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Agriculture and Food Sciences Research 
Vol. 12, No. 2, 84-92, 2025 

ISSN(E) 2411-6653/ ISSN(P) 2518-0193 
DOI: 10.20448/aesr.v12i2.6960 

© 2025 by the authors; licensee Asian Online Journal Publishing Group 

 
 

 
 
 
Comprehensive analytical report on crop performance using ratio-based metrics 

 
Mahalakshmi Kovvuri1 
Bharat Khushalani2 

 
 

( Corresponding Author)  
1,2Shri Vishnu Engineering College for Women, Bhimavaram, India. 
1Email: bharat@svecw.edu.in  
2Email: 23B01A4266@svecw.edu.in 

 
Abstract 

This comprehensive study aims to provide a detailed comparative analysis of crop performance by 
investigating the behavior of crops classified as exhibiting either high or low kilograms per 
hectare (KGH) productivity through the lens of three fundamental ratio-based indicators: 
production-to-area, yield-to-area, and yield-to-production. These ratios are critical metrics that 
help quantify land-use efficiency, productivity levels, and output stability, thereby offering a 
multi-dimensional perspective on agricultural performance. Such ratios indicate how crops behave 
under varying agronomic and environmental contexts. This study delves into crop performance 
by distinguishing them into two primary categories based on variance characteristics. High KGH 
crops are identified as those more susceptible to significant fluctuations due to factors such as 
climatic variability, market shifts, and biological vulnerabilities. Conversely, low KGH crops 
display relatively stable and predictable patterns of output, making them more reliable under 
standard farming conditions. The research utilizes both line and bar charts to effectively visualize 
the inter-crop differences and temporal trends in these ratios, highlighting patterns of consistency 
and volatility that characterize various crops. The findings aim to support enhanced data-driven 
decision-making in crop planning, agricultural land management, and policy formulation, 
particularly in regions facing significant variability in agricultural outputs due to climatic, 
environmental, or socio-economic factors. By carefully analyzing these ratios, the study not only 
sheds light on crops with superior resilience and efficiency but also identifies those prone to 
instability, offering valuable insights for stakeholders in the agricultural sector. 

 
Keywords: Agricultural productivity, bar chart analysis, crop performance, crop planning and policy, crop yield stability. 

 
Citation | Kovvuri, M., & Khushalani, B. (2025). Comprehensive 
analytical report on crop performance using ratio-based 
metrics. Agriculture and Food Sciences Research, 12(2), 84–92. 
10.20448/aesr.v12i2.6960 
History:  
Received: 19 June 2025 
Revised: 21 July 2025 
Accepted: 25 July 2025 
Published: 4 August 2025 
Licensed: This work is licensed under a Creative Commons 

Attribution 4.0 License  
Publisher:  Asian Online Journal Publishing Group 

Funding: This study received no specific financial support. 
Institutional Review Board Statement: Not applicable. 
Transparency: The authors confirm that the manuscript is an honest, 
accurate, and transparent account of the study; that no vital features of the 
study have been omitted; and that any discrepancies from the study as planned 
have been explained. This study followed all ethical practices during writing. 
Competing Interests: The authors declare that they have no competing 
interests. 
Authors’ Contributions: Both authors contributed equally to the conception 
and design of the study. Both authors have read and agreed to the published 
version of the manuscript. 

 

Contents 
1. Introduction ...................................................................................................................................................................................... 85 
2. Methodology ..................................................................................................................................................................................... 85 
3. Ratio Metrics and Analysis ............................................................................................................................................................ 86 
4. Key Findings ..................................................................................................................................................................................... 91 
5. Policy Implications and Recommendations................................................................................................................................ 91 
6. Conclusion ......................................................................................................................................................................................... 92 
References .............................................................................................................................................................................................. 92 
 

 

 

 

 

 

https://www.doi.org/10.20448/aesr.v12i2.6960
mailto:bharat@svecw.edu.in
mailto:23B01A4266@svecw.edu.in
https://creativecommons.org/licenses/by/4.0/
https://creativecommons.org/licenses/by/4.0/


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Contribution of this paper to the literature 
By analyzing the defined ratios, our study not only sheds light on crops with superior resilience 
and efficiency but also identifies those prone to instability, offering valuable insights for 
stakeholders in the agricultural sector. The terminology itself is new and not available in 
previous studies. 

 
1. Introduction 

Agricultural productivity remains a cornerstone of food security, economic resilience, and rural development. 
Conducting a quantitative evaluation through ratio-based metrics provides a nuanced perspective on how crops 
behave under varying agronomic and environmental contexts. This study delves into crop performance by 
distinguishing them into two primary categories based on variance characteristics. High KGH crops are identified 
as those more susceptible to significant fluctuations due to factors such as climatic variability, market shifts, and 
biological vulnerabilities. On the other hand, Low KGH crops display relatively stable and predictable patterns of 
output, making them more reliable under standard farming conditions. This classification supports targeted 
agricultural decision-making, particularly when assessing risk, input optimization, or crop selection for specific 
agro-ecological zones. 

The dataset employed in this study originates from India’s agricultural statistics and encompasses state-wise 
data for a diverse range of principal crops. The temporal coverage spans financial years 2001–02 through 2015–16, 
allowing a longitudinal analysis of trends and variability in crop performance. This dataset includes key variables 
such as the area under cultivation (hectares), total production (metric tonnes), and yield per hectare for each crop 
across multiple states. The data was compiled from government records and agricultural statistical publications 
(source to be specified), providing a comprehensive basis for calculating ratio-based metrics that reflect 
productivity and efficiency. This rich dataset enables an in-depth assessment of how crop outputs evolve over time 
across different agro-climatic zones and socio-economic conditions, making it instrumental for data-driven 
agricultural policy and planning. 

A global dataset of crops is considered in Iizumi and Sakai [1] and the authors perform an analysis over a time 
period of around 35 years. They take wheat, rice, soybean, and maize and obtain the yield time series of these crops. 
Senapati and Goyari [2] have divided their study period into three distinct phases: pre-green, early phase, and 
post-green revolution period, and compared the growth rates during these periods. The development and 
regulatory challenges of genetically modified crops in India are discussed in Shukla et al. [3]. Their work 
highlights both scientific progress and policy barriers. This study extends the discussion by visually analyzing crop 
performance across Indian states. Reddy et al. [4] improved crop simulation in CLM5 using long-term site-level 
data for Indian crops. Their work enhanced model accuracy for wheat and rice growth patterns. Building on this, 
our study visually analyzes crop performance across Indian states for better planning. Long-term trends in millet 
cultivation across India are examined in Yamuna et al. [5]. They reported declining area and production but stable 
or improving yields. This study builds on their insights through visual analysis of crop performance across states. 
The extremes of crop yields in India using extreme value theory are analyzed in Lakshmi Kumar et al. [6]. Their 
study highlights regional differences in yield variability and stresses the need to consider extreme events in 
agricultural planning to improve food security. State-wise yields of major food crops in India for 2014–15 using 
statistical methods are analyzed in Talukdar et al. [7]. The study revealed significant regional differences in 
productivity, emphasizing the need for targeted strategies to improve crop yields. The growth and sustainability of 
major crops in Haryana are studied in Bagaria and Jatav [8], highlighting positive trends due to water-efficient 
practices. Their findings stress the need for sustainable agriculture to ensure long-term food security. Changing 
crop patterns and diversification in Koch Bihar, West Bengal, focusing on six key crops, are considered in Islam 
and Das [9]. Their analysis revealed significant shifts influenced by environmental and socio-economic factors. An 
analysis of four decades of data to assess trends in the area, production, and yield of key crops in Bangladesh is done 
in Akhter et al. [10]. Their findings reveal significant growth in rice and wheat yields, while jute cultivation 
experienced a decline, highlighting the need for adaptive agricultural strategies. A survey of agricultural datasets 
to enhance crop yield prediction using machine learning algorithms is conducted in Tripathi et al. [11]. Their 
study emphasizes the importance of accurate yield forecasting for precision agriculture and food security. The 
growth patterns of oilseed crops in India are considered in Reddy and Immanuelraj [12], revealing significant 
spatial and temporal disparities. Their study highlights the need for targeted strategies to enhance oilseed 
productivity and reduce reliance on imports. A comprehensive global analysis of crop yield trends and variability 
using 8,088 country-crop yield series from FAO data is conducted in Arata et al. [13]. Employing robust statistical 
methods, they identified a slowdown in yield growth and increased variability in certain regions, highlighting 
implications for global food security and the need for adaptive agricultural policies. A systematic review on the 
application of data analytics in crop management is conducted in Chergui and Kechadi [14]. Their study 
emphasizes how digital agriculture leverages big data and advanced technologies to enhance productivity, optimize 
resource use, and promote sustainable farming practices. Global crop yield variability from 1981 to 2010 is 
analyzed in Iizumi and Ramankutty [15], attributing significant changes to climate change. Their study highlights 
the increasing impact of climate factors on yield stability, emphasizing the need for adaptive agricultural strategies. 
Long-term trends in foodgrain yield, area, and production in India are examined in Shaharshad et al. [16]. Their 
analysis highlights how both short-term and long-term factors influence agricultural output dynamics. Growth 
trends and structural changes in India’s commercial crop production from 1980 to 2020 are studied in Kutty [17], 
highlighting the impact of policy reforms on agricultural productivity. A dynamic-statistical biomass model to 
predict crop yields in Kazakhstan, utilizing 21 years of regional data (2000–2021), is developed in Sadenovaa et al. 
[18]. The model demonstrated strong correlations with official statistics, indicating its robustness against 
meteorological variability. 
 

2. Methodology 
The analytical framework employed in this study is centered on a comprehensive evaluation of three critical 

productivity ratios Production-to-Area, Yield-to-Area, and Yield-to-Production across the two major crop 
categories distinguished by their KGH behavior. Each ratio serves a unique purpose in unraveling different aspects 



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of crop performance and land-use efficiency. The methodology involves systematically processing the dataset to 
compute these ratios for every crop and every state-year combination within the study period. These calculations 
form the backbone of subsequent visual analyses. 

Both line and bar plots are employed as key visualization tools to analyze the temporal and cross-sectional 
trends of these ratios. Bar plots provide an intuitive understanding of inter-crop performance by clearly displaying 
the magnitude of ratios in selected years, thereby facilitating easy comparison of productivity and efficiency among 
crops. On the other hand, line plots reveal the temporal evolution of these ratios, capturing fluctuations, peaks, and 
troughs that characterize the high- and low-performing crops over time. This dual approach helps to uncover both 
steady trends and volatility, which are essential for understanding the underlying factors driving crop performance. 

Special attention is given to evaluating performance thresholds, such as the 5 KGH benchmark, which helps 
differentiate between highly productive and moderately performing crops. This threshold also assists in policy 
prioritization by flagging crops that exceed or fall below critical productivity levels. By integrating these ratio 
analyses with visual tools, the study provides policymakers, researchers, and farmers with actionable insights, 
enabling more informed decisions related to crop selection, resource allocation, and resilience building in 
agriculture. 
 

3. Ratio Metrics and Analysis 
3.1. Ratio of Production to Area 

The ratio of production to area serves as a fundamental indicator of output efficiency, measuring how much 
crop yield (typically in tons) is produced per unit of land (e.g., hectare). This ratio is essential in assessing the 
agricultural productivity of land, especially in contexts where cultivable land is scarce or subject to degradation. By 
comparing output relative to the area under cultivation, this metric helps evaluate the intensity and effectiveness of 
land use, offering valuable insights into which crops optimize production under prevailing environmental and 
agronomic conditions. 

In the case of high KGH crops, the data visualizations, particularly line plots, reveal substantial instability and 
fluctuation, as shown in Figure 1. The peaks and troughs observed highlight the unpredictable nature of 
production outcomes, which can be attributed to erratic rainfall, pest infestations, soil fertility variations, and 
inconsistent input application. These fluctuations, visualized further in Figure 2, make high KGH crops less 
dependable from both an economic and policy standpoint. 

 

 
Figure 1. Production/Area – High KGH crops. 

 

 
Figure 2. Production/Area – High KGH crops (Bar). 



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In contrast, low KGH crops exhibit a much more predictable trend in their production-to-area ratio. Bar charts, 
such as in Figure 4, demonstrate uniform and steady performance, while line plots in Figure 3 display smooth, 
consistent curves, indicating resilience and adaptability. These characteristics imply that low KGH crops benefit 
from stable environmental conditions, optimized agronomic practices, and improved seed genetics, making them 
ideal for land productivity enhancement.  

 
Figure 3. Production/Area – Low KGH crops. 

 

 
Figure 4. Production/Area – Low KGH crops (Bar). 

 
 

Mathematical Formula: 

 
This ratio measures how many kilograms of crop are produced per hectare, reflecting land productivity. 
Statistical Classification: 
The Production-to-Area ratio is used to classify crops into High KGH and Normal KGH groups based on a 

threshold value of 5 kg/ha. Crops with P/A values exceeding 5 kg/ha are designated as High KGH, indicating 
exceptionally high land productivity but often accompanied by higher variance (greater year-to-year fluctuations). 
Examples include sugarcane, cotton, and banana, which frequently surpass this threshold, with sugarcane reaching 
peaks over 70 kg/ha. 

Crops consistently below the 5 kg/ha threshold fall into the Normal KGH category. These crops, such as rice, 
maize, and wheat, generally show lower but steadier production per hectare. Statistically, high KGH crops exhibit 
higher variance (standard deviation) in P/A values, reflecting sensitivity to environmental factors and management 
practices, while normal KGH crops show tighter clustering around their mean P/A, indicating stability. 

The Production-to-Area ratio plot clearly demonstrates which crops utilize land most efficiently in terms of 
raw output. Crops like Sugarcane and Banana dominate this visualization, with Sugarcane frequently exceeding 70 
KGH and peaking beyond 75 KGH in certain years, showing extremely high land productivity. Cotton follows 
with notable consistency, often surpassing the 5 KGH threshold, classifying it within the high-efficiency crop 
group. This visual behavior confirms Sugarcane’s and Cotton’s suitability for regions prioritizing land 
maximization. However, the statistical distribution of these values reveals significant year-to-year fluctuations, as 
evidenced by the irregular bar heights across the timeline, indicating high variance. Such variability underlines 
environmental dependency and sensitivity to factors like rainfall, fertilizer input, and agronomic management. On 
the other hand, crops like Maize, Wheat, and Paddy remain steadily clustered in the range of 2–4 KGH, with 
minimal deviation across years, reflecting stable and predictable land performance. This contrast, visually and 



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statistically apparent in the plot, serves as a direct basis for classifying crops into high and normal KGH categories 
and is critical when prioritizing land allocation strategies in fluctuating agricultural zones. 

 
3.2. Ratio of Yield to Area 

The ratio of yield to area is another crucial performance metric, reflecting the efficiency of land use in 
producing a normalized crop output. Unlike the production ratio, this metric often accounts for factors such as 
weight, quality, or moisture content. It is particularly useful in modern agriculture, where maximizing both 
quantity and quality from available land is critical. 

For high KGH crops, this ratio shows substantial inconsistency. Line plots, such as in Figure 5, display sharp 
spikes followed by abrupt declines, indicating sensitivity to environmental and market changes. Figure 6 is a bar 
chart representation that highlights this volatility even further. Peaks may reflect favorable weather or robust 
input use, while dips often suggest stressors like droughts, pest attacks, or supply shortages. These erratic trends 
make planning around such crops a risky endeavor. 
 

 
Figure 5. Yield/Area – High KGH crops. 

 

 
Figure 6. Yield/Area – High KGH crops (Bar). 

 
Conversely, low KGH crops display minimal variation in yield-to-area ratios. This is clearly depicted in Figure 

7 and Figure 8, where both line and bar plots reflect a smooth and consistent performance trend. This stability 
arises from controlled agronomic conditions, including dependable irrigation, the use of hybrid seeds, and reliable 
fertilizer access. Such uniformity enables better input planning, more predictable income for farmers, and improved 
policy effectiveness. 



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Figure 7. Yield/Area – Low KGH crops. 

 

 
Figure 8. Yield/Area – Low KGH crops (Bar). 

 
Mathematical Formula: 

 
This ratio accounts for yield adjusted by quality factors, normalized per hectare. 
Statistical Classification: 
In the statistical context, the Yield-to-Area ratio is analyzed to distinguish crop groups based on their average 

ratio and variance. Crops with Y/A values notably above 0.1 (or similarly high thresholds relevant to the data 
range) and high inter-annual variability are classified as high KGH crops. For instance, ginger shows Y/A spikes 
around 0.42 with sharp drops, indicating high variance and unstable yield performance. 

Normal KGH crops tend to have Y/A ratios clustered within a narrower band (e.g., 0.02 to 0.035) and exhibit 
low variance, signifying consistent yield quality per hectare. Statistically, the difference between high and normal 
KGH is evaluated using measures like standard deviation and coefficient of variation, identifying crops with stable 
versus volatile yield efficiency. 

The Yield-to-Area ratio plot emphasizes quality-adjusted production relative to land use, visually 
distinguishing high-value crops from more stable staples. Crops such as Ginger, Black Pepper, and Turmeric stand 
out with bar heights reaching 0.42 and sometimes even higher, particularly in years like 2003–04 and 2005–06 for 
Ginger. These values are significantly above those of other crops, where the majority such as Rice, Maize, and 
Wheat cluster consistently between 0.02 and 0.035. The wide spread and sharp fluctuations among the high Y/A 
crops suggest strong input responsiveness but also highlight volatility. This variability is evident from the 
irregular and steeply rising or falling bars across years, indicating sensitivity to climatic or economic stressors. In 
contrast, the flat and even distribution of Y/A ratios in staples demonstrates agronomic stability, achieved through 
regulated irrigation, hybrid seed use, and consistent cultivation practices. These statistical trends in the bar plot 
underscore how this ratio can be used not only to assess land performance but also to inform precision agriculture 
decisions, guiding investments into crops that either demonstrate stable returns or require controlled 
environments to mitigate risks. 
 
3.3. Ratio of Yield to Production 

The ratio of yield to production offers insights into how yield performance aligns with total output. While less 
commonly used, it reveals whether gains in yield translate proportionately into production or if inefficiencies such 
as post-harvest losses are present. This metric is especially useful for identifying gaps in crop conversion efficiency. 



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High KGH crops tend to display wide variability in this ratio. Line and bar charts, as presented in Figure 9 and 
Figure 10, show erratic values. These issues could stem from increased area not leading to proportional yield gains 
or losses in the supply chain. The discrepancies suggest that yield improvements in these crops do not always 
result in scaled production, limiting their reliability. 

 

 
Figure 9. Yield/Production– High KGH crops. 

 

 
 

Figure 10. Yield/Production– High KGH crops (Bar). 

 
 

In contrast, low KGH crops typically exhibit stable and higher yield-to-production ratios, as seen in Figure 11 
and Figure 12. This alignment indicates that improvements in yield translate directly into overall production, 
which points to greater operational efficiency. Such crops are more suited for scale-up and offer a better return on 
investment. 

 

 
Figure 11. Yield/Production– Low KGH crops. 

 



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Figure 12. Yield/Production– Low KGH crops (Bar). 

 
Mathematical Formula: 

 
This ratio reflects the consistency or proportionality between yield and production. 
Statistical Classification: 
Statistically, the Yield-to-Production ratio is used to assess operational efficiency beyond raw yield or 

production numbers. Crops with Y/P ratios fluctuating widely ranging, for example, from 0.02 to 0.12 are 
classified as high KGH due to irregular efficiency patterns. Black pepper and tobacco demonstrate such variable 
Y/P ratios, indicating inconsistencies such as post-harvest losses or processing inefficiencies. 

In contrast, crops exhibiting a near-linear relationship in Y/P, with ratios consistently clustered around lower 
stable values (e.g., 0.015 to 0.035), fall under Normal KGH. This stability suggests effective conversion of yield 
gains into production increments, marking operational efficiency. 

Variance metrics, trend analysis, and standard deviation are used statistically to differentiate these categories, 
highlighting crops with consistent versus inconsistent yield-to-production relationships. 

The Yield-to-Production ratio plot provides a clear visualization of the internal relationship between yield and 
total output. High values in this plot such as those for Black Pepper and Mesta, often reaching 0.1 to 0.12 indicate 
that the yield (possibly quality-adjusted) constitutes a significant portion of total production, highlighting potential 
inefficiencies in scaling up or post-harvest processing. These elevated bars contrast with the more consistent lower 
ratios for crops like Maize, Wheat, and Rice, where values range between 0.015 and 0.035. This suggests that in 
these staple crops, increases in yield proportionally translate into overall production, reflecting operational 
efficiency. From a statistical perspective, the high variability observed in the bar heights for crops like Turmeric 
and Ginger points to inconsistency in yield-to-output translation, which may result from unpredictable losses or 
input/output mismatches. Visually, the irregular bar heights for such crops differ markedly from the even, linear 
patterns seen in staples, reinforcing their classification as high-variance crops and highlighting their suitability for 
more controlled, investment-intensive cultivation systems. This ratio, as depicted in the plot, is essential for 
diagnosing systemic inefficiencies and guiding research or infrastructure investments. 
 

4. Key Findings 
The comparative analysis of high KGH and low KGH crops through the lens of the three-ratio metrics reveals 

clear patterns and distinctions. High KGH crops exhibit significant volatility across all ratios, indicating 
inconsistent performance. While they may demonstrate high yields or production levels in certain years, these 
instances are not reliable and often arise under specific, favorable conditions. This unpredictability limits their 
potential for stable food supply planning, commercial investment, and policy support. The inherent risk associated 
with high KGH crops calls for careful consideration before large-scale promotion. 

In contrast, low KGH crops consistently show a strong alignment between area, yield, and production. This 
makes them more dependable for agricultural stakeholders, from farmers and supply chain actors to policymakers. 
Their performance across all three ratios particularly in yield-to-area and yield-to-production suggests high 
efficiency, resilience, and scalability. These attributes make them excellent candidates for subsidy allocation, 
research funding, and national food security strategies. 

Among the three metrics, the Yield-to-Area ratio emerged as the most consistent and reliable across both crop 
categories, offering a clear indicator of land-use efficiency. Meanwhile, the Yield-to-Production ratio proved 
particularly useful in identifying internal inconsistencies and assessing readiness for large-scale farming operations. 
Combined, these ratios create a comprehensive framework for evaluating crop sustainability and productivity. 
 

5. Policy Implications and Recommendations 
Based on the analysis of ratio-based crop performance metrics, several policy and strategic recommendations 

emerge. First, government subsidies and agricultural support programs should prioritize crops that demonstrate 
consistent and high performance across both yield and production dimensions. These crops not only ensure stable 
returns for farmers but also contribute to food security and economic resilience at the national level. 

High KGH crops, despite their risks, should not be dismissed entirely. Instead, they should be the focus of 
targeted innovation efforts, including the development of pest-resistant or drought-tolerant varieties, the 



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promotion of adaptive farming techniques, and the establishment of insurance schemes that cushion farmers against 
environmental shocks. Research funding and public-private partnerships can play a key role in making these crops 
more reliable performers. 

The three ratios analyzed in this report, Production-to-Area, Yield-to-Area, and Yield-to-Production, should 
be integrated into broader agricultural management systems. For example, they can serve as the foundation for 
crop insurance models, where premiums are based on volatility, or in credit scoring systems that assess the risk of 
lending to specific agribusinesses based on crop consistency. Additionally, coupling these metrics with climate, soil, 
and satellite datasets will enable the development of advanced predictive models, supporting precision agriculture 
and smarter resource allocation. 
 

6. Conclusion 
This multi-ratio analytical approach offers a robust and comprehensive decision-support framework for 

evaluating crop performance under varying agronomic and environmental conditions. By systematically examining 
the interrelationships among production, yield, and area through three distinct ratios production-to-area, yield-to-
area, and yield-to-production this methodology provides granular insights into the efficiency, consistency, and 
potential scalability of agricultural outputs. 

Through this lens, the contrast between high KGH and low KGH crops becomes particularly evident. While 
high KGH crops may demonstrate occasional surges in productivity, their unpredictable nature limits their 
reliability for long-term planning and investment. In contrast, low KGH crops consistently deliver stable 
performance across all key metrics, making them more suitable for scaling, strategic support, and policy 
prioritization. 

The integration of visual tools such as bar and line plots not only enhances interpretability but also enables 
temporal and cross-sectional analysis. This visual representation aids researchers, agronomists, and policymakers 
in identifying both risk-prone crops that require targeted interventions and resilient varieties that merit broader 
promotion. When combined with complementary datasets such as climate patterns, soil conditions, and 
technological inputs, this framework can be further strengthened to guide evidence-based agricultural planning. 

Ultimately, this approach promotes a more sustainable, scalable, and stable agricultural ecosystem. It equips 
stakeholders with the clarity needed to make informed decisions, allocate resources efficiently, and drive innovation 
in crop management and development. 
 

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