Home Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia
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Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia

  • Wulansari Winahyu EMAIL logo , Endang Siti Rahayu , Dwidjono Hadi Darwanto and Mohamad Harisudin
Published/Copyright: December 13, 2024

Abstract

Increasing farmers’ income is possible through high-value markets. Employment, access to financing, and technical support have favorable effects and a rise in income. This research seeks to establish the sustainability of organic cabbage farming through economic performance analysis. Quantitative research methods were conducted in this research. Descriptive quantitative analysis was performed with PLS-SEM software. The research data comprise primary data and secondary data. Of the 11 pathway coefficients in this study, only 9 were significant. The protection variable was positively related to the production optimization construct but did not significantly affect financial performance. The variable “healthy and safe” was positively associated with significant constructs of “financial performance” and “production optimization.” The variable “capacity development” was positively related to the constructs of “financial performance” and “production optimization.” The “technical” variable was significantly positively associated with the construct of “financial performance” but did not significantly affect the construct of “optimization of production.” The variable “production optimization” is positively related to the construct of “financial performance” and the construct of “farm performance” of farmers. Agricultural extension and training must be carried out to improve farmers’ technical knowledge. Government subsidies to support organic farming are also needed to ensure the sustainability of organic cabbage farming in the Semarang district.

1 Introduction

Modern farming uses high-energy inputs, especially chemical pesticides and inorganic fertilizers. Chemical pesticides and fertilizers greatly increased food and farming production when they were first used. Organic farming is a potential way to produce food with minimal environmental impact [1]. Environmental issues are the main reason for the establishment of organic agriculture. Using fertilizers and post-harvest management with chemicals is strongly discouraged, followed by rules limiting what can and cannot be done in cultivating organic vegetables, including product certification [2].

Organic agriculture uses natural inputs and no synthetic chemicals to reduce chemical inputs or water, soil, and air pollution residues to produce healthy products and a sustainable, harmonious, and balanced environment. Organic cultivation might be simple to execute in some situations. In China, approximately 36% of the farmers are willing to adopt organic cultivation. Farmer awareness of organic farming’s benefits and government backing make this adoption good. Farmers understand the advantages of organic farming for both themselves and the government’s backing [3]. Organic farms can generate far larger profits from sales, indicating that their economic circumstances are better [4]. Organic farming based on zoning is gaining popularity in Indonesia due to its ability to produce nutritious food, enhance land quality and environmental conditions, and improve farmers’ well-being [5].

To achieve the sustainability of organic agriculture, several elements still need to be adequately pursued, such as merging environmental and social concerns and enhancing the financial performance of farmers. For example, Hourneaux et al.’s [6] research explains that ecological, material, energy, and water factors are the indicators that have the highest priority in measuring production costs. Social factors are indicators of labor capacity development because organic agriculture requires denser work and has the potential to contribute to long-term employment in rural areas. Many indicators have indeed been produced to evaluate sustainability. The article assumes that sustainable development is not based on economic, social, ecological, or institutional dimensions but on its system, which is an integrated system [7]. Thus, sustainable development is not about the choice between environmental protection and social progress but rather the effort to achieve economic and social development harmoniously with environmental protection [8].

One food that can boost energy and keep you healthy is cabbage due to its high mineral and vitamin content. Compared to other horticultural crops, its short harvest season is another reason for its popularity. Cabbage is readily available in stores and markets because of its high production rate. Cabbage grows best at 200–2,000 m above sea level at 10–24°C, with an optimal value of 17°C [9]. The quantity of pests, parasites, and detrivores is greater in organically grown cabbage agroecosystems than in conventionally grown cabbage agroecosystems. Organic cabbage cultivation has a higher arthropod diversity index than conventional cultivation techniques. There was no significant difference in the attack rate of Plutella xylostella F. on organic and conventional growing practices [10]. Organic fertilizers can reduce mace root disease by 19.06–57.01% but can also increase insect attacks by 12.12–27.5%. The conclusion is that the organic fertilizer Kotciplus is ideal for cabbage production [11].

People are looking for livelihoods by the natural resources around where they live. The economic sustenance of small-scale farmers in developing and emerging economies mostly relies on agricultural activities, which serve as the primary source of income in rural regions [12]. Therefore, increasing small-scale farming is important for lowering poverty, making sure there is enough food for everyone, and growing the economy [13]. Smallholders must have access to the market to enhance livelihoods, augment incomes, and foster local economic development [14]. Smallholders can engage in the market if they can compete with other suppliers and meet customer needs.

Increasing farmers’ income is possible through high-value markets. In addition to improved revenue, employment, access to financing, and technical support have favorable consequences. Market participation by smallholder farmers increases incomes by reducing transaction costs and providing improved technology to farmers [8]. Improving market access is at the core of smallholder agriculture development efforts for poverty alleviation. Djokoto et al. [15] suggested that development interventions should consider the relationship between smallholder production and potential markets. How smallholders can compete with other providers and meet customer requests determines their market participation potential. According to Rahman et al. [16], management techniques and managerial talents influence farmers’ financial success.

Due to their limited assets, farmers require an organization to help them process and market their products. Many smallholders need more resources to access loans, develop production processes, and explore new markets [12]. The high transaction costs that farmers face due to living in rural places with inadequate infrastructure greatly diminish the chances that they will participate in market activities [13]. Individuals and social structures like family, friends, and communities struggle to overcome these challenges. Therefore, all small organic farmers are included in one farmer group. Farmer groups share information, learning opportunities, cooperate, and business units.

One of the issues facing farmers is the dearth of financial institutions capable of offering soft loans assistance. In contrast, to meet market demands, farmers must build their capacity to produce safe and healthful organic vegetables. Farmers protect their vegetables by growing crops organically to get optimal production results. Production optimization is expected to improve financial performance while improving farmer performance. This study used subjective performance assessments. Performance measures can be subjective or objective [12,17]. Finance is often the focal point of an objective performance approach. The objective approach focuses on the absolute value of performance measurements (e.g., profit per employee) [18]. Respondents evaluate their success using multiple elements and non-financial criteria using the subjective approach.

Here are some similar studies that have been conducted. Wijaya et al.’s [19] research concluded that the variables of education, experience, and characteristic innovation significantly increase the likelihood of vegetable farmers adopting organic farming systems. Asfawi et al. [20] also showed that the benefit ratio of horticultural organic farming in Semarang Regency was higher (3.06) compared to the benefits of conventional agricultural cultivation (1.73) with significant differences. Menada et al. [21] stated that the highest cabbage farming income was observed during the dry season. Age, farming experience, and land area influenced the risks associated with cabbage farm income. Research from Mdoda et al. [22] showed that cabbage farmers make generally efficient profits but can increase them up to 78.84%, indicating that there is still an outstanding possibility to increase profits by increasing the technical efficiency of smallholders. Several earlier research studies have not investigated organic cabbage farmers’ performance. This study aims to realize the sustainability of organic cabbage farming through analysis of farmer performance by identifying variables that directly and indirectly affect the performance of organic farmers in Getasan District, Semarang Regency.

2 Methodology

This study used quantitative methods. Descriptive quantitative analysis is performed with path models. This research data consists of primary data and secondary data. Primary data is a type of data obtained by researchers directly through interviews and observations with the distribution of questionnaires so that they can find out how real conditions are in the field. For the purpose of data gathering, face-to-face interviews that are based on standardized questionnaires are utilized. This method is suitable for usage in rural areas with inadequate infrastructure, restricted access to online media, and low literacy rates. Interviews were scheduled in the farmer group’s office, the respondent’s house, or the farm. A Likert scale with five points, from 1 to 5, was used for the questions.

Sampling in stages based on the area in Semarang Regency. The sampling steps in this study are as follows: First, determine the farmer groups in each village. Getasan sub-district has 13 villages, with 8 of them having farmer groups that are actively certified organic vegetables from INOFFICE. Purposive selection of farmer groups in farmer groups that actively cultivate cabbage organically. The total sample population of organic cabbage farmers from farmer groups in Batur, Wates, Tajuk, and Kopeng villages was 92 organic cabbage farmers. The approach uses partial least squares structural equation modeling (PLS-SEM) to estimate partial model structure by combining principal component analysis with ordinary least square regression. Social science researchers appropriately use PLS-SEM to test and explain relationships between variables, accept or reject hypotheses, and predict outcome variables [23].

PLS is a technique that can establish a connection between a group of free variables and many bound variables. In the case of predictors that exhibit multicollinearity, PLS can manage a large number of independent (free) variables. As a regression model, PLS forecasts one or more of multiple sets of independent variables [24]. Figure 1 shows the SEM model factors affecting the performance of organic vegetable farmers in Semarang Regency.

Figure 1 
               SEM model factors affecting the performance of organic vegetable farmers in Semarang Regency.
Figure 1

SEM model factors affecting the performance of organic vegetable farmers in Semarang Regency.

  1. Consent: Farmers/respondents are informed that their participation is completely voluntary. Unless explicit permission is obtained to share it, all answers and personal information gathered are kept private. Farmers were also briefed on the study's specific objective: to evaluate agricultural performance to assess sustainability and how their input will help achieve this objective. Farmers/respondents were told they may withdraw from the research without repercussions. Benefits and possible risk factors have been explained.

3 Result

3.1 Evaluation of outside models (reflective measurements)

Table 1 shows all indicator loadings valued above 0.708. The lowest loadings indicator value is 0.898 in indicator P6, while the highest is 0.981 in the FFP2 indicator. A high loadings indicator value (>0.708) indicates that related indicators have much in common, which is captured by the construct. All indicators make an absolute contribution to the assigned construct.

Table 1

Test validity and reliability on conceptual models

Construct (latent variable) Indicators Loadings indicator Reliability indicators ( λ ) Cronbach’s α CR AVE
Protection (X 1) P1 0.939*** 0.882 0.973 0.977 0.897
P2 0.920*** 0.847
P3 0.943*** 0.889
P4 0.936*** 0.875
P5 0.947*** 0.896
P6 0.898*** 0.807
P7 0.912*** 0.832
Healthy and safe (X 2) HS1 0.951*** 0.905 0.939 0.961 0.891
HS2 0.931*** 0.867
HS3 0.949*** 0.901
Capacity building (X 3) CB1 0.903*** 0.815 0.931 0.956 0.879
CB2 0.939*** 0.881
CB3 0.970*** 0.941
Technical (X 4) T1 0.926*** 0.857 0.972 0.978 0.900
T2 0.949*** 0.900
T3 0.974*** 0.949
T4 0.962*** 0.926
T5 0.932*** 0.869
Financial performance (Y 1) FP1 0.944*** 0.890 0.962 0.972 0.897
FP2 0.945*** 0.893
FP3 0.946*** 0.894
FP4 0.954*** 0.911
Production optimization (Y 2) PO1 0.946*** 0.895 0.948 0.963 0.866
PO2 0.937*** 0.879
PO3 0.929*** 0.863
PO4 0.908*** 0.825
Farmer farm performance (Y 3) FFP1 0.970*** 0.941 0.961 0.975 0.928
FFP2 0.981*** 0.963
FFP3 0.938*** 0.880

***Significant at 1%.

Table 1 shows all reliability indicator values above 0.50. The lowest reliability indicator value is 0.807 in the P6 indicator, while the highest is 0.963 in the FFP2 indicator. For the protection construct (X 1), the lowest reliability indicator value of 0.807 (P6) means that the latent variable (construct) of protection can explain the variance of the indicator “Plant diversity is increasing” (P6) by 80.7%. In comparison, the protection construct cannot explain 19.3% of the variance of the indicator. In other words, 19.3% of the variance of the P1 indicator comes from measurement error. The highest reliability indicator value of 0.896 (P5) means that the protection construct can explain the variance of the “protect local products” indicator (P5) of 89.6%. The protection construct cannot explain 10.4% of the indicator variance.

The reliability indicator value for the lowest healthy and safe construct (X 2) is 0.867 on the HS2 indicator, meaning that the latent variable (construct) healthy and safe can explain the variance of the indicator “Organic vegetables provide better taste” (HS2) by 86.7%. In comparison, healthy and safe constructs cannot explain 13.3% of the variance of the indicator healthy and safe constructs cannot explain 13.3% of the variance of the indicator. The highest reliability indicator value of 0.905 on the HS1 indicator means that the latent variable (construct) healthy and safe can explain the variance of the indicator “Healthy consumed and does not cause harm to the body” (HS1) by 90.5%. In comparison, the healthy and safe construct cannot explain 9.5% of the variance of the indicator. In other words, 9.5% of the variance of the HS1 indicator comes from measurement error.

For the capacity development construct (X 3), the lowest reliability indicator value is 0.815 in the CB1 indicator, meaning that the latent variable (construct) of capacity development can explain the variance of the indicator “Hereditary acquired farmer knowledge” (CB1) of 81.5%. In comparison, the capacity development construct cannot explain 18.5% of the variance of the indicator. In other words, 18.5% of the variance of the CB1 indicator comes from measurement error. The highest reliability indicator value is 0.941 on the CB3 indicator, meaning that the latent variable (construct) of capacity development can explain the variance of the indicator “how much respect for local culture” (CB3) of 94.1%. In comparison, the construct of capacity development cannot explain 5.9% of the variance of the indicator. In other words, 5.9% of the variance of the CB3 indicator comes from measurement error.

The lowest reliability indicator value for the technical construct (X 4), valued at 0.857 in the T1 indicator, means that the latent variable (construct) Technical can explain the variance of the indicator “Can reduce tillage” (T1) by 85.7%. In comparison, the Technical construct cannot explain 14.3% of the variance of the indicator. In other words, 14.3% of the variance of the T1 indicator comes from measurement error. The highest reliability indicator value is 0.949 in the T3 indicator, meaning that the technical latent variable (construct) can explain the variance of the indicator “Not using chemical fertilizers” (T3) of 94.9%. In comparison, the technical construct cannot explain 5.1% of the variance of the indicator. In other words, 5.1% of the variance of the T3 indicator comes from measurement error.

The lowest reliability indicator value for the financial performance construct (Y 1), valued at 0.890 in the FP1 indicator, means that the latent variable (construct) of financial performance can explain the variance of the “Increasing farmer assets” indicator (FP1) by 89.0%. In comparison, the financial performance construct cannot explain 11.0% of the variance of the indicator. In other words, 11.0% of the variance of the FP1 indicator comes from measurement error. The highest reliability indicator value is 0.911 in the FP4 indicator, meaning that the latent variable (construct) of financial performance can explain the variance of the indicator “Increase net profit” (FP4) by 91.1%. In comparison, the financial performance construct cannot explain 8.9% of the variance of the indicator. In other words, 8.9% of the variance of the FP4 indicator comes from measurement error.

The production optimization (Y 2) construct has a minimum reliability indicator value of 0.825 on the PO4 indicator. This means that the latent variable (construct) of production optimization can explain the variance of the indicator “Obtaining higher productivity during drought” (PO4) of 82.5%. The production optimization construct cannot explain 17.5% of the indicator variance. In other words, 17.5% of the variance of the PO4 indicator comes from measurement error. The highest reliability indicator value of 0.895 in the PO1 indicator means that the latent variable (construct) of production optimization can explain the variance of the indicator “Increasing efficiency in areas with low input” (PO1) by 89.5%. In comparison, the production optimization construct cannot explain 10.5% of the variance of the indicator. In other words, 10.5% of the variance of the PO1 indicator comes from measurement error.

The lowest reliability indicator value of the farmer farm performance construct (Y 3) is 0.880 in the FFP3 indicator, meaning that the latent variable (construct) of farmer performance can explain the variance of the indicator “There is an improvement in house quality” (FFP3) by 88.0%. In comparison, the farmer performance construct cannot explain 12.0% of the variance of the indicator. In other words, 12.0% of the variance of the FFP3 indicator comes from measurement error. The highest reliability indicator value of 0.963 in the FFP2 indicator means that the latent variable (construct) of farmer performance can explain the variance of the indicator “Have enough food throughout the year” (FFP2) of 96.3%. In comparison, the farmer’s performance construct cannot explain 3.7% of the variance of the indicator. In other words, 3.7% of the variance of the FFP2 indicator comes from measurement error.

The third evaluation is the reliability of internal consistency, assessed using Cronbach’s α and composite reliability (CR). Table 1 shows the lowest Cronbach’s α and CR values in the development and capacity constructs, while the highest Cronbach’s α and CR values are in the protection construct. Cronbach’s α and CR values greater than 0.7 indicate that all constructs in this study have higher internal consistency.

The fourth evaluation is convergent validity. Table 1 shows the lowest AVE value in the production optimization construct and the highest in the farmer performance construct. The AVE value of 0.866 contained in the production optimization construct indicates that the production optimization construct can explain 86.6% of the indicator variance. In comparison, 13.4% of the indicator variance comes from measurement error. The AVE value of 0.900 contained in the technical construct indicates that the technical construct can explain 90.0% of the indicator variance, while 10.0% of the indicator variance comes from measurement error. The AVE value of 0.891 found in the healthy and safe construct indicates that the healthy and safe construct can explain 89.1% of the indicator variance.

By contrast, measurement error accounts for 10.9% of the variance of the indicator. The AVE value of 0.897 contained in the financial performance construct indicates that the financial performance construct can explain 89.7% of the indicator variance. About 10.3% of the indicator variance is attributed to measurement error. The AVE value of 0.928 contained in the farmer performance construct indicates that the farmer performance construct can explain 92.8% of the indicator variance. About 7.2% of the variance in the indicator is attributed to measurement error. The AVE value of 0.897 in the protection construct indicates that the production optimization construct can explain 89.7% of the indicator variance. About 10.3% of the indicator variance is attributed to measurement error when compared. The AVE value of 0.879 contained in the capacity development construct indicates that the capacity development construct can explain 87.9% of the indicator variance. In comparison, 26.5% of the indicator variance comes from measurement error.

3.2 Evaluation of the inner model (structural)

3.2.1 Path coefficient and multicollinearity

Avoiding significant correlations between constructs (multicollinearity) that will cause methodological and interpretive issues is critical. When two predictor constructs have nearly similar meanings and construct scores (highly correlated), the PLS-SEM algorithm, like multiple regression, has trouble estimating models with such data. The recommended VIF value is below 5. Table 2 shows all VIF values smaller than 5. Thus, this study can establish that there is no multicollinearity between latent variables in structural models.

Table 2

Path coefficients and VIFs on structural models

Coefficient T Statistic p-Value VIF
Protection → financial performance 0.030 0.416 0.339 3.590
Protection → production optimization 0.395 4.110 0.000*** 3.048
Healthy and safe → financial performance 0.135 2.014 0.022** 3.342
Healthy and safe → production optimization 0.248 2.395 0.009*** 3.128
Capacity building → financial performance 0.328 2.895 0.002*** 3.643
Capacity development → production optimization 0.210 1.698 0.045** 3.489
Technical → financial performance 0.271 4.223 0.000*** 2.712
Technical → production optimization 0.074 0.878 0.190 2.693
Production optimization → financial performance 0.221 2.656 0.004*** 3.473
Production optimization → farmer performance 0.430 3.499 0.000*** 2.602
Financial performance → farmer performance 0.239 1.805 0.036** 2.602

***Significant at 1%, **Significant at 5%.

Path coefficients and normalized β coefficients are comparable in regression analysis. The hypothesis’s significance level is evaluated by utilizing the value of β.

Table 2 shows the 11 path coefficients in this study. However, only nine path coefficients are significant at 1–5%. More precisely, the latent protection variable is positively related to a significant production optimization construct at 1%. However, the latent protection variable has no significant effect on financial performance. Then, healthy and safe latent variables are positively related to the construct of financial performance and significant production optimization at 5 and 1%.

Furthermore, the latent variables of capacity development are positively related to the construct of financial performance and significant production optimization at 1 and 5%. Specifically technical, this variable is positively related to the construct of significant financial performance at 1%. However, the latent technical variable only significantly affects the construct of production optimization. Furthermore, the latent endogenous variable of production optimization is positively related to the endogenous construct of financial performance and the endogenous construct of farmer-farm performance, which is significant at 1%. Finally, the latent endogenous variable of financial performance is positively related to the endogenous construct of farmer-farm performance, which is significant at 5%.

3.2.2 R 2, Q 2, and f 2 value measurement

Table 3 shows that the five independent variables (protection, healthy and safe, capacity development, technical, and production optimization) were able to explain the variance in financial performance of 78.2% (R 2 = 0.782). The four independent variables (protection, healthy and safe, capacity development, and technical) could explain the variance of production optimization by 71.2% (R 2 = 0.712). The latent variables of financial performance and production optimization explain the high variance, which is 40.3% in farmer performance (R 2 = 0.403).

Table 3

Predictive relevance and accuracy of conceptual model predictions

Endogenous construct R 2 Q 2 Relationship F 2 Size of influence
Financial performance 0.782 0.691 Protection → financial performance 0.001 No influence
Healthy and safe → financial performance 0.025 Weak
Capacity building → financial performance 0.136 Weak
Technical → financial performance 0.124 Weak
Production optimization → financial performance 0.064 Weak
Production optimization 0.712 0.600 Protection → production optimization 0.178 Medium influence
Healthy and safe → production optimization 0.068 Weak
Capacity development → production optimization 0.044 Weak
Technical → production optimization 0.007 No influence
Farmer farm performance 0.403 0.371 Production optimization → farmer performance 0.119 Weak
Financial performance → farmer performance 0.037 Weak

R 2: Coefficient of determination; Q 2: Predictive relevance of the model; f 2: Size of influence.

Table 3 shows Q 2 values for endogenous latent variables financial performance, production optimization, and farmer farm performance of 0.691, 0.600, and 0.371, respectively. This value means that financial performance, farmer business performance, and production optimization are in the big predictive relevance category.

Capacity development, healthy and safety, production optimization, and technical constructs have a weak influence on the construct of financial performance. However, the Protection construct does not affect the financial performance construct. Furthermore, the capacity building and healthy and safe constructs weakly influence the production optimization construct.

In contrast, the protection construct moderately influences the production optimization construct. However, the technical construct does not affect the production optimization construct. Finally, the construct of financial performance and optimization of production have a weak influence on the construct of farmer performance. In conclusion, the results of the R 2, Q 2, and f 2 tests show that the structural model is appropriate in this study.

3.3 Fit model evaluation

Although PLS-SEM was initially designed for predictive purposes, research has sought to expand its ability for theory testing by developing fit model measures.

Table 4 shows the mean value of AVE, average R 2, and value of GoF. The average AVE value was 0.889, and the average R 2 value was 0.632. Based on the multiplication between the AVE and R 2 values, which are then rooted, it produces a GoF value of 0.750. The GoF value means that the model proposed in this study can account for 75% of the suitability that can be achieved.

Table 4

Goodness-of-fit (GoF) index calculation

Construct AVE R 2
Protection (X 1) 0.861
Healthy and safe (X 2) 0.891
Capacity building (X 3) 0.879
Technical (X 4) 0.900
Financial performance (Y 1) 0.897 0.782
Production optimization (Y 2) 0.866 0.712
Farmer farm performance (Y 3) 0.928 0.403
Average grades 0.889 0.632
AVE × R 2 0.562
GoF 0.750

Table 5 shows an SRMR value of 0.068. Therefore, the SRMR value shows that the conceptual model proposed in this study is suitable. The results of the relationship between construct components and performance measures were also confirmed using structural equation modeling (Figure 2).

Table 5

Standardized root mean square residual (SRMR) index

Value
SRMR 0.068
d_ULS 1.999
d_G 5.566
Chi-square 1760.246
NFI 0.672
Figure 2 
                  Structural equation modeling results.
Figure 2

Structural equation modeling results.

4 Discussion

The principle of capacity building uses three indicators (Table 6). Based on the value of indicator loadings, the top priority in capacity building lies in “How much respect for local culture” (Table 6). This means that organic farmers need to respect local culture to achieve capacity building at the research site. Furthermore, the second priority of capacity building lies in “dependence on local assets.” This means that farmers need to utilize local assets to achieve capacity building for organic farm managers. The final priority on capacity building lies in “farmer knowledge acquired from generation to generation.” This means that farmer knowledge obtained from generation to generation is needed to achieve capacity building of organic farm managers. The results of this study differ from the study [3], which found that the top priority in developing organic farming capacity in Iran lies in respecting indigenous knowledge systems and traditional agricultural systems. In contrast, respect for local culture is the third priority.

Table 6

Priority of indicators on the capacity building construct

Construct Indicator variables Priority Estimated indicator loadings
Capacity building How much respect for the local culture 1 0.970
Dependence on local assets 2 0.939
Farmer knowledge acquired from generation to generation 3 0.903

The SEM model in this study links capacity development to financial performance and production optimization. Based on Table 2, capacity building has a positive effect on the financial performance of organic farms, while capacity development does not affect optimizing organic agricultural production. This result means that capacity building carried out by organic farmers through respect for local culture, utilizing local assets, and farming knowledge obtained from parents is proven to improve financial performance and optimize organic agricultural production. The findings are in line with previous research. Oya et al. [25] stated that capacity building through training and other support has been proven to increase the quantity and quality of production, income, and sustainability of agriculture. Furthermore, Morshedi et al. [3] found that the capacity building of farm managers has proven to improve the food security of organic farming households.

Farm managers’ capacity includes the ability to safeguard their farms [26]. Based on the loading indicator’s value, the protection construct’s top priority is “protecting local products” (Table 7). Plant protection in organic agriculture can protect local products (resources), such as varieties of seeds or local seeds, water sources, and fertilizers sourced from plant residues and animal waste. The second priority of the protection construct lies in “increased groundwater, non-dry soil.” Plant protection in organic agriculture can increase groundwater so the soil is not dry (moist). Empirical research demonstrates that organic farming can boost groundwater levels by 15% and groundwater reserves by 10% [27]. The third priority in the protection construct lies in “can maintain land fertility in the long term.” Applying protection to organic farming can maintain land fertility in the long term. Research indicates that cultivating organically can promote the growth of microorganisms that contribute to soil fertility [27]. The fourth priority in the protection construct is “crops are more resistant to pests.” The application of protection on organic farms can protect plants from various diseases. Empirical studies prove organic farming can reduce plant root, stem, and fruit diseases [27]. The fifth priority in the protection construct lies in “can improve soil structure.” Applying protection to organic farming can improve soil structure. Organic agriculture can promote the growth of microorganisms that enhance soil structure, according to empirical research [27]. The sixth priority on the protection construct is “Resistant to dry season.” Protecting water sources in the soil can increase the ability of organic vegetable plants to survive even though the dry season occurs for a relatively long time. Research indicates that organic farming can preserve groundwater, enabling plants to withstand extreme temperatures, salinity, and drought [27]. The final priority in the protection construct lies in “increasing plant diversity.” Applying protection to organic farming can facilitate the growth of diverse crops on agricultural land, including carrots and pakcoy. Research demonstrates that organic farming helps biodiversity by preserving and reviving the variety and quantity of local flowers, particularly rare and segetal species, in grain fields [28].

Table 7

Priority of indicators on the construct plant protection

Construct Indicator variables Priority Estimated indicator loadings
Protection Protecting local products 1 0.947
Groundwater increases. The soil does not dry 2 0.943
Can maintain land fertility in the long term 3 0.939
Plants are more resistant to pests 4 0.936
Can improve soil structure 5 0.920
Resistant to dry season 6 0.912
Plant diversity is increasing 7 0.898

This study explores the impact of crop protection in organic agriculture on financial performance and production optimization. The study showed that protection significantly affected production optimization (Table 2). This means that crop protection through local products can increase organic agricultural production. Previous empirical studies have also found that crop protection can increase agricultural production [29]. The study also found that crop protection in organic farming did not significantly affect financial performance. Likely due to the high cost of crop protection and uncertain prices, crop protection does not affect the financial performance of organic farmers. Das et al. [30] review supports these findings; researchers stated high costs make consumers avoid organic products. Therefore, farmers need more motivation to enhance organic agriculture production. In other words, the researcher indicated that organic farming does not contribute to farmers’ income.

Every farmer has technical expertise in organic farming cultivation. This study uses five indicators that represent the technical expertise of organic farmers (Table 8). Based on indicator loadings, the top priority in the technical construct lies in “not using chemical fertilizers.” This means that the technical expertise of organic farming is related to the principle of not using chemical fertilizers. Previous empirical studies have stated that organic farmers do not like to use chemical fertilizers because the soil becomes infertile in the long run and endangers the health of farmers and consumers [31,32]. The second priority in the technical construct is “processing animal waste and plant residues to become fertilizer.” This means processing animal manure and plant residues to become organic fertilizer, a form of technical halt for organic farmers. Previous empirical studies have suggested that African organic farmers use animal manure and crop residues as fertilizer [31]. The third priority in the technical construct lies in “using environmentally friendly resources.” This means that organic farmers utilize the resources available at the research site, such as animal manure, crop residues, crop diversification, and crop rotation. According to empirical studies, organic farmers use animal and plant wastes and diversify crops with local climate-adapted kinds [31]. The fourth priority in the technical construct lies in “the way soil organics are getting more fertile.” The organic approach uses charcoal from wood burning to provide soil carbon without machinery. Empirical studies state that organic farmers use biochar (wood charcoal) as a carbon source to make the soil more fertile [33]. The final priority on the technical construct lies in “can reduce tillage.” Organic farmers reduce tillage by cultivating the upper soil to remove weeds and adding plant residues. Empirical studies state that reducing tillage is done by introducing grass residues into the soil and planting cover crops, potentially increasing soil carbon [34].

Table 8

Priority of indicators on technical constructs

Construct Indicator variables Priority Estimated indicator loadings
Technical Not using chemical fertilizers 1 0.974
Processing animal manure and plant residues to become fertilizer 2 0.962
Using eco-friendly resources 3 0.949
The organic way the soil is getting more fertile 4 0.932
May reduce tillage 5 0.926

Technical expertise is associated with financial performance and production optimization in this study. The results showed that the technical expertise of organic farmers had a significant positive effect on financial performance, while technical expertise did not significantly affect production optimization. This means that the technical expertise of organic farmers through processing animal manure and plant residues, utilizing wood-burning residues (charcoal), not using chemical fertilizers, and reducing tillage have the potential to increase the income of organic farmers. This finding is in line with Joseph et al.’s [33] study, which states that farmers’ expertise in utilizing biochar (wood charcoal) has an impact on increasing farmers’ profits in the long run.

The primary reasons for consuming organic farm veggies were environmental benefits (57%) and health benefits (94%), namely in preventing high total cholesterol (71%), cardiovascular illness (69%), and obesity (68%) [35]. Food safety is the handling, preparation, and storage of food that can reduce the risk of illness caused by foodborne illness so that it does not cause harm to consumers when eaten according to its designation [36]. A healthy diet lacks problematic nutrients (added sugar, sodium, and saturated fat) compared to the beneficial nutrients that may be contained in food, including vitamins and proteins [37]. Organic farming is essential to provide food products that are healthy and safe to consume [30]. This study uses healthy and safe latent variables to be analyzed on organic farming. The proposed indicators of healthy and safe latent variables consist of healthy consumption and no harm to the body, organic vegetables providing better taste, and producing vegetables with high nutrient content (Table 9). Based on the value of the loading indicator, the top priority on a healthy and safe construct is “healthy consumption and does not cause harm to the body.” This implies that farmers’ organic vegetables should be safe to eat and not harmful to the body. Empirical studies prove consumers believe purchasing and consuming organic vegetables contributes to a healthy body and avoiding disease [38].

Table 9

Priority of indicators on the construct healthy and safe

Construct Indicator variables Priority Estimated indicator loadings
Healthy and safe Healthy consumed and not harmful to the body 1 0.951
Produce vegetables with high nutritional content 2 0.949
Organic vegetables give it a better taste 3 0.931

The second priority on healthy and safe constructs is “producing vegetables with high nutrient content.” This means that organic farming can produce vegetables with high nutritional content. Empirical studies prove organic vegetables have high dissolved solids content, low sugar content, high dry matter content that prevents spoilage quickly and increases storage time, high fiber content, and high bioactive compounds, such as phenolic content, β-carotene, vitamin C, anthocyanins, flavonoids, lycopene, and other antioxidant compounds. These bioactive compounds play a role in preventing cancer, lowering blood pressure, and preventing cardiovascular disease and neurological diseases [39].

The last priority on healthy and safe constructs is “organic vegetables provide better taste.” This means that organic farming can provide a better taste when we consume organic vegetables. Rahman et al. [39] believed the high dissolved solid content results in a better taste. Another study shows that organic agricultural products taste better than conventional ones [40].

Farmers who can produce safe and healthy agricultural products can improve the welfare of rural households in developing countries [41]. This study found that organic farmers who produce healthy and safe vegetables significantly positively affect financial performance and production optimization. This means that increased demand for healthy, safe vegetables contributes to increased incomes and incentivizes farmers to increase the productivity of organic vegetables. This aligns with Morshedi et al. [3], who state that healthy and safe agricultural products impact food security.

Production optimization in this study consists of four indicators (Table 10). Based on the value of the loading indicator, the main priority in the production optimization construct lies in “increasing efficiency in areas with low input.” Through animal manure and plant wastes, organic farming can reduce inputs like fertilizers and plant medicines, increasing efficiency. Clark [42] stated that energy efficiency and efficiency in organic agricultural inputs are higher than in conventional agriculture. The second priority in the production optimization construct lies in “reducing crop failure.” This means farms utilizing organic inputs can reduce vegetable crop failures at the study site. Empirical studies prove that crop failure in organic farming is lower than in conventional agriculture [43]. The third priority in the production optimization construct lies in “reducing production risk.” The production risk in question relates to human health risks and environmental degradation. Thus, organic agriculture eliminates production risks, including farmers’ health and land degradation. Durham and Mizik [44] stated that using organic inputs reduces farmers’ health risks and the quality of environmental degradation. The last priority in the production optimization construct lies in “obtaining higher productivity during drought.” Farmers who utilize organic inputs and protect groundwater can produce high vegetable productivity in the dry season. Durham and Mizik [44] stated that crops that use organic farming systems have higher productivity than conventional agriculture in the dry season.

Table 10

Priority of indicators on the construct production optimization

Construct Indicator variables Priority Estimated indicator loadings
Production optimization Increase efficiency in areas with low input 1 0.946
Reduce crop failure 2 0.937
Reduce production risk 3 0.929
Obtain higher productivity during drought 4 0.908

This study evaluates the relationship between production optimization, financial performance, and farm performance. Table 2 shows that production optimization significantly positively affects financial performance and farm performance. This means that organic farmers’ efforts to optimize production through increased efficiency, reducing crop failure, reducing production risk, and high productivity in the dry season can improve financial performance and farm performance. This finding is in line with Durham and Mizik’s [44] study, which states that although crop productivity is low, low input costs and relatively high selling prices result in high profits for organic farmers. Joseph et al. [33] state that biochar application increases farmers’ productivity and income.

Financial performance in this study consists of four indicators (Table 11). Based on the value of the loading indicator, the top priority in the financial performance construct lies in “increasing net profit.” This means organic farming applications can reduce production costs and increase net profit. Durham and Mizik [44] also said reduced plant medicine and fertilizer costs and high prices increased farmers’ profits. The second priority in the financial performance construct lies in “showing an increase in capital.” This means relatively high profits incentivize farmers to increase their capital in the next planting season. Durham and Mizik’s [44] review states that organic farmers dare to increase their capital when profits and demand increase. The third priority in the financial performance construct lies in “increasing its market share.” This means that organic farming can increase sales and market share of organic vegetables. Durham and Mizik’s [44] review states that the increased production of organic commodities impacts the increasing sales of organic products abroad. The final priority in the financial performance construct lies in “improving farmers’ assets.” This means that organic farming can increase the assets of vegetable farmers at the research site. Durham and Mizik [44] also stated that the return on assets and total asset turnover increase when farmers adopt organic farming.

Table 11

Priority of indicators on the construct financial performance

Construct Indicator variables Priority Estimated indicator loadings
Financial performance Increase net profit 1 0.954
Shows increased capital 2 0.946
Increase its market share 3 0.945
Increase farmers’ assets 4 0.944

This study links financial performance to farmer performance. Table 2 shows that financial performance is significantly positively related to farmer performance. This means that improving financial performance through an increase in net profit, capital, market share, and farmers’ assets has an impact on improving the performance of farmers’ farming. This finding aligns with the review of organic farmers’ income [45], which impacts household food security.

Finally, farmer performance has three indicators (Table 12). Based on the value of the loading indicator, the top priority in the farmer’s farm performance construct lies in “having enough food throughout the year.” This means that organic farming can give farming households the ability to have enough food throughout the year. The second priority in the farmer’s agricultural performance construct is “being able to send children to school.” This means that organic farming can give the head of the farmer’s household the ability to send their children to school. The last priority in the farmer’s business performance construct lies in “improving the quality of the house,” meaning that organic farming can give the head of the farmer’s household the ability to repair and even modify their homes. This finding aligns with studies in India that state organic farming can improve food quality through high nutrition and health, send children to school and attend religious events, and renovate homes with new TVs and furniture [46].

Table 12

Priority of indicators on the construct of farmer performance

Construct Indicator variables Priority Estimated indicator loadings
Farmer performance Have enough food all year round 1 0.981
Can send children to school 2 0.970
Housing quality improves 3 0.938

Based on Table 3, for protection and technical expertise to be strongly related, government policies must develop extension and training programs for organic farmers. Increasing farmers’ technical knowledge of organic content and plant protection can increase productivity, which impacts better financial performance and farmer farming performance. The benefits of training on the expertise of organic farmers are in line with a study by Liu et al. [47], which showed that participating in technical training provided by agricultural cooperatives can significantly increase the likelihood of farmers adopting organic fertilizers.

Subsidy support from the government by purchasing is needed to improve the financial performance of farmers. The trick is for the government to buy organic vegetable products from farmers at high prices and sell them at low prices to consumers. Previous empirical studies have shown that government support in terms of resources, credit, markets, and subsidies is also relevant in motivating the adoption of organic farming [48]. Conversely, subsidies for organic farming support, and high-cost returns can be considered factors that positively affect the implementation of organic technologies [49].

5 Conclusion

Capacity building has been proven to improve financial performance and optimize organic agricultural production. Protection has a significant positive effect on optimizing organic agricultural production. The technical expertise of organic farmers has a significant positive impact on financial performance. Organic farmers who produce healthy and safe vegetables positively affect financial performance and production optimization. The efforts of organic farmers to optimize production can improve financial performance and farm performance. Organic farming increases the assets of vegetable farmers and can improve the business performance of organic cabbage farmers in Semarang Regency.

Our study found that all variables had a weak relationship with financial performance and farmer performance. Policymakers are expected to actively support organic farming. Counselling and training must be conducted so that farmers can know the right organic content and plant protection to increase productivity. The government can create activities such as campaigns to support organic farming by providing output subsidies to farmers.

The above recommendations are expected to improve the financial performance and sustainable farmer performance of organic cabbage farming in Semarang Regency. If organic cabbage farming is sustainable, conventional farmers will be interested in moving to organic. However, for this reason, further research is needed on these sustainability indicators and the factors that affect them.

Acknowledgments

The authors would like to thank all the participating organic farmers in Getasan sub-district in the Semarang district for their support and valuable information for this study during the interview. Sincere thanks and appreciation also to the farmer group members who have provided valuable information concerning organic cabbage farming.

  1. Funding information: Authors state no funding involved.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results and approved the final version of the manuscript. WW: data collection, conceptualization, methodology, supervision investigation, writing, and correcting, analysis data. ESR: revised the manuscript, methodology. DHD: revised the manuscript, results and discussion. MH: revised the manuscript, conclusion and abstract.

  3. Conflict of interest: Authors state no conflict of interest.

  4. 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: 2024-03-08
Revised: 2024-09-26
Accepted: 2024-10-18
Published Online: 2024-12-13

© 2024 the author(s), published by De Gruyter

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

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  73. Evaluating agricultural yield and economic implications of varied irrigation depths on maize yield in semi-arid environments, at Birfarm, Upper Blue Nile, Ethiopia
  74. Chemometrics for mapping the spatial nitrate distribution on the leaf lamina of fenugreek grown under varying nitrogenous fertilizer doses
  75. Pomegranate peel ethanolic extract: A promising natural antioxidant, antimicrobial agent, and novel approach to mitigate rancidity in used edible oils
  76. Transformative learning and engagement with organic farming: Lessons learned from Indonesia
  77. Tourism in rural areas as a broader concept: Some insights from the Portuguese reality
  78. Assessment enhancing drought tolerance in henna (Lawsonia inermis L.) ecotypes through sodium nitroprusside foliar application
  79. Edible insects: A survey about perceptions regarding possible beneficial health effects and safety concerns among adult citizens from Portugal and Romania
  80. Phenological stages analysis in peach trees using electronic nose
  81. Harvest date and salicylic acid impact on peanut (Arachis hypogaea L.) properties under different humidity conditions
  82. Hibiscus sabdariffa L. petal biomass: A green source of nanoparticles of multifarious potential
  83. Use of different vegetation indices for the evaluation of the kinetics of the cherry tomato (Solanum lycopersicum var. cerasiforme) growth based on multispectral images by UAV
  84. First evidence of microplastic pollution in mangrove sediments and its ingestion by coral reef fish: Case study in Biawak Island, Indonesia
  85. Physical and textural properties and sensory acceptability of wheat bread partially incorporated with unripe non-commercial banana cultivars
  86. Cereibacter sphaeroides ST16 and ST26 were used to solubilize insoluble P forms to improve P uptake, growth, and yield of rice in acidic and extreme saline soil
  87. Avocado peel by-product in cattle diets and supplementation with oregano oil and effects on production, carcass, and meat quality
  88. Optimizing inorganic blended fertilizer application for the maximum grain yield and profitability of bread wheat and food barley in Dawuro Zone, Southwest Ethiopia
  89. The acceptance of social media as a channel of communication and livestock information for sheep farmers
  90. Adaptation of rice farmers to aging in Thailand
  91. Combined use of improved maize hybrids and nitrogen application increases grain yield of maize, under natural Striga hermonthica infestation
  92. From aquatic to terrestrial: An examination of plant diversity and ecological shifts
  93. Statistical modelling of a tractor tractive performance during ploughing operation on a tropical Alfisol
  94. Participation in artisanal diamond mining and food security: A case study of Kasai Oriental in DR Congo
  95. Assessment and multi-scenario simulation of ecosystem service values in Southwest China’s mountainous and hilly region
  96. Analysis of agricultural emissions and economic growth in Europe in search of ecological balance
  97. Bacillus thuringiensis strains with high insecticidal activity against insect larvae of the orders Coleoptera and Lepidoptera
  98. Technical efficiency of sugarcane farming in East Java, Indonesia: A bootstrap data envelopment analysis
  99. Comparison between mycobiota diversity and fungi and mycotoxin contamination of maize and wheat
  100. Evaluation of cultivation technology package and corn variety based on agronomy characters and leaf green indices
  101. Exploring the association between the consumption of beverages, fast foods, sweets, fats, and oils and the risk of gastric and pancreatic cancers: Findings from case–control study
  102. Phytochemical composition and insecticidal activity of Acokanthera oblongifolia (Hochst.) Benth & Hook.f. ex B.D.Jacks. extract on life span and biological aspects of Spodoptera littoralis (Biosd.)
  103. Land use management solutions in response to climate change: Case study in the central coastal areas of Vietnam
  104. Evaluation of coffee pulp as a feed ingredient for ruminants: A meta-analysis
  105. Interannual variations of normalized difference vegetation index and potential evapotranspiration and their relationship in the Baghdad area
  106. Harnessing synthetic microbial communities with nitrogen-fixing activity to promote rice growth
  107. Agronomic and economic benefits of rice–sweetpotato rotation in lowland rice cropping systems in Uganda
  108. Response of potato tuber as an effect of the N-fertilizer and paclobutrazol application in medium altitude
  109. Bridging the gap: The role of geographic proximity in enhancing seed sustainability in Bandung District
  110. Evaluation of Abrams curve in agricultural sector using the NARDL approach
  111. Challenges and opportunities for young farmers in the implementation of the Rural Development Program 2014–2020 of the Republic of Croatia
  112. Yield stability of ten common bean (Phaseolus vulgaris L.) genotypes at different sowing dates in Lubumbashi, South-East of DR Congo
  113. Effects of encapsulation and combining probiotics with different nitrate forms on methane emission and in vitro rumen fermentation characteristics
  114. Phytochemical analysis of Bienertia sinuspersici extract and its antioxidant and antimicrobial activities
  115. Evaluation of relative drought tolerance of grapevines by leaf fluorescence parameters
  116. Yield assessment of new streak-resistant topcross maize hybrids in Benin
  117. Improvement of cocoa powder properties through ultrasonic- and microwave-assisted alkalization
  118. Potential of ecoenzymes made from nutmeg (Myristica fragrans) leaf and pulp waste as bioinsecticides for Periplaneta americana
  119. Analysis of farm performance to realize the sustainability of organic cabbage vegetable farming in Getasan Semarang, Indonesia
  120. Revealing the influences of organic amendment-derived dissolved organic matter on growth and nutrient accumulation in lettuce seedlings (Lactuca sativa L.)
  121. Identification of viruses infecting sweetpotato (Ipomoea batatas Lam.) in Benin
  122. Assessing the soil physical and chemical properties of long-term pomelo orchard based on tree growth
  123. Investigating access and use of digital tools for agriculture among rural farmers: A case study of Nkomazi Municipality, South Africa
  124. Does sex influence the impact of dietary vitD3 and UVB light on performance parameters and welfare indicators of broilers?
  125. Design of intelligent sprayer control for an autonomous farming drone using a multiclass support vector machine
  126. Deciphering salt-responsive NB-ARC genes in rice transcriptomic data: A bioinformatics approach with gene expression validation
  127. Review Articles
  128. Impact of nematode infestation in livestock production and the role of natural feed additives – A review
  129. Role of dietary fats in reproductive, health, and nutritional benefits in farm animals: A review
  130. Climate change and adaptive strategies on viticulture (Vitis spp.)
  131. The false tiger of almond, Monosteira unicostata (Hemiptera: Tingidae): Biology, ecology, and control methods
  132. A systematic review on potential analogy of phytobiomass and soil carbon evaluation methods: Ethiopia insights
  133. A review of storage temperature and relative humidity effects on shelf life and quality of mango (Mangifera indica L.) fruit and implications for nutrition insecurity in Ethiopia
  134. Green extraction of nutmeg (Myristica fragrans) phytochemicals: Prospective strategies and roadblocks
  135. Potential influence of nitrogen fertilizer rates on yield and yield components of carrot (Dacus carota L.) in Ethiopia: Systematic review
  136. Corn silk: A promising source of antimicrobial compounds for health and wellness
  137. State and contours of research on roselle (Hibiscus sabdariffa L.) in Africa
  138. The potential of phosphorus-solubilizing purple nonsulfur bacteria in agriculture: Present and future perspectives
  139. Minor millets: Processing techniques and their nutritional and health benefits
  140. Meta-analysis of reproductive performance of improved dairy cattle under Ethiopian environmental conditions
  141. Review on enhancing the efficiency of fertilizer utilization: Strategies for optimal nutrient management
  142. The nutritional, phytochemical composition, and utilisation of different parts of maize: A comparative analysis
  143. Motivations for farmers’ participation in agri-environmental scheme in the EU, literature review
  144. Evolution of climate-smart agriculture research: A science mapping exploration and network analysis
  145. Short Communications
  146. Music enrichment improves the behavior and leukocyte profile of dairy cattle
  147. Effect of pruning height and organic fertilization on the morphological and productive characteristics of Moringa oleifera Lam. in the Peruvian dry tropics
  148. Corrigendum
  149. Corrigendum to “Bioinformatics investigation of the effect of volatile and non-volatile compounds of rhizobacteria in inhibiting late embryogenesis abundant protein that induces drought tolerance”
  150. Corrigendum to “Composition and quality of winter annual agrestal and ruderal herbages of two different land-use types”
  151. Special issue: Smart Agriculture System for Sustainable Development: Methods and Practices
  152. Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  153. FruitVision: A deep learning based automatic fruit grading system
  154. Energy harvesting and ANFIS modeling of a PVDF/GO-ZNO piezoelectric nanogenerator on a UAV
  155. Effects of stress hormones on digestibility and performance in cattle: A review
  156. Special Issue of The 4th International Conference on Food Science and Engineering (ICFSE) 2022 - Part II
  157. Assessment of omega-3 and omega-6 fatty acid profiles and ratio of omega-6/omega-3 of white eggs produced by laying hens fed diets enriched with omega-3 rich vegetable oil
  158. Special Issue on FCEM - International Web Conference on Food Choice & Eating Motivation - Part II
  159. Special Issue on FCEM – International Web Conference on Food Choice & Eating Motivation: Message from the editor
  160. Fruit and vegetable consumption: Study involving Portuguese and French consumers
  161. Knowledge about consumption of milk: Study involving consumers from two European Countries – France and Portugal
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