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Data-Driven Solutions for Scrap Seedling Reduction:
Improving Efficiency in a Vegetable Nursery
Final report submission
Submitted by
Omkar D Jadhav
Roll number: 22f2001265
IITM Online BS Degree Program,
Indian Institute of Technology, Madras, Chennai
Tamil Nadu, India, 600036
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EXECUTIVE SUMMARY..............................................................................................................3
ANALYSIS PROCESS/METHOD.................................................................................................. 4
1. Data Collection.................................................................................................................... 4
2. Data Cleaning and Data Analysis........................................................................................ 7
RESULTS AND FINDINGS............................................................................................................9
1. Month VS Seedling Count................................................................................................... 9
2. Seedlings over months...................................................................................................... 10
3. Comparison between seedlings......................................................................................... 11
4. Seedling VS Scrap seedling count.................................................................................... 12
5. Scrap Seedlings in Month..................................................................................................13
6. Reasons.............................................................................................................................14
7. Reasons VS Seedlings...................................................................................................... 15
8. Reference VS Actual Number of Scrap Seedlings............................................................ 16
INTERPRETATION OF RESULTS AND RECOMMENDATION.................................................. 17
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EXECUTIVE SUMMARY
The "Data-Driven Solutions for Scrap Seedling Reduction" project delves into the
challenges faced by "Praviram Ropvaitka," a leading vegetable nursery in Islampur, Maharashtra,
India. Established in 2004 by V. Kadam, the nursery has a reputation for high-quality seedlings
but grapples with a significant issue of high scrap seedling rates, impacting its efficiency and
productivity.
The primary aim of this report is to provide a comprehensive analysis of the scrap
seedling challenge and propose effective data-driven solutions. The report follows a structured
problem-solving approach, involving data collection, analysis, and strategic recommendations.
The data collection process encompassed gathering production data and conducting
surveys among the nursery's experienced staff. Analysis of this data revealed key insights into
the factors contributing to scrap seedling generation. These insights have been grouped into
categories including disease, pests, and handling errors, shedding light on the root causes of the
problem.
Findings from the analysis indicate that specific plant species are more susceptible to
scrap issues due to variations in environmental conditions and handling practices. In response to
these findings, the report presents a set of targeted recommendations to address the scrap
seedling challenge. These recommendations include focusing on specific plant breeds,
discontinuing certain varieties, utilizing scrap seedlings for fertilizer, optimizing pesticide use,
and implementing varied fertilization techniques for improved germination.
The successful implementation of these recommendations holds the potential to
significantly reduce scrap seedling rates, optimize resource utilization, and elevate the overall
quality of seedlings. Such improvements are poised to enhance the nursery's operational
efficiency, boost productivity, and ultimately contribute to its continued success in delivering
top-tier seedlings to farmers.
In conclusion, this report's data-driven approach and well-defined recommendations offer
a strategic roadmap for "Praviram Ropvaitka" to overcome the challenge of scrap seedlings,
fostering enhanced efficiency, productivity, and long-term viability in the competitive
agricultural market.
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ANALYSIS PROCESS/METHOD
This project carried in following stages
1. Data Collection
2. Data Cleaning and Data Analysis
1. Data Collection
The process of collecting data for this project involved a meticulous approach to
ensure accuracy and reliability. Data were gathered through the following methods:
A. From owner itself
Direct engagement with the owner provided crucial insights into the
nursery's operations. Conversations and consultations were held to extract
information on various aspects, including the diverse variety of seedlings
produced, the specific quantity of seedlings planted each month for every breed,
and the count of seedlings that had been deemed unsuitable and subsequently
scrapped in a given month. Initially, this data was recorded manually, using
traditional pen and paper in a register notebook. However, to enhance
accessibility and analytical capabilities, the data were subsequently digitized
using Microsoft Excel. This transformation allowed for a more structured and
organized representation of the collected information, forming the foundation for
subsequent analysis.
excel_sheet
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B. From survey
Recognizing the importance of understanding the underlying causes of
seedling wastage, a survey approach was adopted. Since the existing data did not
provide insights into the reasons behind seedlings being scrapped, the team
engaged in discussions and conducted surveys with both the owner and the
experienced nursery staff. These conversations unearthed valuable hypothetical
data concerning potential causes for seedlings to become scrap. By delving into
the collective expertise of the nursery staff, this survey-based approach aimed to
bridge the information gap and shed light on the intricate factors contributing to
the scrap seedling challenge.
By combining information sourced directly from the owner and
hypothetical insights derived from surveys, a comprehensive dataset was created.
This dataset formed the cornerstone of subsequent analyses and laid the
groundwork for developing effective solutions to address the scrap seedling issue.
Excel_sheet
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2. Data Cleaning and Data Analysis
The process of data analysis began with the collection of raw data directly from
the nursery's owner. However, the raw data presented certain challenges, including
missing values and entries that were deemed too small to contribute significantly to the
problem statement. Through discussions with the owner, a process of Extract, Load,
Transform (ELT) was applied, focusing on relevant and significant data attributes that
could effectively address the problem.
Microsoft Excel emerged as the primary tool for both data cleaning and
subsequent analysis. The initial step involved inputting the raw data into Excel, which
served as a platform for data transformation and organization. Various techniques were
employed during the data pre-processing phase, such as imputing missing values,
correcting typographical errors, and arranging data in a structured manner. This
preparation laid the foundation for meaningful analysis.
Upon completion of data pre-processing, the actual analysis was carried out
within the Excel environment. Certain data columns were derived or eliminated to
enhance the efficiency of the analysis process. Excel's array of features facilitated the
identification of patterns, creation of pivot tables, and extraction of insightful trends from
the data.
The collected raw data was organized into an Excel sheet, with a deliberate focus
on the data related to the last four months. Collaborative discussions with both the owner
and nursery staff members played a pivotal role in narrowing down the primary reasons
for seedlings being scrapped. By conducting a survey, valuable insights were gathered,
leading to the distribution of percentages representing the various reasons for scrap
seedlings.
Two distinct datasets were utilized for analysis purposes:
Dataset 1: Calculated the percentage of scrap seedlings based on the data.
Dataset 2: Provided an overview of each seedling breed and the corresponding reasons
for being scrapped. This dataset was compared against Dataset 1 to draw meaningful
conclusions.
Key steps in the analysis process included:
● Calculating the percentage of scrap seedlings for each month.
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● Generating new reference data through survey information.
● Generating stacked column charts, bar charts and pie charts as per requirement to
gain insights into the causes of scrap seedlings.
Throughout this process, Excel's capabilities enabled efficient manipulation and
visualization of the data, leading to the extraction of valuable insights. The synergy of
data cleaning and analysis empowered the identification of trends, correlations, and
causal factors behind the scrap seedling challenge, ultimately contributing to informed
decision-making and targeted recommendations for improvement.
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RESULTS AND FINDINGS
1. Month VS Seedling Count
From this, we can say that the month of March had the largest number of new
seedlings planted. Followed by April. January and February have more or less the same
number of new seedlings planted.
The data suggests that there are specific planting trends based on the months and
the suitability of weather conditions for each species. It's essential to consider factors
such as temperature, humidity, and growth requirements when interpreting the variations
in seedling counts.
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2. Seedlings over months
Each species of seedling has a varying pattern of planting over the months,
indicating the preference for specific planting periods based on environmental factors
and market demands.
Some species, like B1 Capsicum, B2 Capsicum, and Tomato, show consistent
planting across multiple months, suggesting their continuous demand and adaptability to
various conditions.
Other species, such as Marigold and Cauliflower, have sporadic planting months,
potentially due to specific growing requirements or market trends.
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3. Comparison between seedlings
B1 Capsicum and B2 Capsicum are consistently planted across all months,
indicating a stable demand for these seedling types.
Watermelon has a concentrated planting in January and lower or no planting in
subsequent months, suggesting a specific growing season preference.
Tomato shows a steady planting pattern across the months, with slightly lower
planting in February.
Marigold has planting in January and March, but not in February or April.
Chili has a similar planting pattern to Marigold, with no planting in February or
April.
Eggplant has planting in January and February, followed by a significant increase
in March and April.
Cabbage is consistently planted throughout the months, indicating its adaptability
to different conditions.
Cauliflower, on the other hand, is only planted in February, suggesting a
specialized growing season for this species.
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4. Seedling VS Scrap seedling count
The above graph indicates that there is no direct linear relationship between the
number of seedlings planted and the number of seedlings scrapped.
Different species exhibit varying patterns of scrap seedling count despite similar
levels of planting. This suggests that the reasons for seedling wastage are species-specific
and not solely determined by the quantity planted.
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5. Scrap Seedlings in Month
The graph visualizes the monthly trend of scrap seedlings, revealing fluctuations
in the count of scrapped seedlings across different months.
Notably, April emerges as the month with the highest number of scrap seedlings,
closely followed by March. In contrast, January and February show comparatively lower
instances of scrapped seedlings.
While a preliminary assumption might attribute the higher scrap seedling count in
April to hot summer conditions, further investigation reveals additional contributing
factors.
(PS : On discussing with the owner, we found that in the month of April, due to lack of
adequate rainfall, many farmers canceled the seedling order)
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6. Reasons
The above chart is made by data collected via survey. Using this data as reference
data.
The chart provides a visual representation of the hierarchy of reasons for seedling
wastage within the nursery. Disease and Pests stand out as the most prominent factor
leading to seedling scrapping, underscoring the significant impact of these biological
challenges. Handling Errors also play a substantial role in seedling wastage. While the
"Other Causes" category is present, the absence of validated data limits any conclusive
insights into its contribution.
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7. Reasons VS Seedlings
From the above graph, By examining the distribution of reasons for seedling wastage
across different plant species, a pattern emerges. Cabbage and cauliflower exhibit a low
proportion of reasons, particularly those related to handling errors. This suggests that these plants
are relatively easier to manage and handle during cultivation.
In contrast, watermelons show a higher likelihood of being scrapped, implying inherent
challenges in cultivating this breed. Tomatoes, while prone to pest and disease attacks, have a
lower chance of being scrapped due to handling errors.
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8. Reference VS Actual Number of Scrap Seedlings
From the above graph, we can say that there are gaps between what we
actually observed and what we thought. This graph shows that there are more
reasons behind scrap seedling. On talking with the owner and staff, we intended
to find an anomaly or major cause for a particular month.
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INTERPRETATION OF RESULTS AND
RECOMMENDATION
From all finding, we can surely say that, farming is way harder than it seems. It depends
on numerous constraints. We can not take each and every constraint under consideration. On
discussing with the owner and staff, we came to know that in the Month of January due to
unexpected rain is a major cause for scrap seedlings.
From all discussion and data analysis, the following recommendations should be
followed
1. Focus more on Cabbage and its various breeds.
Leveraging the insights derived from the analysis (result 2, 3, and 7), it becomes
evident that prioritizing cabbage cultivation is a strategic move. Cabbage seedlings are
consistently planted every month, indicating a steady demand in the market. Moreover,
the data showcases that cabbage seedlings have a lower likelihood of becoming scrap
compared to other breeds. By emphasizing cabbage production, the nursery can tap into a
reliable revenue stream while simultaneously reducing the risk of scrap seedlings. This
reallocation of focus allows the nursery to save on production costs, which can then be
channeled into the cultivation of capsicums – a core product with high market demand.
2. Avoid Eggplant
The analysis (result 3 and 7) underscores that eggplant seedlings face a
combination of low planting numbers and a higher probability of becoming scrap. In light
of this, a prudent decision would be to discontinue eggplant production. By reallocating
the resources and efforts currently invested in eggplants, the nursery can divert them
towards other, more viable seedlings. This not only minimizes the risk of producing scrap
seedlings but also optimizes the utilization of valuable resources, leading to improved
overall efficiency and reduced waste.
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3. Reuse the scrap seedlings (Fertilizer/Vermicomposting)
Rather than considering scrapped seedlings as mere waste, this recommendation
advocates for their transformation into valuable resources. Repurposing scrapped
seedlings as organic fertilizers or vermicompost serves a dual purpose. Firstly, it aids in
increasing the production of all seedlings by enhancing soil health. Secondly, it addresses
the issue of scrap seedlings during germination, thereby decreasing waste. This approach
aligns with sustainable practices and provides the nursery with an additional avenue for
generating revenue by selling surplus organic fertilizers.
4. Enhance Pesticide Utilization
The prominence of pests and diseases as a cause of scrap seedlings (result 6)
underscores the need for proactive pest management. Ignoring these issues can lead to
contagion among plants and subsequent escalation in scrap seedlings. Therefore,
intensifying pesticide utilization is critical. By implementing a well-structured and
consistent pest control strategy, the nursery can effectively safeguard its seedlings from
pest-related damage. This initiative not only reduces the risk of scrap seedlings but also
contributes to maintaining overall plant health and quality..
5. Diversify Fertilizer Use for Improved Germination and Disease Prevention
The significance of various fertilizers in maintaining soil quality and promoting
healthy plant growth cannot be underestimated. Adopting a tailored approach to fertilizer
application, considering the specific needs of each plant and soil type, can substantially
enhance germination rates and mitigate disease risks. This strategy ensures that the
seedlings start their growth journey on a strong foundation, reducing the likelihood of
them becoming scrap due to poor germination or disease susceptibility.
6. Enhance Record-Keeping
Implementing meticulous record-keeping practices is a strategic step to ensure
that the nursery's operations are well-documented and transparent. By maintaining
detailed records of planting, treatments, and outcomes, the nursery gains access to a
treasure trove of historical data. This data can reveal patterns, trends, and correlations that
aid in making informed decisions. Analyzing past performance, successful strategies, and
areas that require improvement becomes feasible with this data-driven approach.
Ultimately, enhanced record-keeping contributes to optimizing processes, maximizing
efficiencies, and refining decision-making processes.
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By implementing these recommendations collectively, "Praviram Ropvaitka" can
effectively address the scrap seedling challenge. Each recommendation tackles a specific aspect
of the issue, from cultivation focus to resource optimization and sustainable practices. The
holistic approach outlined in these recommendations aims to transform challenges into
opportunities, thereby elevating the nursery's operational efficiency, productivity, and long-term
viability in the competitive agricultural market.