in Table 2 show that before and during the COVID-19 pandemic, the price factor significantly affected the consumption of all carbohydrate-source food commodities. The price coefficient is positive, indicating that when the price of carbohydrate-sourced food increases, the proportion of expenditure on that carbohydrate-sourced food increases. Household income is also a factor that significantly affects the consumption of all food commodities as a source of carbohydrates. The coefficient of household income is negative, indicating that when there is an increase in income, the proportion of expenditure on food as a carbohydrate source decreases. Bennett’s law states that at low-income levels, food consumption is prioritized on energy-dense foods derived from carbohydrates, and vice versa. When income increases, energy consumption from carbohydrates decreases [1].

Before and during the COVID-19 pandemic, the number of household members significantly positively affected the consumption of rice, wheat flour, shelled corn, instant noodles, and white rice. The number of household members also affects the consumption of cassava and potatoes during the pandemic. The positive coefficient of household members indicates that when there is an increase in the number of household members, the proportion of household expenditure on food commodities that are carbohydrate sources increases. This is because food sources of carbohydrates, especially rice, are staple foods, so when the number of household members increases, the amount that must be purchased will increase. The rice coefficient value during the pandemic (0.018821) was greater than in the pre-pandemic period (0.01292). This condition was caused during the COVID-19 pandemic, and there was an increase in prices, so household expenditure on rice increased.

Before the COVID-19 pandemic, the residence location significantly affected the consumption of rice and instant noodles. This means there are differences in the consumption of these commodities in rural and urban areas. The positive coefficient indicates that the proportion of spending on rice and instant noodles in urban areas is greater than in rural areas. During the COVID-19 pandemic, location factors significantly affected the consumption of rice, wheat flour, wet-skinned corn, cassava, potatoes, and instant noodles with a negative coefficient except for instant noodles which were positive. This shows that the expenditure on rice, wheat flour, wet-skinned corn, cassava, and potatoes is greater in rural areas than in urban areas. Meanwhile, the proportion of instant noodle expenditure is greater in urban areas.

Household consumption of carbohydrate food sources is also influenced by the prices of other commodities, namely cooking oil, chicken eggs, chicken meat, beef, tofu, tempeh, red chili, shallots, catfish, and preserved milkfish.

Before the COVID-19 pandemic, the price of cooking oil positively affected the consumption of wet-skinned corn, shelled corn, and cassava and negatively affected the consumption of instant noodles and white rice. Meanwhile, during the COVID-19 pandemic, cooking oil had a positive effect on wet-skinned corn and a negative effect on wheat flour and cassava. This shows that before the COVID-19 pandemic, when the price of cooking oil increased, the proportion of spending on wet-skinned corn, shelled corn, and cassava increased, and the proportion of spending on instant noodles and white rice decreased. Meanwhile, during the COVID-19 pandemic, when the price of cooking oil increased, the proportion of expenditure for wet-skinned corn, wheat flour, and cassava decreased.

The price of chicken eggs positively affected the consumption of wet-skinned corn, shelled corn, cassava, and instant noodles in the period before the COVID-19 pandemic. During the COVID-19 pandemic, the price of chicken eggs positively affected rice consumption and negatively affected the consumption of cassava and white rice. The positive effect shows that when there is an increase in the price of chicken eggs, the proportion of expenditure for these commodities increases. At the same time, the negative effect shows that when the price of chicken eggs increases, the proportion of expenditure on food commodities that are carbohydrate sources decreases. The price of chicken meat harms consuming food sources of carbohydrates before and after the COVID-19 pandemic. When the price of chicken meat increases, the proportion of expenditure on rice, wheat flour, wet-skinned corn, cassava, potatoes, and instant noodles decreases. Before the COVID-19 pandemic, the price of beef had a positive effect on the consumption of rice, wheat flour, and instant noodles.

Meanwhile, during the pandemic, the price of beef positively affected the consumption of rice, wheat flour, potatoes, and white rice and harmed the consumption of cassava. The positive effect shows that when there is an increase in the price of chicken meat, the proportion of expenditure for that commodity increases. Meanwhile, the negative effect indicates that when the price of chicken meat increases, the proportion of expenditure on these food commodities decreases.

Before the COVID-19 pandemic, the price of tofu did not affect the consumption of food sources of carbohydrates. During the COVID-19 pandemic, the price of tofu harmed rice consumption. A negative coefficient indicates that if there is an increase in the price of tofu, the proportion of spending on rice will decrease. Before the COVID-19 pandemic, the price of tempeh had a positive effect on rice consumption and a negative effect on the consumption of wet-skinned and shelled corn. This shows that when the price of tempeh increases, the proportion of rice expenditure increases, while the proportion of expenditure on wet and shelled corn decreases. Meanwhile, during the pandemic, the price of tempeh negatively affected the consumption of instant noodles, which means that when the price of tempeh increases, the proportion of spending on instant noodles decreases.

The price of red chili harms the consumption of rice, wheat flour, shelled corn, and cassava in the period before the COVID-19 pandemic. This means that when the price of chili increases, the proportion of expenditure on rice, wheat flour, shelled corn, and cassava decreases. Meanwhile, during the COVID-19 pandemic, the price of red chili positively affected the consumption of rice, wheat flour, and white rice and harmed the consumption of cassava and potatoes. During the COVID-19 pandemic, the price of shallots positively affected the consumption of wet-skinned corn and shelled corn and harmed the consumption of potatoes and instant noodles. Meanwhile, during the COVID-19 pandemic, the price of shallots positively affected the consumption of rice and cassava and negatively affected the consumption of wheat and white rice. The positive effect shows that when the price of shallots increases, the proportion of expenditure on these commodities increases. At the same time, the negative effect shows that when the price of shallots increases, the proportion of expenditure on food commodities that are carbohydrate sources decreases.

Before and during the COVID-19 pandemic, catfish prices negatively affected the consumption of rice, cassava, potatoes, wheat flour, and instant noodles. This shows that when there is an increase in the price of catfish, the proportion of expenditure on rice, wheat flour, cassava, potatoes, and instant noodles decreases. Before the COVID-19 pandemic, the price of processed milkfish negatively affected rice and potato consumption. Meanwhile, during the COVID-19 pandemic, processed milkfish prices harmed rice consumption and positively affected white rice consumption. This means that when the price of processed milkfish increases, the proportion of expenditure on rice decreases, while that for white rice increases.

3.3 Elasticity of food consumption of carbohydrate sources

Changes in the price of goods, income, and prices of other related goods, either substitutes or complements, will affect the demand for goods with different effects for each commodity. The sensitivity of consumption of carbohydrate-source foods to these changes is measured by own-price elasticity, expenditure elasticity, and cross-price elasticity.

Table 3 shows the price and income elasticity for each carbohydrate-source food commodity studied under conditions before and during the COVID-19 pandemic.

Table 3

Own-price elasticity and income elasticity

Carbohydrate-source food Own-price elasticity Income elasticity
Before pandemic During pandemic Before pandemic During pandemic
Rice −0.0553 0.0996 0.5106 0.4142
Flour 1.3415 1.4550 −0.0645 −0.1546
Skinned wet corn 18.4225 19.4401 −7.2873 −7.9520
Shelled corn 132.2022 67.9870 −60.9018 −31.9252
Cassava 2.8619 1.4233 −0.5405 0.0475
Potato 2.9624 3.9349 −0.8291 −1.4034
Instant noodles 0.5774 0.8260 0.3539 0.2306
White rice 5.8249 5.8191 −1.8704 −1.9442

According to economic theory, the price elasticity of a commodity is expected to have a negative sign, which indicates a negative slope on the demand curve. Table 3 shows that prior to the COVID-19 pandemic, the Own-Price elasticity for rice was negative, while for other carbohydrate-source food commodities, it was positive. During the COVID-19 pandemic, the Own-Price elasticity for all carbohydrate-source food commodities was positive.

The Own-Price elasticity of rice commodity has a negative sign of 0.0553, indicating that when there is an increase in the price of rice by 1%, the consumption of rice decreases by 0.0553%. An elasticity value of less than 1 indicates that rice consumption is inelastic. Rice is a staple food consumed by all households, so price changes do not affect rice consumption much. This result is in line with previous research, which shows that rice consumption is inelastic, namely the elasticity of rice prices in Java is −0.424 [47]; rice price elasticity of −0.42 for high-income households and −0.87 for low-income households [50]; the price elasticity of rice in East Java is below 0.5 [40]; and the price elasticity of rice in South Sumatra is −0.59 [51].

The price elasticity of Own-Price for food commodities with a positive sign indicates that when prices fall, the consumption of food sources of carbohydrates decreases and vice versa. This condition is an exception to the normal conditions of the law of demand. Two types of items exhibit this behavior: Veblen items and Giffen items. Veblen goods are a status symbol, and their exclusivity makes them desirable, characterized by their price elasticity and their income elasticity is positive. While Giffen goods are “inferior” goods, the price elasticity is positive [52].

The results of the income elasticity analysis show that before and during the COVID-19 pandemic, wheat flour, wet-skinned corn, shelled corn, potatoes, white rice, and cassava in the pre-pandemic period were inferior goods because the income elasticity was negative. These commodities are Giffen goods if it is associated with their positive price elasticity. This also happened to cassava before the pandemic. When prices fall, consumption of these commodities also decreases because households allocate income to food sources of carbohydrates that have higher social value, such as rice. This is because households will allocate the Giffen paradox phenomenon that has occurred in Ireland for potato consumption [53].

Income elasticity for rice and instant noodles before and during the pandemic and cassava during the pandemic was positive, indicating that all three were normal goods. An elasticity value of less than 1 indicates that all three staple goods are. If it is associated with the value of the price elasticity itself, which is positive, it indicates that the three commodities are Veblen goods. Veblen [54] reveals that a person’s satisfaction in consuming is not seen by how much benefit is felt from the goods consumed but by how much the assessment of others is the effect of consuming the item or can be interpreted as a show-off effect (Veblen Effect). During the pandemic, household purchasing power decreased, so consuming rice and instant noodles was more prestigious than cheaper carbohydrate sources.

Table 3 also shows that before and during the pandemic, the Own-Price elasticity of all carbohydrate-source foods except rice was greater than the income elasticity. This means that price changes than income changes more influence the consumption of food sources of carbohydrates other than rice. Meanwhile, rice consumption is more influenced by changes in income.

Cross-price elasticity shows the percentage change in the number of goods consumed due to changes in prices of other related commodities, cateris paribus. Cross-price elasticity is important because consumers will adjust the composition of the goods purchased if there is a change in the price of other related goods. A positive cross-price elasticity value indicates that the two commodities are substitutes, while a negative cross-price elasticity value indicates the complementary relationship between the two commodities. The cross-price elasticity of consumption of food sources of carbohydrates before and during the COVID-19 pandemic is presented in Table 4.

Table 4

Cross-price elasticity of the pandemic

Commodity Rice Flour Skinned wet corn Shelled corn Cassava Potato Instant noodles White rice
Before COVID-19 pandemic
Rice — — 1.13843 4.18723 — 0.30573 0.11252 0.01270
Flour 0.00516 — 0.21588 0.00064 — — —
Skinned wet corn — — — — — 0.00143 — —
Shelled corn — — 0.04946 — — —
Cassava — — — 0.17882 — 0.01643 — —
Potato — 0.00412 — — — — —
Instant noodles 0.00726 0.02056 0.13600 0.54972 — 0.02648 —
White rice –0.00485 — –0.05684 –0.13512 — — —
Cooking oil — — 0.21893 0.83618 0.02574 — 0.01143 0.03838
Chicken egg — — 0.25476 0.85064 0.03276 — 0.02684 —
Chicken meat 0.01079 0.02496 0.21035 — 0.01303 0.05077 0.01775 —
Beef 0.00687 0.01120 — — — –1.14337 —
Tofu — — — — — — —
Tempe — — 0.00758 0.00519 — — —
Chilli 0.00098 0.00237 — 0.09717 −0.00472 — —
Onion — — 0.04026 0.14802 — 0.01073 0.00322 —
Catfish 0.00334 — — — −0.00322 0.00371 — —
Processed milkfish –0.00085 — — — — −0.00787 — —
During COVID-19 pandemic
Rice — — — — 0.14367 — 0.11300 −0.08434
Flour 0.00723 — 0.02040 — — 0.00755 0.00691 0.00663
Skinned wet corn — — — — −0.00907 — — —
Shelled corn −0.01203 — — — 0.04851 — — —
Cassava −0.00047 — — 0.09681 — — 0.00604 —
Potato — 0.01284 — — — — — —
Instant noodles — — — — −0.00320 0.03445 — —
White rice — — — — −0.02769 — −0.00075 —
Cooking oil — 0.02491 0.27145 — 0.01224 — — —
Chicken egg 0.01956 — — — 0.01050 — — 0.06117
Chicken meat 0.01469 — 0.24922 0.95466 0.01396 0.06535 0.01811 —
Beef 0.01272 0.01419 — — −0.00981 0.02663 — 0.03373
Tofu −0.00026 — — — — — — —
Tempe — — — — — — 0.00727 —
Chilli 0.01244 0.01805 — — −0.00163 0.02023 — 0.02691
Onion 0.01855 0.00925 — — 0.01472 — — 0.00656
Catfish 0.00280 0.00611 — — −0.01041 0.00708 0.00375 —
Processed milkfish −0.00207 — — — — — — 0.02695

Table 4 shows that the cross-price elasticity between food commodity sources of carbohydrates before the pandemic was positive, except for white rice, which was negative. The positive cross-price elasticity indicates that the carbohydrate-source food commodities have a substitution relationship. This is in line with the study’s results [22] that wheat flour and instant noodles are substitutes for rice. The cross-elasticity values of wheat flour and instant noodles to rice were 0.005 and 0.007. This means that for every 1% increase in flour prices, rice consumption will increase by 0.005%, and when the price of instant noodles rises by 1%, rice consumption will increase by 0.007%. The cross-price elasticity of rice to instant noodle consumption is 0.1125, which means that every 1% increase in rice price will increase the consumption of instant noodles by 0.1125%. This shows that changes in rice prices have more effect on instant noodle consumption than changes in instant noodle prices on rice consumption. Rice, as the Indonesian population’s staple food, makes it less affected by changes in food prices for other carbohydrate sources. The negative cross-elasticity of white rice indicates that white rice has a complementary relationship with rice, wet-skinned corn, and shelled corn.

During the COVID-19 pandemic, more carbohydrate-source food commodities were complementary to other carbohydrate-source commodities than in the pre-pandemic period. Shelled corn and cassava are complementary to rice. Wet-skinned corn is a complement to cassava. Instant noodles complement cassava, and white rice complements cassava and instant noodles. Rice is a complement to instant noodles. During the pandemic, these three commodities were consumed to complement each other. Limited incomes and price increases during the pandemic have made the consumption of food sources of carbohydrates consumed as a complement.

Before and during the COVID-19 pandemic, most of the other food commodities, namely chicken eggs, chicken meat, beef, red chilies, shallots, and catfish, had a positive cross-price elasticity value, meaning that these commodities were substitutes for carbohydrate-source foods. When the prices of these commodities rise, their consumption decreases and they are replaced by food sources of carbohydrates. Meanwhile, preserved tofu and milkfish are complementary goods for rice. Research [33] found that during the COVID-19 pandemic, low-income households (quintiles I, II, and III) reduced their demand for nutritious food and consumed more carbohydrate-sourced foods.

4 Conclusions

There were differences in consumption patterns of food sources of carbohydrates before and during the COVID-19 pandemic. The food consumption pattern of carbohydrate sources in this study was seen based on the proportion of expenditure on food sources of carbohydrates to total food expenditure. Household allocates higher consumption expenditures for rice compared to other commodities. During the COVID-19 pandemic, the proportion of expenditure on rice, wheat flour, shelled corn, cassava, and potatoes increased, while for wet corn, instant noodles, and white rice, they decreased.

Before and during the COVID-19 pandemic, price and income significantly affected the consumption of all carbohydrate-source food commodities. The price coefficient itself is positive, while the income coefficient is negative. The number of household members positively affects the consumption of most carbohydrate foods both before and during the study. During the COVID-19 pandemic, the location of residence affected the consumption of rice, wheat flour, wet-skinned corn, cassava, potatoes, and instant noodles, with a negative coefficient except for instant noodles.

Own-price and income elasticity show that before the pandemic, rice was a normal good with consumption being inelastic. Before and during the COVID-19 pandemic, wheat flour, wet-skinned corn, shelled corn, potatoes, white rice, and cassava in the pre-pandemic period were Giffen goods because the price elasticity itself was positive and the income elasticity was negative. Meanwhile, rice during the pandemic, instant noodles before and during the pandemic, and cassava were Veblen goods because their price and income elasticity were positive. The cross-price elasticity shows that before the pandemic, most of the relationships between food sources of carbohydrates were substitutes, meanwhile during the COVID-19 pandemic, more carbohydrate-source food commodities were complementary to other carbohydrate-source commodities. Before and during the COVID-19 pandemic, chicken eggs, chicken meat, beef, red chilies, shallots, and catfish were substitutes for food sources of carbohydrates. Tofu and preserved milkfish are complementary to rice during the pandemic.

Before and during the pandemic, rice was the main source of carbohydrates, and the consumption was influenced by prices and income. During the COVID-19 pandemic, more carbohydrate-source food commodities were complementary to other carbohydrate-source commodities. That means during a pandemic, the consumption of carbohydrates is more, which means that the quality of food is reduced. So effort is needed from the government to maintain the stability of prices of food in the market and to increase income in order to increase the quantity and quality of food consumption.

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Acknowledgments

The authors would like to thank to Universitas Sebelas Maret and the Central Statistic Agency of Central Java for their supports.

  1. Funding information: This article is part of a dissertation research called Consumption and Household Food Security in Central Java which Sebelas Maret University funds.

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

  3. Data availability statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Received: 2022-12-07
Revised: 2023-01-22
Accepted: 2023-03-13
Published Online: 2023-03-30

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

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

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  22. Soil fertility and pomelo yield influenced by soil conservation practices
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  30. Small-scale coffee farmers’ perception of climate-adapted attributes in participatory coffee breeding: A case study of Gayo Highland, Aceh, Indonesia
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  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
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  74. Review Articles
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  76. Black pepper (Piper nigrum Lam) as a natural feed additive and source of beneficial nutrients and phytochemicals in chicken nutrition
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  83. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part I
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  89. Special Issue on Agriculture, Climate Change, Information Technology, Food and Animal (ACIFAS 2020)
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https://doi.org/10.1515/opag-2022-0188
Keywords for this article
AIDS; elasticity; food consumption pattern; share of food expenditure
Creative Commons
BY 4.0

Articles in the same Issue

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  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
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  11. Factors influencing the role and performance of independent agricultural extension workers in supporting agricultural extension
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  14. Diallel analysis of length and shape of rice using Hayman and Griffing method
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  16. Correlation between descriptive and group type traits in the system of cow’s linear classification of Ukrainian Brown dairy breed
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  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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