Some Essentials of Good Agricultural Scientific Research: A Guide for Aspiring Researchers

Agricultural science is the study of the biological, physical, and social aspects of agriculture and how they affect the production, processing, and distribution of food and other agricultural products. Agricultural science is a multidisciplinary field that draws from various disciplines such as biology, chemistry, economics, ecology, engineering, and sociology. Agricultural science aims to improve the quality, quantity, and sustainability of food and other agricultural products, as well as to address the challenges and opportunities faced by farmers, consumers, and policymakers.

Agricultural scientific research is the systematic investigation of agricultural phenomena, problems, and opportunities, using scientific methods and tools. Agricultural scientific research can be applied or basic, depending on the purpose and scope of the study. Applied research is focused on solving specific problems or improving specific practices in agriculture, while basic research is aimed at advancing the fundamental knowledge and understanding of agricultural phenomena. Agricultural scientific research can also be classified into different types, such as experimental, observational, descriptive, analytical, or evaluative, depending on the research design and methods used.

Agricultural scientific research is essential for the development and innovation of agriculture and its related sectors. Agricultural scientific research can contribute to the following outcomes:

• Enhancing the productivity, profitability, and competitiveness of agriculture and its related sectors
• Improving the quality, safety, and diversity of food and other agricultural products
• Reducing the environmental impacts and resource use of agriculture and its related sectors
• Increasing the resilience and adaptation of agriculture and its related sectors to climate change and other shocks and stresses
• Supporting the food security, nutrition, health, and well-being of the population
• Informing the policy and decision-making of the stakeholders involved in agriculture and its related sectors

However, agricultural scientific research is also a complex and challenging endeavor that requires careful planning, execution, and communication. Agricultural scientific research involves various steps, such as identifying the research problem, reviewing the literature, formulating the research objectives and questions, designing the research methodology, collecting and analyzing the data, interpreting and discussing the results, drawing the conclusions and implications, and reporting and disseminating the findings. Agricultural scientific research also requires various skills, such as critical thinking, creativity, problem-solving, communication, collaboration, and ethical awareness.

Therefore, aspiring agricultural researchers need to be aware of some essentials of good agricultural scientific research that can help them conduct and communicate their research effectively and efficiently. This document provides an overview of some of these essentials, such as the following:

• The characteristics of good agricultural scientific research
• The sources and methods of literature review
• The components and types of research objectives and questions
• The elements and types of research design and methodology
• The principles and techniques of data collection and analysis
• The criteria and strategies of data interpretation and discussion
• The guidelines and formats of research reporting and dissemination

The Characteristics of Good Agricultural Scientific Research

Good agricultural scientific research is research that meets the standards and expectations of the scientific community and the stakeholders involved in agriculture and its related sectors. Good agricultural scientific research should have the following characteristics:

• Relevance: The research should address a significant and timely problem or opportunity in agriculture and its related sectors, and provide useful and applicable information and recommendations for the intended audience.
• Originality: The research should contribute new and novel knowledge and understanding of the research topic, and avoid duplication and plagiarism of existing research.
• Validity: The research should use appropriate and rigorous methods and tools to collect and analyze the data, and ensure that the results are accurate, reliable, and consistent.
• Reliability: The research should be conducted and reported in a transparent and honest manner, and provide sufficient and clear information and evidence to support the claims and conclusions.
• Replicability: The research should be conducted and reported in a way that allows other researchers to replicate and verify the results, and to build on the research in the future.
• Generalizability: The research should provide a clear and logical explanation of how the results can be applied or extended to other contexts, situations, or populations, and acknowledge the limitations and assumptions of the research.
• Significance: The research should demonstrate the importance and implications of the results for the advancement of the scientific knowledge and the improvement of the agricultural practices and policies.
• Ethicality: The research should comply with the ethical principles and guidelines of the scientific community and the stakeholders involved in agriculture and its related sectors, and respect the rights and interests of the participants, collaborators, and beneficiaries of the research.

The Sources and Methods of Literature Review

Literature review is the process of searching, selecting, evaluating, and synthesizing the existing literature related to the research topic. Literature review is an essential step in agricultural scientific research, as it can help the researcher to:

• Identify the research problem and justify its significance and relevance
• Establish the theoretical and conceptual framework of the research
• Review the current state of knowledge and understanding of the research topic
• Identify the gaps and limitations in the existing literature
• Formulate the research objectives and questions
• Design the research methodology and select the appropriate methods and tools
• Compare and contrast the results with the existing literature
• Discuss the implications and contributions of the research

To conduct a literature review, the researcher needs to use various sources and methods to find and access the relevant and reliable literature. Some of the sources and methods of literature review are:

• Primary sources: These are the original and first-hand sources of information, such as journal articles, books, book chapters, conference papers, theses, dissertations, reports, patents, and datasets. Primary sources provide the most direct and detailed information about the research topic, and are usually peer-reviewed and published by reputable publishers or organizations.
• Secondary sources: These are the sources that summarize, analyze, or interpret the primary sources, such as review articles, meta-analyses, systematic reviews, literature reviews, textbooks, encyclopedias, handbooks, and guides. Secondary sources provide an overview and synthesis of the existing literature, and can help the researcher to identify the key concepts, theories, methods, and findings of the research topic.
• Tertiary sources: These are the sources that provide general and introductory information about the research topic, such as dictionaries, glossaries, indexes, bibliographies, directories, and websites. Tertiary sources can help the researcher to define the terms, concepts, and scope of the research topic, and to locate the primary and secondary sources.
• Search engines: These are the online tools that allow the researcher to search and retrieve the literature from various databases and repositories, such as Google Scholar, Scopus, Web of Science, PubMed, Agricola, and CAB Abstracts. Search engines can help the researcher to find the most relevant and recent literature, and to use various filters and features to refine and organize the search results.
• Search strategies: These are the techniques that the researcher can use to optimize the search and retrieval of the literature, such as keywords, Boolean operators, truncation, wildcards, phrases, and fields. Search strategies can help the researcher to specify and narrow down the search criteria, and to avoid irrelevant and redundant literature.
• Citation management tools: These are the software applications that help the researcher to store, organize, and manage the literature, such as Zotero, Mendeley, EndNote, and RefWorks. Citation management tools can help the researcher to create and update the personal library of the literature, and to generate and format the citations and references according to the preferred style.

The Components and Types of Research Objectives and Questions

Research objectives and questions are the statements that specify and guide the purpose and scope of the research. Research objectives and questions are derived from the research problem and the literature review, and provide the direction and focus of the research. Research objectives and questions should be clear, concise, specific, measurable, achievable, relevant, and time-bound.

Research objectives and questions can be classified into different types, depending on the level and nature of the research. Some of the types of research objectives and questions are:

• General objective or question: This is the broad and overarching statement that describes the main aim or goal of the research. The general objective or question should answer the question of why the research is conducted and what the research intends to achieve.
• Specific objectives or questions: These are the statements that describe the specific aspects or components of the research. The specific objectives or questions should answer the question of what the research will do and how the research will do it.
• Hypotheses: These are the statements that express the expected or predicted relationship between the variables or phenomena of interest. Hypotheses are usually formulated for quantitative or experimental research, and should be testable and falsifiable.
• Research questions: These are the questions that guide the inquiry and exploration of the research topic. Research questions are usually formulated for qualitative or descriptive research, and should be open-ended and answerable.

The Elements and Types of Research Design and Methodology

Research design and methodology are the plans and procedures that describe how the research will be conducted and how the data will be collected and analyzed. Research design and methodology should be aligned with the research objectives and questions, and should ensure the validity, reliability, replicability, and generalizability of the research. Research design and methodology should include the following elements:

• Research approach: This is the overall strategy or framework that guides the research process and the choice of methods and tools. Research approach can be classified into three main types: quantitative, qualitative, and mixed methods. Quantitative research approach is based on the measurement and analysis of numerical data, and uses statistical methods and tools. Qualitative research approach is based on the description and interpretation of textual or visual data, and uses thematic or content analysis methods and tools. Mixed methods research approach is based on the integration and complementarity of quantitative and qualitative data, and uses both statistical and thematic or content analysis methods and tools.
• Research type: This is the specific category or classification of the research, based on the purpose and scope of the study. Research type can be classified into various types, such as exploratory, descriptive, explanatory, evaluative, or action research. Exploratory research type is aimed at discovering and generating new ideas and insights about the research topic. Descriptive research type is aimed at describing and characterizing the research topic. Explanatory research type is aimed at explaining and understanding the causes and effects of the research topic. Evaluative research type is aimed at assessing and judging the value and effectiveness of the research topic. Action research type is aimed at solving and improving a practical problem or situation related to the research topic.
• Research method: This is the specific technique or procedure that is used to collect and analyze the data. Research method can be classified into various methods, such as experimental, observational, survey, interview, focus group, case study, document analysis, or content analysis. Experimental research method is based on the manipulation and control of the independent variable and the measurement of the dependent variable. Observational research method is based on the observation and recording of the behavior or phenomenon of interest. Survey research method is based on the administration of a questionnaire or an instrument to a sample of the population. Interview research method is based on the interaction and conversation between the interviewer and the interviewee. Focus group research method is based on the discussion and interaction among a group of participants. Case study research method is based on the in-depth and holistic investigation of a single or multiple cases. Document analysis research method is based on the examination and evaluation of various types of documents. Content analysis research method is based on the systematic and objective analysis of the content and meaning of the data.
• Research population and sample: This is the group or subset of the group that is the target or the source of the data. Research population is the entire group of interest, while research sample is the selected portion of the population. Research population and sample should be clearly defined and described, and should be representative and adequate for the research objectives and questions.
• Research variables and indicators: These are the concepts or phenomena that are measured or manipulated in the research. Research variables can be classified into different types, such as independent, dependent, intervening, moderating, or control variables. Independent variable is the variable that is manipulated or changed by the researcher. Dependent variable is the variable that is measured or affected by the independent variable. Intervening variable is the variable that mediates or explains the relationship between the independent and dependent variables. Moderating variable is the variable that modifies or influences the relationship between the independent and dependent variables. Control variable is the variable that is held constant or eliminated to reduce the confounding effects on the dependent variable. Research indicators are the specific and observable measures or expressions of the variables.
• Research instruments and tools: These are the devices or applications that are used to collect and analyze the data. Research instruments and tools should be valid, reliable, and suitable for the research objectives and questions, and the research approach and method. Research instruments and tools can be classified into various types, such as questionnaires, scales, tests, checklists, observation guides, interview guides, focus group guides, coding schemes, software, or hardware.
• Research ethics and procedures: These are the principles and guidelines that ensure the ethical conduct and reporting of the research. Research ethics and procedures should comply with the ethical standards and regulations of the scientific community and the stakeholders involved in agriculture and its related sectors, and should protect the rights and interests of the participants, collaborators, and beneficiaries of the research. Research ethics and procedures should include the following aspects: informed consent, confidentiality, anonymity, privacy, data security, data ownership, data sharing, data management, data quality, data analysis, data interpretation, data reporting, data dissemination, and data citation.

The Principles and Techniques of Data Collection and Analysis

Data collection and analysis are the processes of gathering and processing the data to answer the research objectives and questions. Data collection and analysis should be consistent and coherent with the research design and methodology, and should ensure the validity, reliability, replicability, and generalizability of the research. Data collection and analysis should follow the following principles and techniques:

• Data collection principles and techniques: Data collection should be based on the following principles and techniques:
• Sampling: This is the technique of selecting and obtaining the data from the research population or sample. Sampling should be based on the appropriate sampling method, such as probability or non-probability sampling, and the appropriate sampling size, such as power analysis or confidence interval.
• Measurement: This is the technique of assigning values or scores to the data according to the research variables and indicators. Measurement should be based on the appropriate measurement scale, such as nominal, ordinal, interval, or ratio scale, and the appropriate measurement instrument or tool, such as questionnaire, scale, test, checklist, observation guide, interview guide, focus group guide, or coding scheme.
• Administration: This is the technique of implementing and managing the data collection process. Administration should be based on the appropriate data collection mode, such as face-to-face, telephone, mail, online, or mixed mode, and the appropriate data collection procedure, such as pilot testing, pre-testing, training, monitoring, quality control, or feedback.

Data analysis principles and techniques: Data analysis should be based on the following principles and techniques:

• Preparation: This is the technique of organizing and preparing the data for analysis. Preparation should include the following steps: data cleaning, data screening, data coding, data entry, data verification, data transformation, data reduction, and data integration.
• Exploration: This is the technique of examining and describing the data. Exploration should include the following steps: data visualization, data summarization, data distribution, data correlation, and data comparison.
• Inference: This is the technique of drawing conclusions and implications from the data. Inference should include the following steps: data hypothesis testing, data model building, data model testing, data model interpretation, and data model validation.

The Criteria and Strategies of Data Interpretation and Discussion

Data discussion is the process of analyzing, interpreting, and communicating the results of data analysis to various audiences. Data discussion can be done for different purposes, such as informing decision-making, generating new insights, evaluating outcomes, or sharing best practices. Data discussion can also be done in different formats, such as reports, presentations, dashboards, or visualizations.

However, data discussion is not a straightforward task. It requires careful planning, preparation, and execution to ensure that the data is presented in a clear, accurate, and meaningful way. Moreover, data discussion involves various challenges, such as dealing with complex or incomplete data, addressing ethical or privacy issues, or handling feedback or criticism. Therefore, data discussion needs to follow some criteria and strategies to ensure its quality and effectiveness.

Criteria for Data Discussion

The criteria for data discussion are the standards or principles that guide the data discussion process and evaluate its quality. The criteria for data discussion can vary depending on the context, purpose, and audience of the data discussion, but some common criteria are:

• Relevance: The data discussion should address the main question or problem that motivated the data analysis. The data discussion should also focus on the key findings or implications of the data analysis, and avoid irrelevant or unnecessary details.
• Accuracy: The data discussion should present the data and the analysis methods in a truthful and precise way. The data discussion should also acknowledge the limitations, assumptions, or uncertainties of the data and the analysis, and avoid misleading or biased interpretations.
• Clarity: The data discussion should use clear and concise language, and avoid jargon or technical terms that the audience may not understand. The data discussion should also use appropriate and consistent formats, such as tables, charts, or graphs, to display the data and the analysis results.
• Coherence: The data discussion should have a logical and coherent structure, and follow a clear and consistent narrative. The data discussion should also use transitions, headings, or summaries to connect the different parts of the data discussion, and guide the audience through the main points and arguments.
• Impact: The data discussion should highlight the significance and implications of the data and the analysis results for the audience and the context. The data discussion should also provide recommendations, suggestions, or actions based on the data and the analysis results, and persuade the audience to take them into account.

Strategies for Data Discussion

The strategies for data discussion are the techniques or methods that help the data discussion process and improve its quality. The strategies for data discussion can vary depending on the context, purpose, and audience of the data discussion, but some common strategies are:

• Know your audience: Before starting the data discussion, you should identify and understand your audience, such as their background, interests, expectations, and needs. You should also tailor your data discussion to your audience, such as using appropriate language, tone, and format, and emphasizing the relevant and important aspects of the data and the analysis results.
• Plan your data discussion: Before starting the data discussion, you should plan and organize your data discussion, such as defining the main goal, message, and structure of your data discussion, and selecting the most suitable and effective data and analysis results to support your data discussion. You should also prepare and rehearse your data discussion, such as checking the accuracy and clarity of your data and analysis results, and practicing your delivery and presentation skills.
• Engage your audience: During the data discussion, you should engage and interact with your audience, such as using stories, examples, or analogies to illustrate your data and analysis results, and using questions, feedback, or polls to involve your audience in the data discussion. You should also adapt and adjust your data discussion to your audience, such as modifying your pace, tone, or emphasis, and addressing any questions, comments, or concerns from your audience.
• Visualize your data: During the data discussion, you should visualize and display your data and analysis results in a clear and attractive way, such as using appropriate and consistent colors, shapes, or symbols, and using labels, titles, or legends to explain your data and analysis results. You should also simplify and highlight your data and analysis results, such as using filters, aggregations, or calculations, and using annotations, captions, or headlines to emphasize your data and analysis results.
• Evaluate your data discussion: After the data discussion, you should evaluate and reflect on your data discussion, such as collecting and analyzing the feedback, reactions, or outcomes from your audience and the context, and identifying the strengths, weaknesses, or areas of improvement of your data discussion. You should also follow up and communicate with your audience, such as providing additional information, resources, or support, and maintaining the relationship and collaboration with your audience.

Ethical Considerations and Responsibilities of Good Agricultural Scientific Research 

Good agricultural scientific research should also respect the ethical principles and standards that govern the conduct of research in general and in the field of agriculture in particular. These include: 

• Respect for the environment: The research should minimize the negative impacts and maximize the positive impacts on the environment, such as soil, water, air, biodiversity, and ecosystems, and follow the principles of sustainability and conservation. 

• Respect for animals: The research should ensure the welfare and well-being of the animals involved in the research, such as livestock, poultry, fish, and wildlife, and follow the principles of humane treatment, care, and use. 

• Respect for humans: The research should protect the rights and interests of the humans involved in the research, such as researchers, farmers, consumers, and communities, and follow the principles of informed consent, confidentiality, privacy, and safety. 

• Respect for the integrity of science: The research should uphold the values and norms of scientific inquiry, such as honesty, accuracy, objectivity, openness, and accountability, and follow the principles of academic integrity, such as citation, acknowledgment, and avoidance of plagiarism, fabrication, falsification, and misrepresentation. 

Good agricultural scientific research is a complex and challenging endeavor that requires adherence to the principles and methods of effective and ethical research. Agricultural research is a rewarding and challenging career that requires a combination of scientific knowledge, practical skills, creativity, and ethical awareness. It is also a dynamic and evolving field that responds to the changing needs and demands of the world. As an aspiring researcher, you have the opportunity and responsibility to make a difference in the field of agricultural science and society. We hope that this document has inspired and motivated you to pursue your research goals and aspirations.