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Article

Agricultural Markets, Cropping Patterns, and Consumption Patterns: The Moderating Effect of COVID-19 on Mountainous Communities

by
Muhammad Khayyam
1,
Fatima Daud Kamal
2,
Muhammad Nouman
3,
Arjumand Nizami
2,
Jawad Ali
2 and
Muhammad Asad Salim
2,*
1
School of Economics and Management, Southwest Jiaotong University, Chengdu 611756, China
2
Helvetas Swiss Intercooperation, Islamabad 04404, Pakistan
3
Institute of Management Sciences, Peshawar 25100, Pakistan
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(20), 14934; https://doi.org/10.3390/su152014934
Submission received: 31 July 2023 / Revised: 6 October 2023 / Accepted: 12 October 2023 / Published: 16 October 2023
(This article belongs to the Section Sustainable Agriculture)
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Abstract

:
Since COVID-19’s emergence in 2020, all segments of life, including farming communities, have been impacted. The pandemic revealed both vulnerabilities and opportunities for resilience, particularly for those dwelling in the harsh mountainous terrains, which have already strained food ecosystems. Taking influence from an exhaustive literature review, this study proposes and tests a model for the transformations observed in the agriculture markets, particularly input, labor, and product markets, and elucidates the influence of these changes on cropping and consumption patterns. With data from two major mountainous terrains in north Pakistan spanning three years before and during the pandemic, a quantitative inquiry was carried out on the agriculture markets and farming patterns. A total of 5273 members of the farming communities were targeted for data collection. A two-step process was used for data analysis, including an evaluation of the outer or measurement model followed by the inner or structural model through partial least squares structural equation modeling (PLS-SEM). With a hitherto ignored focus on the already vulnerable mountainous communities, the findings confirm the direct influence of agriculture markets on changes in the farmers’ cropping patterns as well as the moderating influence of the pandemic on these relationships. Consistent with previous literature, the results also affirm the influence of changes in cropping patterns and changes in consumption patterns. However, it was found that the agriculture input markets strongly predict the changes in cropping patterns, whereas the labor and product markets have comparatively lower prediction value. By investigating the various facets of food supply chains, this study offers valuable insights on market dynamics in times of a crisis, such as a pandemic.

1. Introduction

At the onset of 2020, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 or COVID-19) took over the globe like a tsunami tidal wave. It came quietly and swiftly, but gradually the din became too loud, and so did the destruction in the form of loss of good health and, sadly, of lives. Literature abounds on nearly every aspect of life: health, food, agriculture, livelihoods, and economies being affected. Research focusing on food safety has shown that the four main pillars of food security, i.e., availability, access, utilization, and stability [1], have been affected by the pandemic [2]. On the other hand, though the pandemic is proven to have started in Asia, the principal Asian farming and food systems were found to be ‘moderately’ resilient to COVID-19 [3]. The reason for this resilience might have been the policies imposed by governments, whereby the priority was the availability of food and its affordability [3].
As the COVID-19 pandemic unfolded, the resilience of food supply chains, especially during a time of crisis, became a dominant aspect of market consideration or analysis. Such food supply chains had to rapidly adjust to demand-side shocks such as panic buying, food purchasing, growing, and consumption patterns. It also needed to plan for disruptions on the supply side, such as labor shortages, transportation, and supply networks. Markets of all forms had been severely impacted by the pandemic [4]. For example, Adhikari and Parajuli [5] reported that for Nepalese farmers, the prolonged lockdown started to severely affect the distribution and production of food. Consequently, the government allowed the farmers to carry on with their farming activities while respecting the COVID-19 precautionary health measures. The pandemic did cause a dent in the availability of food as well as its production, trade/distribution, supply chains of inputs, and farm products. It also made the country realize the magnanimity of the problem, especially when it was reduced to dependence on others for food.
Besides the adverse effects of the pandemic on growers in the plains that provide significant agricultural produce, farmers in the mountainous regions have not been spared either. For example, in the case of Pakistan, Ali et al. [6] focus on the lack of community cooperation with government agencies (regarding the spread of the disease) and the significant socioeconomic impact on the lives of the locals in the country’s Himalayan region. The attention of research has been more on the financial uncertainties, loss of income and jobs, and food insecurity due to the outbreak of the disease. However, not many studies have been identified that investigate the effect of the pandemic on the cropping decisions of farming communities in mountainous regions.
According to Rasul and Hussain [7], Pakistan has almost 61% of its geographical area categorized as mountainous, in which nearly 40 million people reside. The mountainous people’s livelihoods and food security depend heavily on the local resources. At all elevations, the specific agro-ecological and other livelihood potentials vary on a considerable level, though agro-pastoral or horticultural activities are the main sources of income. This, alongside small businesses, wage labor, tourism, non-timber forest products, remittances, and mostly animal husbandry, contributes towards livelihoods and food security. In light of the above discourse, this study aims to be a first attempt at analyzing the influence of various facets of agricultural markets on cropping and consumption patterns and the moderating effect of COVID-19 on these relationships. Data over a period of three years (2019–2021) have been taken for investigation with a focus on farming communities from mountainous regions of Haripur and Chitral Districts (northwest Pakistan) and the Gilgit Baltistan region (Figure 1).
Nutrition-based research or studies carried out in mountainous or inaccessible areas in one way or another shed light on the debate/distinction between ‘Preferences vs. Availability and/or Accessibility vs. Poverty’ [8]. Life and earning livelihoods, as well as the nature and causes of food in mountainous regions, have always been different from those in the plains. According to Rasul and Hussain [7], socioeconomic and environmental changes, as well as topographical constraints, have affected food insecurity to a considerable degree. Hence, it is significantly higher in the mountains than in the plains. Top it off with COVID-19-related unusual situations, constraints, and debilitating living conditions. The inverse effects of the COVID-19 pandemic have been widely felt in the restrictions on movement, closure of production facilities, and curbs on food trade. This, in turn, caused the perceived lack of availability and relatively higher costs of food ingredients in food markets [9].
During the pandemic years in Pakistan (approximately 2020–2021), the whole country and the mountainous regions, especially those comprising this current research study, suffered significantly due to (1). Lockdowns (2). Curbs on Transportation (3). Strict control of all types of movement between districts. The lockdowns induced by the pandemic caused severe restrictions and crippled the transportation of agricultural and other commodities. Hence, the dwellings up on the mountains could not lay hands on, for example, staple wheat, which would be sent up from the plains. And the locals would have to revert and rely on the forgotten staple of maize flour. This could well be taken up for future investigation.

2. Aims and Hypotheses

The broader aim of this study is to help understand the market dynamics for agriculture and food supply chains during times of crises such as a pandemic. More specifically, this study addresses three key objectives: Firstly, it studies the influence of agricultural input markets, agricultural labor markets, and agricultural product markets on the changes in the cropping patterns of the selected mountainous communities. Secondly, it seeks to investigate, through the three individual variables, the moderating effect of COVID-19 on the changes in cropping patterns. And thirdly, how the cropping patterns, hence evolved, have affected the changes in consumption patterns of the mountainous communities.
Although market integration does enter all farming systems, it varies in grade and scope and highlights the importance of farmers’ cropping patterns. Cropping patterns are defined as the proportion of area under different crops at a point in time. While a change in the pattern implies a change in both the proportion of area under different crops at two or more points in time [10,11]. An interesting perspective on cropping patterns has been put forward by Firdaus [10] whereby no cropping pattern can be good for all times to come, and it can vary from macro to micro areas, both in space and time.
Agricultural input markets are a major wing of agricultural economics, which includes agricultural land, agrochemicals, fertilizers, and animal feed. It can, therefore, be defined as comprising all activities that ensure the supply of farm inputs to the farms and the movement (or transportation) of agricultural products from the farms to the consumers [12]. For sustainable land use and production, agricultural input markets are extremely important, as land is the single-most important source of livelihood for the majority of the rural poor [13]. According to Wolf [14], fertilizer and pesticide dealers, as well as crop consultants, have consistent access to farmers, and consequently, they exercise considerable influence on the cropping systems.
Farmers’ decision-making regarding cropping patterns is influenced by a range of constraints, including biophysical factors, production technologies, input and labor markets, financial limitations, social norms, and policies [15]. The agricultural input markets play a significant role in predicting changes in cropping patterns in mountainous communities in Pakistan. Limited access to input markets can pose significant challenges for smallholder and subsistence farmers, as they heavily rely on their own production for food [16]. Similarly, Niragira’s [17] research emphasized that the food production systems in the poorest areas often face limitations such as low input use, mixed cropping practices, and limited livestock. Based on the discussion above, we hypothesize as follows:
Hypothesis 1 (H1).
Agricultural input markets predict the changes in cropping patterns in the mountainous communities of Pakistan.
In addition to input markets, the pre-COVID-19 data suggest that a farmer’s decisions on production or cropping can also be shaped by other conditions in a variety of ways, particularly the dynamics of local and/or seasonal labor availability. This means that it might not be profitable to grow a crop with a very narrow harvesting window in a month where the general demand for agricultural labor is high in the region [18].
The agricultural labor market plays a crucial role in predicting changes in cropping patterns. The diversification of crops has been identified as an effective strategy for farmers to manage various risks, including labor market fluctuations and other output market risks [19]. Studies have shown that a more diverse cropping pattern is associated with increased labor availability over the production period, while fluctuations in agricultural labor markets are influenced by socioeconomic, environmental, and agricultural marketing factors [20]. Furthermore, disruptions in agricultural and food markets caused by labor shortages have highlighted the dependence of farming families on their own land for cropping decisions and the reliance of landless individuals on local markets for their diets [21]. Taking into consideration the findings above, we deduce that:
Hypothesis 2 (H2).
Agricultural labor markets predict the changes in cropping patterns in mountainous communities in Pakistan.
Similar to the input and agricultural labor markets, the product markets are crucial to the agriculture sector. Some farm operations require access to seasonal and migrant farm workers, e.g., for planting, weeding, pruning, and harvesting, while other farms and agri-businesses depend on a steady, reliable supply/pool of workers all year round [22].
The foundation for the food market, including manufacturing and processing industries, is agricultural products [23]. The agricultural product markets are an all-encompassing term for both agricultural produce (fresh fruits, vegetables, cereals, grains), by-products (different vegetable or seed oils, livestock feed, molasses, etc.), and products or commodities themselves, which might have undergone some form of processing. Agricultural products are dependent on the region and/or the climate of a region. Access to agricultural product markets is a significant predictor of changes in cropping patterns, especially in mountainous communities. Non-farm-level factors, such as access to local product markets, credit, inputs, and road networks, have a significant relationship with farmers’ choices of crops and diversification at the farm level [24]. Besides, visible changes in traditional cropping patterns can be observed with a shift towards more remunerative crops [25]. The agricultural product markets, market associations, and disruptions in food supplies significantly impact farmers’ ability to sell their produce, resulting in shortages and price hikes, particularly affecting the poor and urban dwellers [26]. These findings demonstrate the critical role that agricultural product markets play in predicting and influencing cropping patterns within these communities. Based on this logic, we developed the following hypothesis:
Hypothesis 3 (H3).
Agricultural product markets predict the changes in cropping patterns in mountainous communities in Pakistan.
As the COVID-19 pandemic took over the globe at the start of the year 2020, it affected multiple facets of society. Having taken an economic toll, the pandemic challenged the existing dynamics of multiple sectors and industries.
A considerable number of studies have been carried out on the relationship existing between the moderating effects COVID-19 might have on different variables [27,28], but very few exist on the moderating effect on agricultural activities [29,30]. A few studies were skimmed out of a vast plethora of research on the moderating effect. The COVID-19 pandemic has had a significant impact on the agriculture sector worldwide, disrupting supply chains, labor availability, and market dynamics, thereby affecting food security and agricultural productivity [31,32,33]. This has led to decreased agricultural production and shortages in food supplies. Furthermore, research by Xie et al. [31] and Saxena et al. [32] shows the moderating effects of social support and online learning on coping with pandemic-related challenges. Interestingly, despite major disruptions in input supply chains and higher prices, research reveals the resilience of farmers in managing crop production during the pandemic, indicating minimal changes in cropping patterns [4]. However, the overall impact of COVID-19 on input markets and food accessibility remains moderate, as indicated by studies conducted by Dixon et al. [3] and Tang et al. [33]. Notably, these studies highlight the need to examine the specific moderating effects of COVID-19 on agricultural markets and their impact on cropping patterns. Understanding and addressing the challenges posed by the pandemic can inform government actions to mitigate the significant impacts on agricultural labor markets, trade, value chains, and food security globally. Based on an extensive literature review, none of the studies that have been carried out to date have explored the moderating effect of COVID-19 on the relationship of agricultural markets (input, labor, and product) with the changes in the cropping patterns of the mountainous regions in Pakistan.
Hence, capitalizing on the above-mentioned literature, or lack thereof, regarding the moderating effect of COVID-19 on the relationship(s) of the agricultural input markets, agricultural labor markets, and agricultural product markets with the changes in cropping patterns, we hypothesize that:
Hypothesis 4a (H4a).
COVID-19 moderates the structural relationships between agricultural input markets and changes in cropping patterns in mountainous communities in Pakistan.
Hypothesis 4b (H4b).
COVID-19 moderates the structural relationships between agricultural labor markets and changes in cropping patterns in mountainous communities in Pakistan.
Hypothesis 4c (H4c).
COVID-19 moderates the structural relationships between agricultural product markets and changes in cropping patterns in mountainous communities in Pakistan.
The relationship between changes in cropping patterns leading to changes in consumption patterns has been discussed in a multitude of research studies, but evidence could not be found in the literature regarding changes in cropping patterns due to the moderating effect of COVID-19.
Several factors influence the choice of crops and the cropping pattern, which can be due to agricultural policies, infrastructural facilities, technological factors, socioeconomic factors, and environmental factors, water availability, and crop management practices [34]. And most importantly, for the monetary benefits at the farmers’ level [35]. The COVID-19 pandemic has indirectly impacted consumption patterns in mountainous communities in Pakistan by influencing cropping patterns in the region. Research studies highlight the importance of considering cropping patterns in agricultural statistics, particularly for small farms in developing countries [11]. Additionally, Tiwari et al. [36] emphasize the economic profitability and social acceptability of vegetable-based cropping patterns in sustainable upland farming systems. However, the pandemic has introduced challenges to food access and availability [21]. These challenges have led to shifts in consumer demand towards less nutritious and cheaper food options, as well as price instability. Romeo-Arroyo et al. [37] also observed changes in consumer behavior during confinement, with some individuals becoming more health-conscious towards home-cooked meals while others showed a lenience towards snacks and ultra-processed foods. Nevertheless, these findings may not directly apply to the poverty-stricken mountainous regions of Pakistan, where factors such as transportation disruptions, regional lockdowns, and limited access to quality food supplies further exacerbate the situation [2,11,36,37].
In addition to the above-mentioned reasons and causes of changes in cropping patterns leading to consumption patterns, through this study, the moderating effect of COVID-19 on the changes in cropping patterns is being investigated. Therefore, we proposed the following hypothesis:
Hypothesis 5 (H5).
Changes in cropping patterns predict changes in consumption patterns in mountainous communities in Pakistan.
Deriving from the multitude of research studies cited above, it is evident that agricultural market dynamics play a crucial role in farmers’ decisions regarding what they would want to cultivate and consume. The scenario becomes further complicated once a crisis or catastrophe like COVID-19 hits the world. While factors like inputs, labor, and product markets influence the farmer’s cropping patterns or decisions, subsequently influencing their consumption patterns, it is important to investigate these relationships in light of the pandemic as well. What might be the moderating effect of COVID-19 in influencing market dynamics is an important concern. Consequently, we combine all the hypotheses developed from the literature into an all-encompassing model, as shown below (Figure 2).

3. Materials and Methods

In light of the above discourse, this study was carried out using quantitative data, which were collected from both on- and off-farm plots, questionnaires, and interviews held with the farming communities, as well as household surveys, focus group discussions, and workshops held over a period of three years (2019–2021) in selected mountainous regions of Pakistan.

3.1. Data Collection

In order to collect data from the respondents, a detailed questionnaire containing different sections was prepared. The introduction section explained the purpose of the research, assured confidentiality, and requested permission to use the data for research purposes. The general information section included the demographic details of farmers and gathered information regarding the agriculture, labor, and product markets in targeted areas. In the following sections, the questions regarding the changes in cropping patterns and consumption patterns were investigated.
The period allotted for data collection was considerable (over a period of 3 years (2019–2021), spanning the pre- and during COVID-19 months/years. The research area extended over the mountainous regions of the Khyber Pakhtunkhwa Province and the Gilgit Baltistan region of Pakistan. Both of these regions span over the high-altitude Himalayas (8849 m), the Karakorum (8611 m), and the Hindu Kush (7708 m).
The questionnaires were administered during peak COVID-19 times by Nutrition in Mountain Agro-Ecosystems (NMA) project staff with the help of local partners. At the time of data collection, district-to-district movements (both human and cargo) were strictly restricted, and it was only possible to interview the local farmers by the local staff.

3.2. Participants and Sampling Procedure

As the data gathering spanned over a period of years, the instance of bias was nullified due to the changing COVID-19 situation. A total of 5273 members of the targeted group (the farming community) participated voluntarily and answered all the questions. Table 1 represents the demographic information of all the respondents. Regarding the gender distribution of the data collection/survey, nearly 63.3% were men, whereas 36.7% were women. Regarding the age of the respondents, 2.0% were under the age of 25, while 36.3% were aged 25 to 35, and 26.1% were from 36 to 45 years old, and 35.6% were from 46 to 55 years old, respectively. A total of 20.4% of the farmers were from Chitral District, 36.5% from Haripur District, 27.3% from Skardu District, 1.0% from Gilgit, and 14.8% from Shigar Valley (Table 1).
The questions related to the study variables considered in the current research are presented in Table 2.

3.3. Measurements

For this study, data were collected from farmers who participated both before and during the COVID-19 pandemic. To reduce the risk of method bias (as the same participants were providing data for both the independent and dependent variables), deliberate steps were taken to ensure that all farmers involved received clear instructions and understood the questionnaire. Two key approaches were used to minimize collinearity and method bias: the variance inflation factor (VIF) and tolerance (TOL) tests. The measures for each construct in the model were adapted from previous studies, followed by questionnaire construction guidelines. This involved considering both the agricultural market construct as well as the COVID-19 construct shown in Figure 2 as latent variables.
To conduct further analysis of the proposed relationships in the current research model, a structural equation modeling (SEM) approach was adopted, consisting of an outer model and an inner model. The research specifically utilized Partial Least Squares SEM (PLS-SEM) through Smart PLS 3 as an appropriate multivariate technique for this study. PLS-SEM has been widely accepted in strategic management research owing to its flexibility, adequacy, and ability to handle complex and multivariate relationships, as evidenced in the studies conducted by [38] and [39]. This analytical technique is useful in analyzing multiple relationships among variables, which makes it a valuable tool for this research.

4. Analyses and Results

The research employed a two-step process to assess the proposed research model. Firstly, an evaluation of the outer model (measurement model) was conducted, which involved assessing the validity and reliability of the scales used in the study. The next step involved evaluating the inner model (structural model) to examine the model’s fit and the proposed relationships between variables. To perform this analysis, the third version of Smart-PLS (Smart-PLS 3.0) was utilized.

4.1. Measurement Model Assessment

To assess the reflective measurement model, the study considered the evaluation of convergent and discriminant validity. The specific results of this assessment are presented in Table 2 and Table 3. Convergent validity (CV) examines the correlation between items that measure the same construct [40]. This was evaluated by analyzing factor loadings, composite reliability, and average variance extracted (AVE). All item factor loadings ranged between 0.732 and 0.883 (above 0.5), indicating satisfactory results. The values for composite reliability (CR) ranged from 0.749 to 0.842, surpassing the required threshold of 0.7, which demonstrates good internal consistency. Additionally, AVE was calculated by squaring the factor loadings of items within a construct and dividing them by the total number of items. The obtained results indicated that the AVE for all constructs ranged from 0.529 to 0.582, exceeding the minimum threshold of 0.5. This confirms that all items explain more than 50 percent of the variance in their respective constructs [41].
Discriminant validity was assessed using the criteria of Fornell and Larcker as well as Henseler’s heterotrait-monotrait (HTMT) ratio [42,43]. The results of these tests can be found in Table 3. According to Fornell and Larcker, the AVE should be greater than the squared correlation between other constructs. In this study, the square roots of the AVE for each construct, as shown in Table 2, were larger than the estimated correlations with other constructs, indicating satisfactory discriminant validity [42]. Overall, the assessment confirmed that all constructs in the study are distinct and capture unique phenomena not represented by other constructs. Furthermore, following the guidelines of Henseler et al. [43] the HTMT ratios listed in Table 3 were all lower than 0.89, indicating satisfactory discriminant validity for each construct in the proposed model.
Table 4 summarizes our findings, which indicate that the TOL values were greater than 0.1 and the observed VIF values were less than 10. These results suggest that collinearity was not a concern in the study and that there was minimal risk of method bias.

4.2. Structural Model Assessment

To assess the proposed structural model (Figure 2), the study placed importance on the dimensions and values of standardized path coefficients, along with their associated t-statistics, including the calculation of the coefficient of determination (R2). The study employed bootstrapping, a resampling technique, with 5000 resamples to measure the path coefficients and their relative significance in the proposed model. Additionally, the study considered measuring effect sizes (ƒ2) for the structured paths as recommended by Hair et al. [41]. Furthermore, to evaluate the predictive capability of the proposed model, Stone-Geisser’s Q2 was also taken into account.
The results of the bootstrapping procedure yielded β-coefficients, t-values, and ƒ2-values for each structural path, as shown in Table 5. With a confidence level of 99.0 percent, all proposed relationships were found to be significant. The strongest changes in the consumption patterns were observed by the changes in cropping patterns (CR.P → CON.P, β = 0.606, t = 23.85, LL = 0.551, UL = 0.654, p ≤ 0.01), which sustained H5. In addition, the agricultural input markets had the strongest effect on the changes in cropping patterns (AIM → CR.P, β = 0.335, t = 6.065, LL = 0.175, UL = 0.366, p ≤ 0.01), thus supporting H1. However, the agricultural labor and product markets (ALM → CR.P, β = 0.243, t = 3.128, LL = 0.044, UL = 0.184, p ≥ 0.01) and (APM → CR.P, β = 0.257, t = 3.485, LL = 0.043, UL = 0.163, p ≥ 0.01) have a significant positive but lower effect on the changes in cropping patterns. These results sustained H2 and H3.
We used the Cohen criteria [44] to measure the effect sizes (ƒ2), adapting thresholds of 0.02 for small effects, 0.15 for medium effects, and 0.35 for large effects. All variables surpassed the minimum threshold (0.02), indicating their impact on the changes in cropping patterns at small-to-medium levels. The overall (ƒ2) results are presented in Table 5. Additionally, we assessed the coefficient of determination (R2) and predictive relevance (Q2) of independent variables on dependent variables. The computed R2 value for the change-dependent variable was 0.616, thus suggesting that the overall agricultural markets (inputs, labor, and product) considered in this study explain 61.6% of the variance in changes in cropping patterns.

4.3. Moderation Effect

Our theoretical model suggests that the perceived severity of the pandemic moderates the relationships between agricultural markets and changes in cropping patterns (H4 a, b, and c). To investigate the moderation effect, we followed the resampling technique of bootstrapping, generating 5000 resamples as recommended by Hair et al. [40]. For each moderation path in the model, we utilized the specific indirect-effect function in Smart-PLS [40] to report the outcomes. We also reported the p-values and 95% confidence intervals (bias-corrected) in Table 6 to assess the significance of the proposed indirect results.
The results from the specific indirect function showed that COVID-19 moderates the relationship between agricultural input markets (AIM → COV.19 → CR.P, β = 0.220, t = 6.488, p ≤ 0.00), agricultural labor market (AIM → COV.19 → CR.P, β = 0.128, t = 5.143, p ≤ 0.00), and agricultural product market (AIM → COV.19 → CR.P, β = 0.203, t = 5.867, p ≤ 0.00). The mentioned results sustained H4a, H4b, and H4c.

5. Discussion and Conclusions

The proposed theoretical framework has provided valuable insights into the changes observed in agriculture markets and the corresponding shifts in cropping and consumption patterns in the mountainous regions of Pakistan. This research reinforces the interconnected nature of agricultural markets, inputs, labor, consumption, and production patterns, underlining the significance of holistic approaches in addressing the challenges facing the agricultural sector in the future.
Survey results demonstrated significant cross-loading values for constructs related to input market accessibility, supplies, quality, and affordability, indicating their importance in understanding the role of input markets in shaping cropping patterns. The overall model exhibited a calculated composite reliability of 0.829 and an average variance extracted of 0.548, indicating good data reliability and validity (Table 2). Research suggests input constraints have an influence on the cropping patterns of farming communities [15]. The influence is much more likely to be exacerbated in mountainous communities [17]. This validates Hypothesis 1 that agricultural input markets predict the changes in cropping patterns in the mountainous communities of Pakistan.
Hypothesis 2 suggests that agricultural labor markets play a significant role in predicting the changes in cropping patterns in mountainous communities in Pakistan. Data from pre-pandemic conditions demonstrated that farmers’ decisions regarding crop selection were affected by labor availability, affordability, and other factors within the input market. Crop diversification was embraced as a risk mitigation strategy for input market fluctuations, with studies revealing a correlation between increased labor availability and more diverse cropping patterns. To this effect, another study has observed a shift towards more lucrative crops over time resulting from changes in the labor market [45].
There are findings from the literature that helped in the formulation of Hypothesis 3 regarding the agricultural product markets, predicting the changes in cropping patterns in mountainous communities in Pakistan. Suggestions can be found about farmers’ cropping patterns being influenced by conditions in the product markets. The analysis of data gathered for this study and the ensuing results agreed with the previous works, such as [25,26,46]. It is pertinent to note from the results that agriculture inputs markets have a stronger prediction value for changes in cropping patterns compared to labor and product markets. This reveals an interesting facet of the nature of the food supply chain, whereby it is likely that farmers in the mountainous communities mostly employ labor from within their families or villages, leading to lesser uncertainties. They also have a better understanding of the product markets from years of experience. Hence, the pandemic, while disruptive, may not have influenced changes in cropping patterns as strongly as the inputs. A possible explanation for the greater influence could be that inputs have their own supply chain nodes further backward in the food chain that are beyond the control or influence of the farmers.
Besides the three antecedents to the agricultural markets, which are inputs, labor, and products, it was considered imperative to assess the moderating effect of COVID-19 on the changes in cropping patterns resulting from agriculture markets (Hypotheses H4a, b, and c). The COVID-19 pandemic has had significant impacts on various aspects of agricultural markets in the mountainous areas of Pakistan. Existing research [31,32] indicated that the pandemic had played a role in the farmers’ decisions concerning their input choices, the nature of labor engaged by them, and the conditions they had witnessed in the markets for their finished goods or products. However, the COVID-19 pandemic-imposed labor shortages disrupted agricultural and food markets. Movement restrictions and shifts in food demands further disrupted the agriculture sector, affecting farming families who depend on their own land and local markets for sustenance. Research conducted during the pandemic highlighted adverse effects on labor markets and agricultural production, raising concerns about food security and disruptions in value chains. These findings underscore the pandemic’s challenges for the agricultural labor market and the need for government intervention. Government policies have to grapple with a multitude of agricultural issues in times of crisis, particularly a pandemic in which food security and survival are at the forefront [47].
Firstly, the agriculture inputs market has seen disruptions due to the pandemic. Farmers faced challenges in accessing inputs such as seeds, fertilizers, and pesticides, which affected their ability to continue agricultural activities. Supply chain disruptions and restrictions on the movement of goods contributed to these difficulties. These disruptions in the input market have had implications for agricultural production and productivity in the mountainous regions.
The same is discussed in Hypothesis H4a, which states that COVID-19 moderates the structural relationships between agricultural input markets and changes in cropping patterns in mountainous communities in Pakistan.
Secondly, the study also discusses the impacts of COVID-19 on disruptions in the supply chain and labor shortages. The cross-loading results for our data support a strong relationship between agricultural labor availability, accessibility, cost, and changes in cropping patterns. Hence, it is concluded that agricultural labor markets have a significant impact on cropping patterns in mountainous communities in Pakistan, considering the influence of non-farm factors and the effects of the pandemic. The results endorse evidence from the field that the choice of crops by farmers is related to various non-farm-level factors, including the nature of labor available [22]. There is further evidence [48,49] suggesting that the lockdowns resulting from COVID-19 disrupted labor inputs and led to disruptions in crop production patterns. With the implementation of lockdowns and travel restrictions, many seasonal and migrant workers were unable to reach the mountainous areas to participate in agricultural activities. This labor shortage affected cropping activities, leading to decreased production and economic losses for farmers. This gives rise to Hypothesis H4b that COVID-19 moderates the structural relationships between agricultural labor markets and changes in cropping patterns in mountainous communities in Pakistan.
Thirdly, Hypothesis H4c theorizes that COVID-19 moderates the structural relationships between agricultural product markets and changes in cropping patterns in mountainous communities in Pakistan. It shows that the agricultural products market has been affected as the pandemic disrupted supply chains, leading to a decrease in cropping patterns. The results of this study confirmed the moderating influence of the pandemic, thus endorsing previous work that highlighted the positive impact of social support in moderating the relationship between risk expectation and farmers’ belief in recovery.
The study reveals significant factors such as poor-quality produce, lack of dietary modifications during COVID-19, loss of productivity due to market closures, and inadequate food supply for survival throughout the season. The findings underscore the hypothesis that COVID-19 moderates the relationships between agricultural markets and cropping patterns, but further investigation is needed to understand the specific impact of COVID-19 on cropping patterns in mountainous communities in Pakistan. The findings highlight challenges related to produce quality, dietary changes, market closures, and limited food supply, which have significant implications for food security and livelihoods in these regions.
Finally, Hypothesis 5 investigated the relationship between changes in cropping patterns and changes in consumption patterns in mountainous communities in Pakistan. Findings revealed that respondents perceive the current cropping pattern as important and express the need for it to be altered to ensure food and nutrition security. A desire for diversification in food consumption was hence observed, particularly toward healthier and organic options. In terms of consumption patterns, respondents consider the current pattern to be significant but acknowledge its inadequacy in providing food and nutrition security. The results of this study are consistent with [11,21]. As a result, all of our hypotheses (H1–H5) were successfully substantiated and supported by the research outcomes.
The pandemic highlighted the vulnerabilities of the food system and led to discussions about resilient food systems. Local and domestic production models were found to be more resilient compared to large-scale and global production models [50]. This scenario is likely because the local markets tend to have a limited scope where production and consumption decisions are taking place on a small scale thus making them less prone to pandemic-related disruptions compared to large-scale markets. However, the reliance on local resources for livelihoods and food security became even more evident during the pandemic, as restrictions on movement and trade did disrupt the availability and affordability of food in these regions. With COVID-19 having a far-reaching impact on global food systems, including the farming component [2,50], the possibility of cropping patterns being affected increases. Hence, our results further confirm previous findings.
It is important to note that the proposed theoretical model proved to be highly valuable in comprehending the desired investigation behavior and exhibited remarkable explanatory power in this study. The model effectively explained the notable shifts witnessed in agriculture markets and the subsequent changes in cropping and consumption patterns, specifically within the mountainous regions in Pakistan. Through rigorous analysis, a significant correlation was revealed between agricultural input markets, agricultural labor markets, and agricultural product markets, further strengthening the understanding of the interconnectedness between these factors and their influence on cropping and consumption patterns. Consequently, all our initial hypotheses (H1–H5) were robustly validated and supported by comprehensive research findings.
In conclusion, the results show significant impacts of agriculture inputs, labor, and consumption markets on the cropping patterns in the mountainous areas of Pakistan. On top of that, the COVID-19 pandemic did moderate the structural relationships between agricultural inputs, labor, and product markets in the cropping patterns of the high regions. It caused disruptions in supply chains, transportation, and access to inputs and affected the availability and productivity of agricultural products, while labor shortages have resulted in decreased production and economic losses for farmers. The pandemic has also highlighted the vulnerabilities of the food system and the importance of resilient and sustainable local production. Furthermore, the HTMT analysis establishes the discriminant validity of the constructs in the study, indicating that they measure distinct dimensions. The collinearity assessment confirms the absence of severe collinearity among the independent variables, ensuring the reliability and validity of the study’s analysis and conclusions.

6. Future Research Recommendations

Overall, the COVID-19 pandemic has had a profound impact on agriculture markets, inputs, labor, consumption, and production patterns in the mountainous areas of Pakistan. These disruptions have highlighted the need for improved resilience and adaptation strategies for the agricultural sector in the face of future crises. The findings of the current research underscore the significance of the proposed research model in comprehending the desired investigational behavior and its exceptional explanatory capabilities. The proposed model proved to be an appropriate fit for elucidating the transformations observed in agriculture markets as well as the corresponding alterations in cropping and consumption trends specifically within the mountainous regions of Pakistan. These observations signify the existence of significant relationships between agricultural input markets, agricultural labor markets, agricultural product markets, and the associated changes in cropping and consumption patterns. These findings emphasize the need for improved resilience and adaptation strategies in the agricultural sector to mitigate the impacts of future crises.
Some researchers have opined that the pandemic was likely to have a moderate effect on markets [3]. On the other hand, there does exist some contending research that, despite disruptions in input supply chains caused by the pandemic, cropping patterns amongst farmers remained unchanged [4]. It is due to these inconsistent findings from previous literature that there might still be a need to further investigate the moderating influence of the pandemic. Nevertheless, this research addresses a gap in existing literature, as no previous studies have explored the moderating effect of COVID-19 on the relationship between agricultural markets (input, labor, and product) and changes in cropping patterns in Pakistan’s mountainous regions.
Despite its strengths, the study has certain limitations that need to be pointed out as well. Firstly, due to the use of convenience sampling with a focus on mountainous communities residing in specific locations, findings cannot be generalized to other scenarios directly. Secondly, the data collection as part of the NMA project was carried out during times of lockdowns, limiting field work and access to respondents. Thirdly, while the study takes into account the influence of inputs, labor, and product markets on cropping and consumption patterns, it does not consider factors like weather, climate change, and natural disasters in these more vulnerable mountainous regions and the role they may have played in influencing the dynamics.
In order to further understand the nature of supply chains in general and agricultural food supply chains in particular, it is imperative that future researchers test this study’s model on farming communities in plain lands that are mostly characterized by large-scale production and relatively easier and more stable access to input, labor, and product markets. Further investigations can be carried out on additional nodes of the food supply chain whereby agricultural produce is further processed and packaged into commercial food products for consumption. Finding out how the model behaves in these contexts will help provide further theoretical and empirical insights.

Author Contributions

Conceptualization, M.A.S. and M.K.; methodology, M.A.S. and M.K.; data collection, M.A.S., F.D.K., A.N. and J.A.; formal analysis, M.A.S., F.D.K., M.N., M.K., A.N. and J.A.; writing—original draft preparation, M.A.S., F.D.K., M.N., M.K., A.N. and J.A.; writing—review and editing, M.N., A.N. and J.A.; visualization, M.K. and M.A.S.; project administration, M.A.S.; funding acquisition, A.N., J.A. and M.A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was conducted under the Nutrition in Mountain Agroecosystems project funded by the Swiss Agency for Development and Cooperation—Global Programme Food Security, grant number 81055242.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all farmers involved in the study.

Data Availability Statement

Due to privacy restrictions, we regretfully cannot provide any supporting material at this moment. However, we encourage readers who have any inquiries or require assistance to reach out to our team directly. Our dedicated team is here to help and will gladly provide the necessary support you need.

Acknowledgments

The authors acknowledge support from all the staff of Helvetas Swiss Intercooperation, especially Irshad Ali Mian—National Program Officer MEAL; Muhammad Riaz—Manager Administration; Attique Ahmed—Manager Finance; Shahid Mahmood—Admin and Finance Manager. We are thankful to all the partner organizations from Chitral, Haripur, Gilgit, Skardu, Hunza, Nagar, and Shigar for their support and correspondence with farmers, especially during the COVID-19 pandemic when it was impossible for the authors to visit and directly interact with the farmers.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 14934 g001
Figure 1. Map of Pakistan with provincial boundaries and project districts.
Figure 1. Map of Pakistan with provincial boundaries and project districts.
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Figure 2. Hypothesized Model.
Figure 2. Hypothesized Model.
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Table 1. Characteristics of Survey Sampling (n = 5273).
Table 1. Characteristics of Survey Sampling (n = 5273).
Demographics Statistics
SpecificationsN%
GenderMale334063.3
Female193336.7
AgeUnder-251042.0
25–35191436.3
36–45137826.1
46–55187735.6
LocationChitral107520.4
Haripur192536.5
Skardu143827.3
Gilgit551.0
Shigar78014.8
Was their area under lockdown during COVID-19?Yes5273100
No00
Table 2. Convergent Validity Assessment (n = 5273).
Table 2. Convergent Validity Assessment (n = 5273).
Constructs and ItemsCross LoadingsComposite ReliabilityAverage Variance Extracted
Agriculture Input Market
The input market was not accessible.0.7710.8290.548
The inputs market had no supplies.0.831
The inputs available were not of the desired quality.0.864
Inputs available were too expensive/not affordable due to a shortage of supplies.0.732
Agriculture Labor Market
Agriculture labor was not available.0.7620.7490.542
Agriculture labor was not accessible.0.869
Agriculture labor of the desired skillset was not available.0.763
Agriculture labor was too expensive/not affordable due to a shortage of supplies/high demand.0.883
Agriculture Product Market
The product produced did not fulfill market quality standards.0.8790.8070.582
The product was damaged due to a lack of storage facilities.0.79
The produce was damaged due to the absence of customers in the market.0.873
The product was not sold at the desired/market rate.
The demand for products produced in the local market was low.
The demand for products produced in other regions was high.
Effect of COVID-19
The produce was not of good quality.0.850.8280.547
The produce was not according to the dietary requirements/modifications during COVID.0.841
The produce was lost due to COVID associated market closures.0.792
The produce was not enough for survival through the season.0.75
Changes in Cropping Patterns
The current cropping pattern is important to me.0.7930.8180.529
The current cropping pattern is not suitable anymore (we need to diversify our food basket to deal with pandemics like COVID).0.743
The cropping pattern needs to change. We need to produce more healthy and organic food.0.817
The current cropping pattern does not ensure food and nutrition security for a cropping season.0.835
The current cropping pattern is primitive and needs technological advancement.0.778
Changes in Consumption Patterns
The current consumption pattern is important to me.0.8530.8420.571
The current cropping pattern does not ensure food and nutrition security for a cropping season.0.734
There is a need to consume more healthy and organic food.0.771
Diversification in the consumption of food will ensure more choices and reduce the risks of food insecurity.0.857
Table 3. Discriminant Validity Assessment (n = 5273).
Table 3. Discriminant Validity Assessment (n = 5273).
Heterotrait–Monotrait Ratio (HTMT)
Agricultural Input Market (AIM)
Changes in Cropping Patterns (CR.P)0.882
Effect of COVID-19 (COV.19)0.8560.849
Changes in Consumption Patterns (CON.P)0.8360.8550.789
Agricultural Product Market (APM)0.8460.7460.7870.776
Agricultural Labor Market (ALM)0.8950.7950.8010.7730.672
Table 4. Collinearity Assessment.
Table 4. Collinearity Assessment.
Independent Variables (IV’s)Tolerance (TOL)Variance Inflation Factor (VIF)
COVID-190.4172.159
Agricultural Input Market (AIM)0.5162.904
Changes in Cropping Patterns (CR.P)0.4182.180
Changes in Consumption Patterns (CON.P)0.5432.161
Agricultural Labor Market (ALM)0.6291.129
Agricultural Product Market (APM)0.5372.106
Table 5. Assessment of Structural Paths (Hypotheses testing).
Table 5. Assessment of Structural Paths (Hypotheses testing).
Structural Pathsβ-Valuet-Valueƒ2LLULResults
AIM → CR.P0.3356.0650.0690.1750.366Supported
ALM → CR.P0.2433.1280.0800.0440.184Supported
APM → CR.P0.2573.4850.0830.0430.163Supported
CR.P → CON.P0.60623.8590.5810.5510.654Supported
Table 6. PLS Moderation Effect.
Table 6. PLS Moderation Effect.
Structural Pathsβ-Valuet-Valuep ValuesStatus
AIM → COV.19 → CR.P0.2206.4880.000Supported
ALM → COV.19 → CR.P0.1285.1430.000Supported
APM → COV.19 → CR.P0.2035.8670.000Supported
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Khayyam, M.; Kamal, F.D.; Nouman, M.; Nizami, A.; Ali, J.; Salim, M.A. Agricultural Markets, Cropping Patterns, and Consumption Patterns: The Moderating Effect of COVID-19 on Mountainous Communities. Sustainability 2023, 15, 14934. https://doi.org/10.3390/su152014934

AMA Style

Khayyam M, Kamal FD, Nouman M, Nizami A, Ali J, Salim MA. Agricultural Markets, Cropping Patterns, and Consumption Patterns: The Moderating Effect of COVID-19 on Mountainous Communities. Sustainability. 2023; 15(20):14934. https://doi.org/10.3390/su152014934

Chicago/Turabian Style

Khayyam, Muhammad, Fatima Daud Kamal, Muhammad Nouman, Arjumand Nizami, Jawad Ali, and Muhammad Asad Salim. 2023. "Agricultural Markets, Cropping Patterns, and Consumption Patterns: The Moderating Effect of COVID-19 on Mountainous Communities" Sustainability 15, no. 20: 14934. https://doi.org/10.3390/su152014934

APA Style

Khayyam, M., Kamal, F. D., Nouman, M., Nizami, A., Ali, J., & Salim, M. A. (2023). Agricultural Markets, Cropping Patterns, and Consumption Patterns: The Moderating Effect of COVID-19 on Mountainous Communities. Sustainability, 15(20), 14934. https://doi.org/10.3390/su152014934

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