<figure><img src="image_1.png" /></figure>

RESEARCH ARTICLE

# R-Based Graphical Representation of Trends in Food Production and Agriculture Value Chains in India

Polina Lemenkova ID

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

University of Bologna, Department of Biological, Geological and Environmental Sciences, Bologna/Italy

ARTICLE INFO

**Article History**

Received: 27.10.2025

Accepted: 07.12.2025

First Published: 30.12.2025

**Keywords**

Agribusiness

Cartography

Farming

Food systems

R language

Sustainable agriculture

<figure><img src="image_4.png" /></figure>

ABSTRACT

This study analyses the dynamics in agricultural economics in India during 2000-2023. Agricultural economics in India is a critical sector, supporting population and contributing around to the GDP through food security, the economic growth, and rising exports. Nevertheless, agriculture of India strongly depends on climate and soil setting, as these factors affect the cultivation of crops and growth cycle. Several datasets on agriculture economics of India were evaluated to reveal trends in food production and show effects climate and soil types on agriculture. The materials include three types of data: agricultural production from Food and Agriculture Organization (FAO), climate data from Climate Change Knowledge Portal, soil data from FAO/UNESCO World Digital Soil, and administrative data on India from governmental map repository. The methodology is based on the statistical analysis and GIS mapping. Practical approach includes statistical analysis and plotting of parameters to analyse dynamics in regional context. Statistical analysis was performed by R libraries, while cartographic visualization was based on the QGIS software. The core R packages include 'ggplot2', 'tidyverse', 'dplyr', 'RColorBrewer', and 'viridisLite'. The results demonstrated dynamics in food production, export and consumption in India in recent two decades. The dominant role in export was identified as rice (basmati), spices, tea levels, fruits (mangoes) and cane sugar. The links between agriculture production, climate and soil setting shown that rising temperatures and extremes in precipitation negatively affect agricultural activities and food production in India by decreasing crop yields. This study demonstrated the use of R as effective method of large dataset processing for analysis of trends.

Please cite this paper as follows:

Lemenkova, P. (2025). R-based graphical representation of trends in food production and agriculture value chains in India. *Journal of Agricultural Production, 6*(4), 253-270. https://doi.org/10.56430/japro.1811604

1. Introduction

1.1. Background

Food production is the primary factor contributing to the economic development in India. The analysis of dynamics in agriculture production is significant in improving our knowledge of the effects from climate, as well as the impacts of soil on productivity. It is therefore essential to study the correlations between the interconnected components and trends in diverse structure of the agricultural production. Besides regional importance, the agricultural sustainability supports

societal development, trade policies and exchanges, health protection, political stability, and technological economic system in global food structure.

Complex structures in agrifood economics refer to the interconnected web of factors and actors that influence the food system, including diverse ownership forms, intricate supply chains, market and non-market interactions, and the complex interplay between social, economic, and environmental elements. Agrifood systems connect the food production, and their interactions with society, economy, and the environment. Such systems involve numerous actors, including farmers and

<figure><img src="image_5.png" /></figure>
consumers, and are influenced by a diversity of factors such as
climate change, population dynamics, economic growth,
ecological sustainability, and policy decisions (McBratney &
Park, 2025).

Hence, the agrifood systems presents a complex structure
with processes, people and products interconnected through
various levels of involvement, including international
agriculture trade communities (Demirci, 2025; Makita, 2011),
digital technologies (Bayrak & Olu, 2025; Stanescu et al., 2025)
and social systems (Komolafe et al., 2022; Sharmiladevi, 2023).
As a result, complex system of agrifood sector addresses
numerous risks related to food structure and yield sustainability
(Ciruela-Lorenzo et al., 2020; Forero et al., 2025; Randhawa et
al., 2021).

**1.2. Research Problem**

In India, the complexity of agricultural system is shaped by
a variety of actors, from individual consumers and small farms
diversified in various states to corporations and governmental
factories, all of whom are influenced by internal and external
factors like climate, technological advancements, and policy.
The conditions for economic growth in agricultural sector of
India strongly depend on the environmental sustainability and
geopolitical market. The dynamics of these factors supports
food production (Joshi, 2012; Szeląg-Sikora, et al., 2025). Both
for global trade and at regional level, the integration of these
factors ensures values in agriculture commerce (Bansal, 2011).
Economic growth in agriculture sector includes estimating risks
of farming within climate context and profitability of
cultivating crops on diverse soil types.

Understanding the structure of the agrifood systems
requires data analysis, modelling and visualization. The use of
advanced tools of data analysis enabled to reveal trends in the
complex structure of agrifood system, which strongly relies on
processing and interpreting external information through data
analysis and modelling (Pawar et al., 2022; Sumathi et al.,
2018). Data analysis for automatic identification of spatio-
temporal structures in agricultural sector has long been known
as an important research objective (Huynh et al., 2024; Malik
et al., 2020; Yadav et al., 2022a). Analysed and visualised data
are indicative of the climatic and soil variability in agricultural
components and enables to reveal complex links between the
components of the agrifood structures. Statistical methods have
been proven to be an efficient approach to identifying such
complex structures in agricultural economics.

Emerging trends in data analysis and modeling in
agriculture and environmental sustainability include the
integration of Artificial Intelligence (Mmbando, 2025; Sood et
al., 2022) and machine learning (Bali & Singla, 2021; Lingwal
et al., 2022). In this study, we employ such methods for
statistical analysis and graphical representation of trends in
agrifood system of India using R computational language.

**1.3. State of the Art**

Advanced methods of data analysis in environmental agriculture and food studies are becoming more powerful through programming algorithms of R (Marcu et al., 2019; Lemenkova, 2019b) or Python (Jayashree & Sumalatha, 2024; Lemenkova, 2019a, 2025a; Prathap et al., 2024). The latter utilises libraries of Pandas, NumPy, Matplotlib, and Seaborn, as reported in relevant studies (Lemenkova, 2019c; Savaliya et al., 2024). Techniques like augmented analytics (Rejeb et al., 2021), deep learning (Lemenkova, 2024d; Yakkala et al., 2024) and convolutional neural networks (El Sakka et al., 2024; Lemenkova, 2025b) significantly improve the performance of analytics in environmental studies through automation of data handling.

Other major trends in smart agriculture include real-time
data analysis, driven by the growth of the Internet of Things
(IoT) and edge computing (Yadav et al., 2022b). In this regard,
the important approach is given to scripting techniques which
ensure automation of data processing through increased speed
and accuracy in data handling (Lemenkova, 2022b, 2023).

To calculate the risks of farming, researches on agriculture
and environmental sustainability often employ a combination
of practical experience and data-driven theoretical methods of
complex landscape evaluation to make informed decisions on
yield predictability and nature protection (Jusoh et al., 2025;
Klaučo et al., 2017; Sahu et al., 2006). Such models include
diverse datasets which include both natural and economic
factors. The first ones include local setting on soil and terrain
(Klaučo et al., 2013) and biological hazards, such as pests and
diseases, or climate-related events of floods (K. B. Kumar et al.,
2025; Kuntla et al., 2025; S. Sharma & Mujumdar, 2024) or
droughts (Datta & Reddy, 2023; Lemenkova, 2022a, 2024c;
Todmal, 2024).

The economic risk factors balance the profitability of
harvest, estimate probability risks in planting target crops
resistant to natural hazards (Kopeć, 2024) for economic and
environmental sustainability (Ligarda-Samanez, et al., 2025).
While economic parameters of agriculture risks include
fluctuating market prices (Cao & Mohiuddin, 2019), interest
rate changes (Aye et al., 2025; Fiagbor & Brown, 2025) and
loan repayment issues, the institutional risks rely on the
government policies and trade tariffs. For instance, these
include the increased input costs for farmers, such as fertilizers
and equipment, and reduced export competitiveness leading to
lower crop prices and market share losses.

Another important issue for ensuring sustainable agriculture
is estimating crop health as a key factor for agriculture
profitability. Calculating vegetation health is possible through
advanced statistical tools, either mathematical or derived from
remote sensing (RS) data, or the combination of therein
(Kondoyanni et al., 2025; Lemenkova, 2025d; Lausch et al.,
2025; S. S. Sharma et al., 2025). For instance, satellite imagery
can be used to measure and monitor crop conditions like vigour,
stress, and yield. These techniques employ the predefined
formulae of Normalized Difference Vegetation Index, (NDVI)
and similar approaches (Lemenkova, 2024a, 2024b; Mingrone
et al., 2025). The RS technologies play a crucial role in
agricultural monitoring and crop prediction, as satellite data
analysis enable to estimate crop health, soil types, detect
diversified agriculture practices, improve field management,
and perform environmental analysis (Dutta Roy, et al., 2025;
Lemenkova, 2025c; N. Sharma et al., 2025).

The combination of the diverse approaches in methodology
that includes the variety of data ensures robust finding. To this
end, diverse techniques can be used for data processing through
statistical and geospatial analysis of trends and correlations in
multi-source data.

**1.4. Objectives and Goals**

The analysis of efficiency in food systems requires advanced methods of data analysis in the era of rapidly increasing data and the need to analyse them. In this study, we employ the techniques of R programming language for data analysis, modelling and visualization. The study goal is to analyse current trends in agriculture production in India diversified by the product types, economic benefits according to price and volume of the imported products to the global trade market, and financial value of the target variables: major agriculture producs (rice basmati, spices, tea levels, fruits, and cane sugar) and minor agriculture producs (cashew, cotton, coffee, jute, maize, fruits, rice (basmati), soya, sweet potatoes, spices (cumin, coriander, chilli) and wheat), produced and exported by India to the global agriculture sector.

**1.5. Study Area**

1.5.1. *Regional aspects*

In India, the key influencing factors of sustainable agriculture include several parameters, among which relief, climate, soil setting and economic sustainability. While natural factors largely control water availability and soil quality as essential components of agriculture (X. Li et al., 2024), the latter includes the production risk related to yield fluctuations and input costs (Y. Li & Cao, 2025), as well as human risk, such as personal labor availability (Bağırtan & Demir, 2021) and health (Rivera Chavez et al., 2025) which affect financial stability of food and agriculture sectors. Nowadays, the agriculture system of India experience a transformative period, caused by the environmental crisis, economic trade links and climate change (M. Sharma et al., 2025) or insect pests (G. Singh & Joshi, 2025). Contemporary farming systems are reformed with the goal to advance towards sustainable practices of precision agriculture and smart farming systems (Prasad et al., 2023).

The topography and physiography of India has a complex structure with diversified relief which creates perfect conditions for large-scaled agriculture plantations and their distribution across the subcontinent, Figure 1. The combination of these factors controls the possibility of agriculture development in diverse regions of India. The topography of India determines the parameters of landscape (slope steepness, terrain ruggedness), affecting water runoff and soil deposition, while climate (especially the monsoon cycles, temperature and rainfall) impacts weathering and rates of decomposition.

<figure><img src="image_6.png" /><figcaption>**Figure 1.** Administrative districts of India. Data: FAO. Software: QGIS. Source: Author.</figcaption></figure>
### 1.5.2. Climate change

The dynamics in agriculture production of India is influenced by the two parameters largely affected harvest: temperature and precipitation. These parameters vary according to the climate types in India and show seasonal fluctuations yearly, Table 1. The dominant climate of India is of monsoon tropical type which has regional variations due to a variety of factors. The most important factors affecting climate types in

India include the geographic location (latitude and altitude) and the geomorphology (Himalayan Mountain range), which largely influences temperature and rainfall patterns. Other factors contributes through the monsoon winds, which bring seasonal rains favouring plant growth, and the proximity to the Indian Ocean, which creates moderate coastal climates and extreme inland conditions. Such variability necessarily creates the conditions for specific crop types and controls the agricultural activities.

<table><caption>**Table 1.** Statistical data on climate parameters (temperature and precipitation) in India, 1991-2023. Data source: Climate Change Knowledge Portal.</caption><thead><tr><th>Month</th><th>Average Minimum Surface Air T (°C)</th><th>Average Mean Surface Air T (°C)</th><th>Average Maximum Surface Air T (°C)</th><th>Precipitation (mm)</th></tr></thead><tbody><tr><td>Jan</td><td>10.83</td><td>17.76</td><td>24.75</td><td>11.87</td></tr><tr><td>Feb</td><td>13.29</td><td>20.45</td><td>27.66</td><td>12.9</td></tr><tr><td>Mar</td><td>17.36</td><td>24.71</td><td>32.1</td><td>16.84</td></tr><tr><td>Apr</td><td>21.46</td><td>28.49</td><td>35.56</td><td>30.6</td></tr><tr><td>May</td><td>24.08</td><td>30.59</td><td>37.15</td><td>52.93</td></tr><tr><td>Jun</td><td>24.52</td><td>29.67</td><td>34.88</td><td>152.09</td></tr><tr><td>Jul</td><td>23.65</td><td>27.55</td><td>31.49</td><td>283.44</td></tr><tr><td>Aug</td><td>23.22</td><td>26.87</td><td>30.58</td><td>255.27</td></tr><tr><td>Sep</td><td>22.35</td><td>26.75</td><td>31.21</td><td>177.5</td></tr><tr><td>Oct</td><td>19.63</td><td>25.42</td><td>31.26</td><td>80.76</td></tr><tr><td>Nov</td><td>15.28</td><td>21.92</td><td>28.61</td><td>25.95</td></tr><tr><td>Dec</td><td>11.72</td><td>18.63</td><td>25.59</td><td>10.04</td></tr></tbody></table>

Recently, the climate of India is changing, with rising temperatures and more frequent extreme weather events: heatwaves, floods, and droughts. Such climate-related hazards significantly impact the agricultural activities. The long-term retrospective of climate warming in India shows gradual increase in temperatures during the 20th century, Figure 3. The increase in surface temperatures indicated year 2024 as the hottest year ever recorded since 1901. Key factors contributing

to the temperature warming in India include a recent increase in greenhouse gas emissions and natural phenomena, e.g., a strong El Niño event in 2024. The long-term trend shows a continuous increase of temperature in India which necessarily affects food production and agriculture activities. More specifically, a significant positive trends is visible between 5 decades of 1901-1950, 1979-2020, and overall, despite a temporary dip during 1951-1978, Figure 2.

<figure><img src="image_7.png" /><figcaption>**Figure 2.** Dynamics of climate warming in India for long-term period (1901-2024): the rise of surface air temperature in century retrospective. Source: Climate Change Knowledge Portal.</figcaption></figure>
The meteorological survey of India registered an average annual mean surface air temperature at 0.65°C above the long-term average (1991-2020), Figure 3.

<figure><img src="image_8.png" /><figcaption>Observed Climatology of Average Mean Surface Air Temperature</figcaption></figure>

**Figure 3.** Observed climatology of average mean surface temperature in India, 1991-2020. Source: Climate Change Knowledge Portal.

The analysis of recent trends in the annual mean temperature in India shows the rise by 0.69°C between 2000 and 2024, Table 1. The most intense and longest heatwave year of 2024 on record for India indicates temperatures in some northern regions reaching nearly 50°C, which is also reflected in average mean temperature that reached 25.43 °C.

Such increase in temperature leads to the significant challenges for food production in India, affects farmer livelihoods and leas to the economic instability. Thus, rising temperatures cause crop damage, erratic monsoons lead to droughts or floods along the coasts of the Bay of Bengal (Kantamaneni et al., 2019; Lemenkova, 2024e) or Arabian Sea (V. Kumar et al., 2020), and soil degradation exacerbated by extreme weather events (Patro & Bartakke, 2025). These factors significantly reduce yield of major crops, such as rice and wheat. Temperature and precipitation patterns in India experience seasonal cycles, following the monsoon periods and affecting agriculture growth cycle, Figure 4.

<table><caption>**Table 1.** Dynamics on temperature in India in recent two decades. Data source: Climate Change Knowledge Portal.</caption><thead><tr><th>Year</th><th>Average Mean Surface Air T (°C)</th><th>5-year T (°C) (Gaussian Smooth)</th></tr></thead><tbody><tr><td>2000</td><td>24.81</td><td>24.85</td></tr><tr><td>2001</td><td>24.93</td><td>24.89</td></tr><tr><td>2002</td><td>25.2</td><td>24.93</td></tr><tr><td>2003</td><td>24.94</td><td>24.98</td></tr><tr><td>2004</td><td>25.02</td><td>25.02</td></tr><tr><td>2005</td><td>24.77</td><td>25.05</td></tr><tr><td>2006</td><td>25.03</td><td>25.08</td></tr><tr><td>2007</td><td>25.06</td><td>25.1</td></tr><tr><td>2008</td><td>24.82</td><td>25.1</td></tr><tr><td>2009</td><td>25.57</td><td>25.09</td></tr><tr><td>2010</td><td>25.5</td><td>25.07</td></tr><tr><td>2011</td><td>24.88</td><td>25.04</td></tr><tr><td>2012</td><td>24.84</td><td>25.02</td></tr><tr><td>2013</td><td>24.71</td><td>25</td></tr><tr><td>2014</td><td>24.86</td><td>24.99</td></tr><tr><td>2015</td><td>24.98</td><td>24.99</td></tr><tr><td>2016</td><td>25.36</td><td>25</td></tr><tr><td>2017</td><td>25.23</td><td>25.03</td></tr><tr><td>2018</td><td>25.1</td><td>25.05</td></tr><tr><td>2019</td><td>25.03</td><td>25.08</td></tr><tr><td>2020</td><td>24.87</td><td>25.11</td></tr><tr><td>2021</td><td>25.07</td><td>25.13</td></tr><tr><td>2022</td><td>25.18</td><td>25.16</td></tr><tr><td>2023</td><td>25.03</td><td>25.19</td></tr><tr><td>2024</td><td>25.43</td><td>25.22</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>**Figure 4.** Observed seasonal cycle of meteorological parameters over India in 1991-2020. Source: Climate Change Knowledge Portal.</figcaption></figure>

1.5.3. Soil setting

Soil particle size determines its texture and significantly impacts absorbing potential. A balance of particle sizes (sand, silt, and clay) enables to achieve proper water retention, drainage, and aeration (Lindh & Lemenkova, 2022a, 2022b; Sun et al., 2025). Such soil property is essential for agricultural activities, as it directly affects the ability of soil to hold water and nutrients, allow for air circulation, and support root growth for plant health.

Varied topography and climate setting of India influence soil types by controlling key factors of agriculture: erosion, moisture distribution, organic matter content, and nutrient availability, Figure 5. Within the country, the most fertile and rich soils are young alluvial soil ('fluvisol' in Figure 5). This type of soil is dominating in the lowlands, the Indo-Gangetic Plain, floodplains and river deltas along the eastern coastal plains. Such soil types cover about 46% of the land area of India and are considered as the best soil type for agriculture. Fluvisols are highly fertile and support a wide variety of crops, including cereals (rice and wheat), sugarcane, cotton, pulses, oilseeds, vegetables, and fruits.

The 'Luvisols' are older, well-developed soils with a distinct clay-rich layer (an argillic horizon) that forms through the downward movement of clay particles to a subsurface layer with higher clay content. This type of soil is red-colored and derived from materials having neutral reaction: granite or gneiss. Such soil type has acceptable agricultural potential due to its clay content and fertility, especially in semi-arid regions.

The areas with distributed ‘gleysol’ (coloured orange on Figure 5) are unsuitable for agriculture, due to low fertility. Such type of soil has poor drainage leading to waterlogged, oxygen-poor conditions, which inhibits root growth, microbial activity, and nutrient availability for plant roots and soil organisms. Apart from these major soil types, different other soil compositions are distributed at higher altitudes of the mountainous regions and are suitable for tea leaves plantations on the stepped slopes of the mountainous terrace terrain.

The best soil for tea plantations on stepped slopes is Laterite soil (‘Rhodic Ferralsols’). Tea plants need well-drained soil to prevent waterlogging. Laterite soils, particularly those on hilly terraced slopes, provide this essential drainage due to its properties: sandy loam texture, deeply weathered and well-drained structure and acidic content (pH <7.0). Hence, tea leaves plantations of India are excellently grown on the laterite soil in the mountainous regions.

The best soil type for rice cultivation in India is alluvial soil, due to its high fertility and excellent moisture-retaining capacity. This fertile, clayey soil is found in river plains and deltas, is rich in essential nutrients like potash, phosphoric acid, and lime, and is easily tilled (Besky, 2024). The proximity of large rivers supports water sources, maintains soil moisture, and ensures crop yield. In turn, tropical monsoon climate favours the growth of water-intensive crops: rice, sugar cane, and tea. Consequently, these cultures are produced in large quantities and presents the essential part of the agriculture economics of the country.
<figure><img src="image_10.png" /><figcaption>**Figure 5.** Soil types in India. Data: FAO/UNESCO. Software: QGIS. Source: Author.</figcaption></figure>

**2. Materials and Methods**

To conduct a comprehensive analysis of agrifood systems with climate aspects, it is necessary to use several parameters: agriculture (statistical data on production in values and volume), environmental (maps of soil distribution), climate (data on temperature and precipitation). Processing and statistical interpreting such data are pivotal steps in the analysis of agrifood systems, as they determine the relevance of the results and enable to find correlations. In this study, we performed the statistical computing and graphical plotting using R language within the RStudio environment (R Core Team, 2024). To this end, this study adopts the integrated approach of statistical analysis and mapping trends in the agrifood system in India.

R presents a valuable tool for data modelling, visualization and analysis as a formal language with its specific syntax, rules and formats. An interactive environment for statistical computing and graphics, R supports diverse statistical methods through specially designed packages (libraries). Here, the 'ggplot2' was used as a major library for data plotting and modelling.

The data frame matrix was created using 'read_csv' command of 'readr' package. This package provides a fast method to read rectangular datasets from delimited files, such as comma-separated values (CSV). Hence, the data from the FAO was imported to the project as follows: `df <- read_csv("FAOSTAT_Prices.csv")`. The preliminary control of the data frame was done using command 'head(df)'.

The maps in Figure 1 and 5 were plotted using QGIS using existing methodology of cartographic workflow. The facetted plot in Figure 6 was modelled using the combination of functions 'geom_point()' and 'geom_line()' of R with the aid of package 'ggplot'. Here, the arguments of the functions are given as production types, years of production types (range from 2000 to 2023, according to the available data), value signifying the absolute values of the production (Tons), and the function 'facetWrap' that allows plotting several sub-graphs on the same graph.

The histograms in Figure 7 were plotted to represent the distribution of quantitative data as agriculture products in India in 12 categories for the recent two decades (2000-2023). The
function histogram was called by the `geom_histogram()` command of R package `ggplot2`. It automatically divided the dataset on agriculture production in India into bins for each category and counted the number of observations falling into each bin. The vertical line indicates the mean in each sub-graph for analysis of the most typical values.

The density of data distribution was graphically represented using `geom_density` command that shows the variations of a continuous variable as agriculture products by categories using a smooth, continuous curve, Figure 8. This technique is a useful approach for modelling shape of the cumulative datasets, and visualising spread of data distribution with most repetitive values and concentration of data.

The boxplot technique was called by the function `geom_boxplot` which was used for data processing, modelling and visualization. Boxplots provide a graphical comparison of several samples by categories (i.e., types of agriculture products). This function enables to test for the equality of the means of the several examples among the agriculture products in India. The additional aesthetics was controlled by the `theme()` function of R for this and other statistical plots. The comparison of plots indicates a significant difference in data distribution by agriculture products and normality of groups within the dataset. The equality of variances in the several samples can be compared using median, the first and third quartiles, Figure 9.

The economic output of the agriculture sector in India in Figure 10 was computed for the period of 2000-2023, where x axis indicates the Gross Production Value (GPV) in current US $ and y axis shows the products by category. This function was called through the command `geom_bar()` of R `ggplot2` package with relevant attributes. For representing multivariate data, we used function `geom_violin()` for plotting data distribution for Gross per Capita Production (GCP) Index for India (2000-2023), Figure 11. This function was combined with `geom_boxplot()` to highlight the locality and skewness groups by agriculture products for numerical values.

The pie chart function of R was employed to divide the dataset into several sectors where each segment represents a proportion in percentage of the whole, Figure 12. The visual representation of this graph was achieved through the combination of two functions of `ggplot2` package: `geom_bar()` and `coord_polar()` which created a circular view of the plot. To plot the circular graph in Figure 13, we employed the `library(dplyr)` which enables data manipulation for calculating percentages. Particularly, this function was executed by the `mutate()` command which creates new columns that depend on existing variables and are transformed into percentage rate of the whole.

Afterwards, we used the combination of the two functions of `ggplot2` - `geomRect()` and `coord_polar`($\theta$ = "y") which enable to represent the data in circular view. The result show the market share of major agriculture products in India for recent period. Finally, the plot in Figure 14 was prepared using the library(ggrides) of R which enables to show changes in distributions of the agriculture products over time.

### 3. Results and Discussion

The agriculture production of India was analysed on the country level with identified volumes (kg/ha) for production of items, statistical markers of trends in production data (median, quartiles and outliers), and total economic output in products (US$). The agriculture and food industry of India presents a major global player, making it a leading producer of selected products, such as tea leaves, fruits (mangoes, avocado, etc) pulses, and spices. Moreover, India is ranking second for rice, wheat, and sugarcane as visualised on the trends in the production of these categories in recent two decades, Figure 6.

The constant trend consists in the increase of production of rice, maize, tea leaves and wheat. In 2004, the tea production in India declined by 4.3% to ca. 820,200 tons due to adverse weather and the closure of tea gardens in Assam. This was part of a broader trend of depressed prices and economic challenges in the Indian tea industry. Since then, the increase in tea leaves production reached its current maximum of about 1.3 M tons in 2025.

The production of mangoes generally demonstrated the increase in production, despite some minor fluctuations in the period of 2017-2020, related to yield and instability in harvest. The same pattern is shown by the production of spices, cashew and tea leaves which experienced decrease in the production during the period of 2017-2020. The production of sweet potatoes covers a large part of the economy with rapid increase in the period of 2005-2015 which was maintained despite some fluctuations until now. The production of jute also shows gradual increase in the trend.

Histograms showing data distribution on agriculture products in India shows the distribution of various products by years, which fluctuates depending on the external factors: crop prices, farm sizes, and technical efficiency scores across different regions, Figure 7. The histograms illustrate the production of major products which includes both smallholders of the Indian farmers and large producers supported by the state. Other information that can be extracted from the histograms is the distribution of production of major agriculture types that depends on climate and soil properties.
<figure><img src="image_11.png" /><figcaption>**Figure 6.** Trends in the production of major agriculture products (100 t), 2000-2023, for India. Data: FAO. Software: R. Source: Author.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>**Figure 7.** Histograms showing data distribution on agriculture products in India, 2000-2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>

The share of top production by types illustrated on graph in Figure 8. Thus, India is the world's leading producer of jute, contributing nearly 60% of the global output which gives a significant source of livelihood for the population of the country. Moreover, the country produces over 810,000 tonnes of cashew nuts annually, making it a top global producer, Figure 8. The reasons for high cashew production are related to climate-

environmental factors, such as favorable climate in the coastal regions (states: Maharashtra, Andhra Pradesh, and Odisha), as well as a best soil types in these regions, favourable for cashew production: deep, well-drained, red sandy loam or laterite soils.

Other key agricultural exports include coffee which indicates the decrease in production. The reasons for decline in
coffee production consist in climate change and adverse weather, including a dry spell followed by excessive rain, which harm flowering and fruit development of coffee beans, leading to lower yields. As a result, the production of coffee became unprofitable and demonstrated decline, Figure 8.

The production of soya follows cycles in productivity supported by government initiatives to boost its production and

exports. India is the world's fifth-largest soybean producer, with the major producing states being Madhya Pradesh, Maharashtra, and Rajasthan. Regional environmental setting in these states favour the production of soybeans through the distribution of suitable for soybean soil: well-drained, loamy soil rich in organic matter with a pH of 6.0-7.5. Such type of soil offers the best conditions for seed germination and root development of soy.

<figure><img src="image_13.png" /><figcaption>**Figure 8.** Agriculture production of major crop types in India in absolute values (kg/ha), 2000-2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>

The agriculture sector faces challenges related to dependency on the monsoon and low productivity in areas with low fertile soil. The cumulative effects of climate and soil factors influence the productivity of crop types and affect the dynamics of their growth, Figure 9. The comparison of the values of the groups of agriculture products in India during recent two decades shows that the highest values are achieved by the categories 'sweet potatoes', 'tea leaves' and 'mangoes', Figure 9.

The highest values of the agriculture production are indicated by wheat, tea leaves and sweet potatoes with values reaching 12000 kg/ha. Besides, India is a significant producer of sweet potatoes, with a production of 1.1 to 1.2 M tons in recent two decades. The leading states are Odisha, Uttar Pradesh, and West Bengal, which account for a large portion of the total output in India. Suitable soil setting for the growth of sweet potato in Odisha consist in the distribution of fertile and well-drained sandy loam soil with pH between 5.8-6.7. The state of Uttar Pradesh has a diverse range of soils which create favourable setting for cultivation of sweet potatoes. These include alluvial soils in the plains, Mollisols in the Tarai region, and red and black soils in the southern Bundelkhand region.

Although the specific soil type varies by location, the variations of common types, such as loam, sandy loam and clay loam, and sandy soils are suitable for cultivation of sweet potatoes. Together with warm and tropical climate, soil setting makes these states suitable for production of sweet potatoes which shows the increase in production in recent decades.

The country is the second-largest world producer of wheat. In 2023, the total production reached ca. 112.743 M tonnes, with the main producing states being Uttar Pradesh, Punjab, and Haryana. The fertile loamy soil with moderate water-holding capacity as a mix of sand, silt, and clay shows favourable conditions for wheat cultivation in these states. India produces over a billion kilograms of tea leaves annually, making it the second-largest tea producer in the world after China. The major states producing tea leaves are Assam, West Bengal, and Tamil Nadu, where the industry employs over 2 M workers. Soil and climate favourable in these states make the key factors beneficial for tea leaves production. Favourable climate and soil settings ensure that India produced over 1.3 billion kg annually, which accounts for ca. 23% of the world's tea production with 1,382.03 M kg in recent year.
<figure><img src="image_14.png" /><figcaption>**Figure 9.** Box plots of density distribution for agriculture products in India, 2000-2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 10.** Economic output of agriculture sector in India for major crop types, 2000-2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>

The graph in Figure 10 shows the absolute leader in products is rice (basmati), followed by the other crop types. The basmati rice production is growing, with an estimated crop of over 13.1 M tons in recent years. Key growing states include Punjab, Haryana, and Uttar Pradesh, which account for the majority of production due to the favourable climate. The nation is the world's leading producer and exporter of rice, with exports showing a strong year-on-year growth, driven by global demand. At the same time, the production or rice depends on the meteorological setting and specifically, on monsoon cycles and fluctuations in temperature and precipitation.

Figure 11 shows the distribution of the gross per capita production (GPC) for India in 2000-2023. Here, the non-food production shows the smallest category, while the food production demonstrate the dominating part of the sector.

This graphs demonstrates that India has significant production of milk and meat. India is the world's largest milk producer, with an annual output of about 239.3 M tonnes in 2023, i.e., 24% of global production. The per-capita availability of milk has reached 471 grams per day. A major portion of this production comes from cows (53.12%), followed by buffaloes (43.62%). As for interpretation of meat production, India produced an estimated 10.25 M tonnes of meat in 2023, with a 4.95% increase from the previous year.

West Bengal is the top-producing state, followed by Uttar Pradesh, Maharashtra, Telangana, and Andhra Pradesh. In recent years, India produced approximately 810,000 T of cashew in 2022-2023, with a yearly growth rate of (4%). However, the production challenges due to erratic weather patterns and rising costs. The production is concentrated in the coastal regions of the peninsula, with Maharashtra leading as
the top producer. Major producing states include Maharashtra,
Andhra Pradesh, Kerala, Odisha, Karnataka, and Tamil Nadu.

<figure><img src="image_16.png" /><figcaption>**Figure 11.** Gross per capita production (GPC) for India, 2000-2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>

<figure><img src="image_17.png" /><figcaption>**Figure 12.** Production of the processed crops in India (Tons), from 2000 to 2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>

Figure 12 shows the comparative dynamics of the production of the processed crops in India in 2000 against 2023. The comparison of these graphs shows the increase in the

production of the processed crops in recent two decades. The production of molasses demonstrated the dynamics with values increased from 8022 to 11125 Tons, which can be explained by
the fact that it is a byproduct of sugar cane processing, where
India is a major global supplier.

Market share of the major agriculture products in India in
recent years is shown in Figure 13, including available data on
cashew, coffee, cotton, jute, maize, mangoes, rice, soya, spices,
sweet potatoes and wheat. This figure shows that in the

agriculture market of country, tea leaves, cereals (wheat, maize), fruits (mango) and grains (rice) have the largest share, making up about 49.50% of the market in 2024. Among them, rice is the leading agricultural export, followed by products like sugar, spices, and jute. Other key products include cashew cotton (highest production), and fresh fruits and vegetable, Figure 13.

<figure><img src="image_18.png" /><figcaption>**Figure 13.** Market share of the major agriculture products in India, 2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>

Figure 14 shows the data distribution in the producer's prices in India for major products in USD$/Tonne. The figures shows that the most expensive products are exotic fruits (mango), and spices, followed by other product types with cheaper ground price, such as peas, rice, soya beans and others. Such variability reflects the regional setting of soil and climate in India which affect the production which in turn regulate trade prices. Thus, in the context of agriculture, soil and climate demonstrate links of relationship: monsoon climate influence soil development by controlling soil moisture levels and causing intense weathering, which leads to specific soil types like laterite soil. At the same time, agricultural practices in turn influence both soil and climate in regions of intensive rice

plantations in India (especially, states Andhra Pradesh, West Bengal, Kerala, and Tamil Nadu).

Moreover, climate change poses significant challenges to agriculture of India through extreme weather that impacts soil and water resources in major rivers where agriculture activities are intense in deltas or banks: Ganga, Brahmaputra, Indus, Godavari, Krishna, and Narmada. At the same time, agricultural practices can mitigate climate change by managing soil carbon sequestration. Such complex cycle of links between agricultural practices, soil setting and climate change forms challenging conditions for agrarian development of India and supporting its economic growth.

<figure><img src="image_19.png" /><figcaption>**Figure 14.** Producer prices in major agriculture products of India, 2000-2023. Data: FAO. Software: R. Source: Author.</figcaption></figure>
Overall, the analysis of the data shows that the agriculture industry in India significantly increased in size from 2000 to 2023. The trade market of the country exhibited a growth rate of over 10% during 2000-2023. The growth is driven by a combination of government support for agriculture subsidies, increased cropping intensity with the use of modern fertilisers, and expansion in allied activities. Nevertheless, the development of agriculture is constrained by climate change and soil factors, distribution of regional soil types or soil degradation as a result of overuse. Another aspect is related to the climate change and weather patterns, with increasing resilience of smart agriculture towards natural disasters (floods along the coasts of the Indian Ocean).

To conclude, data analytics in agricultural sector enables to monitor the effectiveness of cultivation of plants and their responses to climate change and soil setting. The results of data analysis can enables to optimise crop growth in majority of Indian crops through improved resource management and supported decision-making. AI systems analyse data from satellites to monitor crop health, detect diseases, manage irrigation, control water demand and provide recommendations for irrigation and fertilisation (Sajib & Sayem, 2025), and pest control (Diaz-Delgado et al., 2025). Such technologies help increasing crop yields, reduce waste, and address climate-related environmental challenges. For example, advanced modelling techniques enable to predict harvest times, which is important for monsoon climate of India.

Finally, the perspectives in the technological progress in agriculture of India can be directed towards the precision farming optimised through the artificial intelligence (AI). Such advanced technologies improve the harvest outputs in India through increased efficiency and sustainability (Gopi et al., 2023). For instance, recent modernisation of farming techniques of India includes the applications of precision farming, which optimises the use of resources, and automation (R. Singh & Singh, 2025). Moreover, the use of robots handles repetitive tasks like weeding and harvesting (Jia et al., 2025).

## 4. Conclusion

Major trends in agriculture production of India were revealed to assess the dynamics in food agroecosystem related to production and costs and identify sustainable practices. In this paper, we demonstrated that the effectiveness of the agricultural economics of India largely depends on the cumulative effects of natural factors (soil, climate and monsoon seasonality) and technological factors (irrigation facilities, infrastructure and labour work). The use of R programming libraries is advised for creating a series of statistical multi-facetted graphs through modelling by packages. The visualized dataset included a variety of categories for analysis of agriculture market.

The cumulative measures of sustainable agriculture in India support resilient food systems for regional market and global trade connections. When analysing the data for decision making in advanced agriculture practices, the essential is the balance between the environmental soil setting, sustainable use of fertilisers and pesticides to prevent soil degradation, economic profitability and economic benefits for the country. The latter include crop rotation, integrated pest management, and water conservation.

In this paper, key items in food production and agriculture sector of India are analyzed for their productivity and economic impact in the context of climate change and regional soil setting. The selected categories included major products in agriculture sector of India, such as cashew, cotton, coffee, jute, maize, fruits, rice (basmati), soya, sweet potatoes, spices (cumin, coriander, chilli) and wheat. By use of R-based data representation in analysis of agricultural trends, this manuscript contributes to the monitoring of agriculture in India. The findings provide insights for advancing food security in India through support of the decision-making.

The results have technological and practical applications in agricultural monitoring of India. The integration of R statistical libraries and FAO dataset as a tool for analysis of agriculture market which proved to be a robust strategy for identifying trends in major products with maximum increase and potential of further development. Such approach can be tailored and applied in other geographic regions to provide the analysis of food and agriculture sector and trade market.

For future sustainable development of effective agriculture sector in India, maintaining practices of organic farming is recommended. This include, for instance, developing precision agriculture and adopted eco-friendly concepts, policies, and sustainable methodologies of farming and planting. A large portion of the population in India depend on agriculture for their livelihood, therefore, maintaining research on agriculture production, dynamics of prices and production is important. Besides, India is an important player on the global market of food and agriculture exchange between countries, therefore, constant and smooth trends in production of India (e.g., tea leaves, rice, maize, fruits) are essential for the world trademark network. Future studies can also address the integration of additional criteria, i.e., socio-economic and financial parameters and environmental policy decisions that support precision agriculture. Such approach could extend the R-based decision-making modelling. Finally, the sensitivity analyses and uncertainty checks can additionally support information extraction regarding the robustness of the presented results for analysis of the agriculture and food sector as vital for societal development.

## Compliance with Ethical Standards

This study does not require ethical committee approval.
**Conflict of Interest**

The author has no conflict of interest to declare.

**References**

Aye, G. C., Kotur, L. N., & Ater, P. I. (2025). Threshold effects of economic-policy uncertainty on food security in Nigeria. *Journal of Risk and Financial Management*, **18**(2), 68. https://doi.org/10.3390/jrfm18020068

Bağırtan, S., & Demir, N. (2021). Investigation of seasonal female and child labor use in cotton agriculture: The case of Mardin province. *Journal of Agricultural Production*, **2**(1), 16-25. https://doi.org/10.29329/agripro.2021.344.3

Bali, N., & Singla, A. (2021). Deep learning based wheat crop yield prediction model in Punjab region of north India. *Applied Artificial Intelligence*, **35**(15), 1304-1328. https://doi.org/10.1080/08839514.2021.1976091

Bansal, R. (2011). Growth of the electronic commerce in China and India: A comparative study. *Journal of Asia-Pacific Business*, **12**(4), 356-374. https://doi.org/10.1080/10599231.2011.611457

Bayrak, T., & Olu, M. (2025). Applications of artificial intelligence in smart agriculture: Plant health, drone technology, and digital communication. *Journal of Agricultural Production*, **6**(3), 157-166. https://doi.org/10.56430/japro.1767174

Besky, S. (2024). Regeneration and its discontents: Biodynamic agriculture and Darjeeling tea plantations. *Social & Cultural Geography*, **26**(9), 977-996. https://doi.org/10.1080/14649365.2024.2441767

Cao, Y., & Mohiuddin, M. (2019). Sustainable emerging country agro-food supply chains: Fresh vegetable price formation mechanisms in rural China. *Sustainability*, **11**(10), 2814. https://doi.org/10.3390/su11102814

Ciruela-Lorenzo, A. M., Del-Aguila-Obra, A. R., Padilla-Meléndez, A., & Plaza-Angulo, J. J. (2020). Digitalization of Agri-cooperatives in the smart agriculture context. Proposal of a digital diagnosis tool. *Sustainability*, **12**(4), 1325. https://doi.org/10.3390/su12041325

Datta, R., & Reddy, M. J. (2023). Trivariate frequency analysis of droughts using copulas under future climate change over Vidarbha region in India. *Stochastic Environmental Research and Risk Assessment*, **37**, 3855-3877. https://doi.org/10.1007/s00477-023-02484-3

Demirci, S. (2025). Balancing trade and sustainability: Türkiye's squid and cuttlefish market dynamics. *Journal of Agricultural Production*, **6**(1), 53-60. https://doi.org/10.56430/japro.1617852

Diaz-Delgado, D., Rodriguez, C., Bernuy-Alva, A., Navarro, C., & Inga-Alva, A. (2025). Optimization of vegetable production in hydroculture environments using artificial

intelligence: A literature review. *Sustainability*, **17**(7), 3103. https://doi.org/10.3390/su17073103

Dutta Roy, A., Ranglong, A., Timilsina, S., Das, S. K., Watt, M. S., de-Miguel, S., Deb, S., Sahoo, U. K., & Mohan, M. (2025). Spaceborne LiDAR reveals anthropogenic and biophysical drivers shaping the spatial distribution of forest aboveground biomass in eastern Himalayas. *Land*, **14**(8), 1540. https://doi.org/10.3390/land14081540

El Sakka, M., Mothe, J., & Ivanovici, M. (2024). Images and CNN applications in smart agriculture. *European Journal of Remote Sensing*, **57**(1), 2352386. https://doi.org/10.1080/22797254.2024.2352386

Fiagbor, R., & Brown, O. (2025). Assessing the 10-item Food Security Survey Model (FSSM): Insights from college students in three US universities. *Nutrients*, **17**(6), 1050. https://doi.org/10.3390/nu17061050

Forero, Á., Cruz, J. C., & Muñoz, C. (2025). Adoption of agricultural innovations within the ‘farm to fork’ strategy: A realistic review of barriers, paradoxes, and avenues for change. *Sustainability*, **17**(21), 9493. https://doi.org/10.3390/su17219493

Gopi, A., Sharma, P., Sudhakar, K., Ngui, W. K., Kirpichnikova, I., & Cuce, E. (2023). Weather impact on solar farm performance: A comparative analysis of machine learning techniques. *Sustainability*, **15**(1), 439. https://doi.org/10.3390/su15010439

Huynh, T. N., Burgers, T., & Nguyen, K.-D. (2024). Efficient real-time droplet tracking in crop-spraying systems. *Agriculture*, **14**(10), 1735. https://doi.org/10.3390/agriculture14101735

Jayashree, S., & Sumalatha, V. (2024). Performance juxtapose of plant leaf disease detection using adaptive deep convolutional recurrent neural network (ADCRNN) in MATLAB versus python. 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). Hassan. https://doi.org/10.1109/IACIS61494.2024.10721899

Jia, W., Zhang, Z., Dong, X., Ou, M., Gao, R., Wang, Y., Yang, Q., & Wang, X. (2025). Quantification and optimization of straight-line attitude control for orchard weeding robots using adaptive pure pursuit. *Agriculture*, **15**(19), 2085. https://doi.org/10.3390/agriculture15192085

Joshi, R. M. (2012). Emerging challenges under WTO to international dairy trade with special reference to India. *Transnational Corporations Review*, **4**(3), 59-76. https://doi.org/10.1080/19186444.2012.11658335

Jusoh, A., Abbas, A. F., Abu Yazid, M. H., Latip, H. A., & Che Hussin, A. R. (2025). Cultivating digital pathways: A systematic review of e-commerce adoption in Asian agriculture. *Journal of Agricultural & Food Information*, **1-37**. https://doi.org/10.1080/10496505.2025.2553282

Kantamaneni, K., Sudha Rani, N. N. V., Rice, L., Sur, K., Thayaparan, M., Kulatunga, U., Rege, R., Yenneti, K.,
& Campos, L. C. (2019). A systematic review of coastal vulnerability assessment studies along andhra pradesh, India: A critical evaluation of data gathering, risk levels and mitigation strategies. *Water*, **11**(2), 393. https://doi.org/10.3390/w11020393

Klaučo, M., Gregorová, B., Stankov, U., Marković, V., & Lemenkova, P. (2013). Determination of ecological significance based on geostatistical assessment: A case study from the Slovak Natura 2000 protected area. *Open Geosciences*, **5**(1), 28-42. https://doi.org/10.2478/s13533-012-0120-0

Klaučo, M., Gregorová, B., Koleda, P., Stankov, U., Marković, V., & Lemenkova, P. (2017). Land planning as a support for sustainable development based on tourism: A case study of Slovak rural region. *Environmental Engineering and Management Journal*, **16**(2), 449-458. https://doi.org/10.30638/eemj.2017.045

Komolafe, O. J., Nwankwo, T. N., & Chilaka, P. C. (2022). Willingness of agriculture students to be involved in agripreneur career in southeast Nigeria. *Journal of Agricultural Production*, **3**(1), 9-16. https://doi.org/10.29329/agripro.2022.413.2

Kondoyanni, M., Loukatos, D., Templalexis, C., Lentzou, D., Xanthopoulos, G., & Arvanitis, K. G. (2025). Computer vision in monitoring fruit browning: Neural networks vs. stochastic modelling. *Sensors*, **25**(8), 2482. https://doi.org/10.3390/s25082482

Kopeć, P. (2024). Climate change—the rise of climate-resilient crops. *Plants*, **13**(4), 490. https://doi.org/10.3390/plants13040490

Kumar, K. B., Sharma, S., Das Bhowmik, R., & Mujumdar, P. P. (2025). Spatial synchronization of river floods growing beyond the basin boundaries in Peninsular India. *Scientific Reports*, **15**, 18160. https://doi.org/10.1038/s41598-025-02922-y

Kumar, V., Pradhan, P. K., Sinha, T., Rao, S. V. B., & Chang, H.-P. (2020). Interaction of a low-pressure system, an offshore trough, and mid-tropospheric dry air intrusion: The Kerala flood of august 2018. *Atmosphere*, **11**(7), 740. https://doi.org/10.3390/atmos11070740

Kuntla, S. K., Saharia, M., & Jain, S. K. (2025). The changing magnitude and timing of riverine floods in India. *npj Natural Hazards*, **2**, 44. https://doi.org/10.1038/s44304-025-00099-y

Lausch, A., Bumberger, J., Jung, A., Pause, M., Selsam, P., Zhou, T., & Herzog, F. (2025). Monitoring agricultural land use intensity with remote sensing and traits. *Agriculture*, **15**(21), 2233. https://doi.org/10.3390/agriculture15212233

Lemenkova, P. (2019a). Testing linear regressions by StatsModel library of python for oceanological data interpretation. *Aquatic Sciences and Engineering*, **34**(2), 51-60. https://doi.org/10.26650/ASE2019547010

Lemenkova, P. (2019b). Statistical analysis of the mariana trench geomorphology using R programming language. *Geodesy and Cartography*, **45**(2), 57-84. https://doi.org/10.3846/gac.2019.3785

Lemenkova, P. (2019c). Processing oceanographic data by Python libraries NumPy, SciPy and Pandas. *Aquatic Research*, **2**(2), 73-91. https://doi.org/10.3153/AR19009

Lemenkova, P. (2022a). Mapping climate parameters over the territory of Botswana using GMT and gridded surface data from TerraClimate. *ISPRS International Journal of Geo-Information*, **11**(9), 473. https://doi.org/10.3390/ijgi11090473

Lemenkova, P. (2022b). Console-based mapping of Mongolia using GMT cartographic scripting toolset for processing TerraClimate data. *Geosciences*, **12**(3), 140. https://doi.org/10.3390/geosciences12030140

Lemenkova, P. (2023). Using open-source software GRASS GIS for analysis of the environmental patterns in Lake Chad, Central Africa. *Die Bodenkultur: Journal of Land Management, Food and Environment*, **74**(1), 49-64. https://doi.org/10.2478/boku-2023-0005

Lemenkova, P. (2024a). Exploitation d'images satellitaires Landsat de la région du Cap (Afrique du Sud) pour le calcul et la cartographie d'indices de végétation à l'aide du logiciel GRASS GIS. *Physio-Géo*, **20**, 113-129. https://doi.org/10.4000/11pyj (In French)

Lemenkova, P. (2024b). Approche cartographique par le SIG GRASS pour l'analyse de la structure du paysage au Libéria, Afrique de l'Ouest. *Dynamiques environnementales*, **53**, 1-36. https://doi.org/10.4000/12n01

Lemenkova, P. (2024c). Deep learning methods of satellite image processing for monitoring of flood dynamics in the Ganges Delta, Bangladesh. *Water*, **16**(8), 1141. https://doi.org/10.3390/w16081141

Lemenkova, P. (2024d). Artificial intelligence for computational remote sensing: Quantifying patterns of land cover types around Cheetham wetlands, Port Phillip Bay, Australia. *Journal of Marine Science and Engineering*, **12**(8), 1279. https://doi.org/10.3390/jmse12081279

Lemenkova, P. (2024e). Artificial neural networks for mapping coastal lagoon of Chilika Lake, India, using earth observation data. *Journal of Marine Science and Engineering*, **12**(5), 709. https://doi.org/10.3390/jmse12050709

Lemenkova, P. (2025a). Automation of image processing through ML algorithms of GRASS GIS using embedded Scikit-Learn library of Python. *Examples and Counterexamples*, **7**, 100180. https://doi.org/10.1016/j.exco.2025.100180

Lemenkova, P. (2025b). Machine learning algorithms of remote sensing data processing for mapping changes in land
cover types over central Apennines, Italy. *Journal of Imaging*, **11**(5), 153.
https://doi.org/10.3390/jimaging11050153

Lemenkova, P. (2025c). Reclassification scheme for image analysis in GRASS GIS using gradient boosting algorithm: A case of Djibouti, East Africa. *Journal of Imaging*, **11**(8), 249.
https://doi.org/10.3390/jimaging11080249

Lemenkova, P. (2025d). Suivi de la dynamique du paysage à l'aide de l'analyse d'images satellite par apprentissage automatique du SIG GRASS dans le Parc National du W, nord du Bénin. *Bulletin de L'association de Géographes Français*, **102**(1),133-154. https://doi.org/10.4000/15e84 (In French)

Li, X., Li, Z., Fu, W., & Li, F. (2024). The influence of shallow groundwater on the physicochemical properties of field soil, crop yield, and groundwater. *Agriculture*, **14**(3), 341. https://doi.org/10.3390/agriculture14030341

Li, Y., & Cao, W. (2025). Green and efficient technology investment strategies for a contract farming supply chain under the CVaR criterion. *Sustainability*, **17**(17), 7600.
https://doi.org/10.3390/su17177600

Ligarda-Samanez, C. A., Huamán-Carrión, M. L., Calsina-Ponce, W. C., Cruz, G. D. I., Calderón Huamaní, D. F., Cabel-Moscoso, D. J., Garcia-Espinoza, A. J., Sucari-León, R., Aroquipa-Durán, Y., Muñoz-Saenz, J. C., Muñoz-Melgarejo, M., & Jilaja-Carita, E. E. (2025). Technological innovations and circular economy in the valorization of agri-food by-products: Advances, challenges and perspectives. *Foods*, **14**(11), 1950. https://doi.org/10.3390/foods14111950

Lindh, P., & Lemenkova, P. (2022a). Permeability, compressive strength and Proctor parameters of silts stabilised by Portland cement and ground granulated blast furnace slag (GGBFS). *Archive of Mechanical Engineering*, **69**(4), 667-692.
https://doi.org/10.24425/ame.2022.141522

Lindh, P., & Lemenkova, P. (2022b). Dynamics of strength gain in sandy soil stabilised with mixed binders evaluated by elastic P-waves during compressive loading. *Materials*, **15**(21), 7798. https://doi.org/10.3390/ma15217798

Lingwal, S., Bhatia, K. K., & Singh, M. (2022). A novel machine learning approach for rice yield estimation. *Journal of Experimental & Theoretical Artificial Intelligence*, **36**(3), 337-356.
https://doi.org/10.1080/0952813X.2022.2062458

Makita, R. (2011). A confluence of Fair Trade and organic agriculture in southern India. *Development in Practice*, **21**(2), 205-217.
https://doi.org/10.1080/09614524.2011.543277

Malik, A., Rai, P., Heddam, S., Kisi, O., Sharafati, A., Salih, S. Q., Al-Ansari, N., & Yaseen, Z. M. (2020). Pan evaporation estimation in Uttarakhand and Uttar Pradesh states, India: Validity of an integrative data

intelligence model. *Atmosphere*, **11**(6), 553.
https://doi.org/10.3390/atmos11060553

Marcu, I., Suciu, G., Bălăceanu, C., Drăgulinescu, A. -M., & Dobrea, M. A. (2019). *IoT solution for plant monitoring in smart agriculture*. IEEE 25th International Symposium for Design and Technology in Electronic Packaging (SIITME). Cluj-Napoca.
https://doi.org/10.1109/SIITME47687.2019.8990798

McBratney, A., & Park, M. (2025). Agriculture over the horizon: A synthesis for the Mid-21st century. *Sustainability*, **17**(21), 9424. https://doi.org/10.3390/su17219424

Mingrone, M., Seracini, M., & Cevoli, C. (2025). Spectral reconstruction applied in precision agriculture: On-field solutions. *Applied Sciences*, **15**(20), 10985.
https://doi.org/10.3390/app152010985

Mmbando, G. S. (2025). Harnessing artificial intelligence and remote sensing in climate-smart agriculture: The current strategies needed for enhancing global food security. *Cogent Food & Agriculture*, **11**(1).
https://doi.org/10.1080/23311932.2025.2454354

Patro, B. S., & Bartakke, P. P. (2025). Collaborative station learning for rainfall forecasting. *Atmosphere*, **16**(10), 1197. https://doi.org/10.3390/atmos16101197

Pawar, R. S., Nema, S., Jawale, D. R., Joshi, K., Debnath, S., & Singh, S. P. (2022). *The role of innovative data mining approaches for analyzing and estimating the crop yield in agriculture among emerging nations*. 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). Greater Noida.
https://doi.org/10.1109/ICACITE53722.2022.9823729

Prasad, R., Tiwari, R., & Srivastava, A. K. (2023). Internet of things-based fuzzy logic controller for smart soil health monitoring: A case study of semi-arid regions of India. *Engineering Proceedings*, 58(1), 85.
https://doi.org/10.3390/ecsa-10-16208

Prathap, C., Sivaranjani, S., & Sathya, M. (2024). ML-based yield prediction in smart agriculture systems using IoT. 5th International Conference on Innovative Trends in Information Technology (ICITIIT). Kottayam.
https://doi.org/10.1109/ICITIIT61487.2024.10580172

R Core Team. (2024). *R: A language and environment for statistical computing*. https://www.R-project.org/

Randhawa, M., Dhaliwal, S. S., Sharma, V., Toor, A. S., Sharma, S., & Kaur, M. (2021). Ensuring yield sustainability and nutritional security through enriching manures with fertilizers under rice-wheat system in North-western India. *Journal of Plant Nutrition*, **45**(4), 540-557.
https://doi.org/10.1080/01904167.2021.1943748

Rejeb, A., Rejeb, K., & Keogh, J. G. (2021). Enablers of augmented reality in the food supply Chain: A systematic literature review. *Journal of Foodservice Business Research*, **24**(4), 415-444.
https://doi.org/10.1080/15378020.2020.1859973
Rivera Chavez, Z. B., Porcaro, A., De Simone, M. C., & Guida, D. (2025). Improving sustainable viticulture in developing countries: A case study. *Sustainability*, **17**(12), 5338. https://doi.org/10.3390/su17125338

Sahu, P. K., Kundu, A. L., Samui, R. C., Mani, P. K., & Pramanick, M. (2006). Sustainable maximization of yield in rice-wheat system of cropping through application of phosphocompost: A farmers’ field study. *Journal of Journal of New Seeds*, **8**(2), 73-84. https://doi.org/10.1300/J153v08n02_05

Sajib, M. M. H., & Sayem, A. S. M. (2025). Innovations in sensor-based systems and sustainable energy solutions for smart agriculture: A review. *Encyclopedia*, **5**(2), 67. https://doi.org/10.3390/encyclopedia5020067

Savaliya, L., Sapovadiya, M., Garg, D., Patel, P., & Shah, M. (2024). *Agricultural analysis of machine learning algorithms for crop prediction*. 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). Kirtipur. https://doi.org/10.1109/I-SMAC61858.2024.10714828

Sharma, S., & Mujumdar, P. P. (2024). Baseflow significantly contributes to river floods in Peninsular India. *Scientific Reports*, **14**, 1251. https://doi.org/10.1038/s41598-024-51850-w

Sharma, M., Joshi, S., Gupta, P., & Joshi, T. (2025). Climate, crops, and communities: Modeling the environmental stressors driving food supply chain insecurity. *Earth*, **6**(4), 121. https://doi.org/10.3390/earth6040121

Sharma, N., Singh, S., & Kaur, K. (2025). Optimised DNN-based agricultural land mapping using sentinel-2 and landsat-8 with google earth engine. *Land*, **14**(8), 1578. https://doi.org/10.3390/land14081578

Sharma, S. S., Mukherjee, J., & Dell'Acqua, F. (2025). Leveraging sentinel-2 data and machine learning for drought detection in India: The process of ground truth construction and a case study. *Remote Sensing*, **17**(18), 3159. https://doi.org/10.3390/rs17183159

Sharmiladevi, J. C. (2023). Impact study of agricultural value added on foreign direct investment, economic development, trade openness for India following ARDL approach. *Cogent Economics & Finance*, **11**(2). https://doi.org/10.1080/23322039.2023.2270595

Singh, G., & Joshi, N. K. (2025). Insect pests of wheat in north India: A comprehensive review of their bio-ecology and integrated management strategies. *Agriculture*, **15**(19), 2067. https://doi.org/10.3390/agriculture15192067

Singh, R., & Singh, S. (2025). A review of Indian-based drones in the agriculture sector: Issues, challenges, and solutions. *Sensors*, **25**(15), 4876. https://doi.org/10.3390/s25154876

Sood, A., Bhardwaj, A. K., & Sharma, R. K. (2022). Towards sustainable agriculture: Key determinants of adopting artificial intelligence in agriculture. *Journal of Decision Systems*, **33**(4), 833-877. https://doi.org/10.1080/12460125.2022.2154419

Stanescu, S.-G., Ionescu, C. A., Ștefan, M. C., Ionescu, L., Bondac, G.-T., & Cristea, A. M. (2025). Digitalization and blockchain integration in agri-food supply chains: Towards a resilient, circular, and sustainable future. *Sustainability*, **17**(20), 9276. https://doi.org/10.3390/su17209276

Sumathi, K., Santharam, K., & Selvalakshmi, N. (2018). *Data analytics platform for intelligent agriculture*. 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). Palladam. https://doi.org/10.1109/I-SMAC.2018.8653740

Sun, C., Yang, J., Liu, Q., Wu, Y., & Miao, J. (2025). The effect of admixing different types of fine particles into the coarse-grained layer on a capillary barrier cover's water storage capacity: A laboratory study. *Sustainability*, **17**(22), 10301. https://doi.org/10.3390/su172210301

Szeląg-Sikora, A., Oleksy-Gębczyk, A., Rydwańska, P., Kowalska-Jarnot, K., Kochanek, A., & Generowicz, A. (2025). Sustainable food consumption and the attitude-behavior gap: Factor analysis and recommendations for marketing communication. *Sustainability*, **17**(21), 9476. https://doi.org/10.3390/su17219476

Todmal, R. S. (2024). Intensity, frequency and coverage of hydro-meteorological droughts and agriculture in the semi-arid basins of Maharashtra (India). *Climatic Change*, **177**, 140. https://doi.org/10.1007/s10584-024-03794-3

Yadav, A., Chithaluru, P., Singh, A., Joshi, D., Elkamchouchi, D. H., Pérez-Oleaga, C. M., & Anand, D. (2022a). An enhanced feed-forward back propagation Levenberg Marquardt algorithm for suspended sediment yield modeling. *Water*, **14**(22), 3714. https://doi.org/10.3390/w14223714

Yadav, A., Noori, M. T., Biswas, A., & Min, B. (2022b). A concise review on the recent developments in the internet of things (IoT)-based smart aquaculture practices. *Reviews in Fisheries Science & Aquaculture*, **31**(1), 103-118. https://doi.org/10.1080/23308249.2022.2090228

Yakkala, V. S., Nusimala, K. V., Gayathri, B., Kanamarlapudi, S., Aravinth, S. S., Salau, A. O., & Srithar, S. (2024). Deep learning-based crop health enhancement through early disease prediction. *Cogent Food & Agriculture*, **11**(1), 2423244. https://doi.org/10.1080/23311932.2024.2423244