Data is essential for any business, but especially for agribusiness, where many factors can affect productivity, profitability, and sustainability. However, not all data is equally relevant or useful. How can you identify the most important data points in agribusiness that can help you make better decisions and improve your performance? Here are some tips to guide you.
The first step to identify the most important data points in agribusiness is to define your goals and objectives. What are you trying to achieve, improve, or optimize? For example, you may want to increase yield, reduce costs, enhance quality, or comply with environmental standards. Your goals and objectives will help you narrow down the data points that are most relevant to your situation and needs.
1. Production data - including yield, quality, input costs, the time required to harvest/market if it is a plant/land-based activity 2. Government/Policy data - export incentive/Government subsidies or Support price/Import substitution possibility. Disincentive (e.g Tobacco) 3. Financial Data- Historical price points for both input costs and realization, Profitability & Opportunity cost 4.
Agribusiness encompasses the economic sectors for farming and business related activities . It defines the supply chain, procurement, logistics and how goods are made available to the consumer. The important data points for agribusiness can be planting and fertilizing target and actual population, spacing, total acres, moisture levels at harvest time and grain temperature. Combining these data points/ variables and applying data analysis life cycle on the data sets to train models shall result in various outcomes and problem solutions. One of the objective can be to analyze the use of drones for scouting for pests & diseases, monitoring water stress, screening plants, locating stray livestock, and gathering data for flood risk modelling
Agriculture revolution and geo politics. Political dynamics of the world are meant to control agricultural performance so as to have dominance on agribusiness sector hence Political and economic control control
For any set of data to be important in agribusiness , the most effective way is to identify a project within the system, identify its objectives, map out key data input then how to attain the dat. that way it will determine what is important, cost benefit analysis will require a separate set of data as opposed to yield performance data requirements
As I have worked on the field with village level agri business participants. From the farmers perspective - cost of harvesting / labour - cost of transportation to dispose the produce - cost of storage - market trend - availability of seeds and fertilisers - other growers data - government msp vs cost of production - quality segregation and variety selection - soil testing data - maintenance of land records - government agencies suggestions - insurance cost - repayment of loans secured from traders and commission agents. - Health conditions of the farmer to cultivate labour demanding crops. The secondary market side - exchange trade data - local market trends - cultivation reports - international restrictions, trade agreements
The next step is to choose your data sources and methods. Where will you get the data from, and how will you collect, store, and analyze it? Depending on your goals and objectives, you may need different types of data, such as soil, weather, crop, animal, market, or financial data. You may also need different methods, such as sensors, drones, satellites, surveys, or databases. You should choose the data sources and methods that are reliable, accurate, timely, and cost-effective.
Identify critical data points in agribusiness by focusing on factors like weather patterns, soil health metrics, and market demand, as these directly impact crop yield, quality, and profitability.
Selecting data sources and collection methods in agribusiness hinges on their alignment with your objectives. For diverse needs like soil composition, climate conditions, or market trends, employ tools ranging from ground sensors and drones to satellite imagery and market databases. Opt for reliable, precise, and timely sources that also respect budget constraints, ensuring an efficient and informed decision-making process within your agricultural operations.
Mostly from Government Agencies, for small farmer. Big land holders can avail servises from private consultant's. Now small farmer can also use online available data for decision-making.
Animal production holds significant importance in agribusiness, particularly in Brazil. Precision livestock farming (PLF) is vital for effective management; however, it requires a careful choice of data sources and methods. Mobile-based manual data input offers a simple and accessible means of data collection. Automation through devices such as automatic scales and sensors are the state-of-the-art technologies. Artificial intelligence (AI) plays a transformative role by enabling swift and precise analysis of vast datasets. Machine learning algorithms within AI aid in pattern recognition and trend prediction. Importantly, PLF not only enhances efficiency but also promotes animal welfare and environmentally sustainable management practices.
The third step is to evaluate your data quality and validity. How trustworthy and relevant is the data that you have collected or accessed? You should check for any errors, gaps, biases, or inconsistencies in the data, and correct or discard them if possible. You should also verify that the data is valid for your goals and objectives, and that it reflects the reality of your agribusiness context and conditions.
We need to contribute time in data cleansing , to check for nulls, outliers and inconsistencies . Finding the gaps and clusters in data points make sure the quality of data .
A garantia da qualidade e veracidade dos dados no agronegócio é fundamental. O princípio "lixo entra, lixo sai" sublinha a importancia de dados precisos, pois a falta de qualidade pode levar a análises e decis?es falhas. Esses dados afetam previs?es de safra, gest?o de recursos, análise de mercado e etc. Para validar sua acurácia, métodos incluem análise estatística, compara??o com fontes confiáveis, e testes de integridade. Investir em coleta e análise de dados robustos, e tecnologias para precis?o é essencial. Isso envolve verificar fontes, implementar sistemas de valida??o, e atualizar bases de dados, assegurando decis?es baseadas em informa??es confiáveis, conduzindo a uma gest?o eficiente e resultados bem-sucedidos no agronegócio.
Data available in public domain is not trust worthy. Need to do your own field work to validate, before you move forward. Time to work on collaborative data collection and sharing by AgBiz and AgTech companies to help startups. Move away from government statistics.
Data quality and validity are crucial in agribusiness analytics. Assessing the trustworthiness of your data involves checking for errors, inconsistencies, or biases, correcting them where feasible. Ensure the data's relevance and applicability to your specific goals, confirming it accurately represents your agribusiness's real-world context. High-quality, valid data is the backbone of sound decision-making.
At the time of starting data collection we must know that what is the aim of this data collection. Then we must define a systemic data collection system.
The fourth step is to analyze your data and extract insights. How can you interpret and understand the data that you have gathered or obtained? You should use appropriate tools and techniques, such as statistics, charts, graphs, dashboards, or models, to visualize and summarize the data. You should also look for patterns, trends, correlations, or anomalies in the data, and identify the causes and effects of the data points.
In Agriculture where diversity in crops, seeds, water quality/availability and weather vary vastly within neighbouring geographic areas, one size fits all will not work. Need to collect data specifically for each crop for each village (looks ideoitic and immpossible, but it is the truth). I have seen variations within a village.
Analyzing agribusiness data involves employing statistical methods and visualization tools like graphs and dashboards to decipher patterns and correlations. By scrutinizing trends and irregularities, you can uncover causal relationships within the data, gaining actionable insights for strategic decisions in your agricultural practices.
This depends fully on location , size of farm , irrigation facilities available , manpower availablity , storage facility available and near by market . Use of Collected data may vary on above parameters for small farmers and large land holders.
There are already several software options that utilize AI algorithms and Big Data technology to analyze large volumes of data, both in agriculture and livestock farming. These technologies are becoming increasingly accessible. Most of them operate in the Cloud, allowing users to access insights anytime, anywhere, and make faster and more accurate decisions based on data.
The fifth step is to apply your data and insights to your decisions. How can you use the data and insights that you have derived to improve your agribusiness performance and outcomes? You should compare the data and insights with your goals and objectives, and evaluate the pros and cons of different options and scenarios. You should also test and validate your decisions with experiments, feedback, or benchmarks, and monitor and measure the results and impacts of your decisions.
Unfortunately, many startups do this exercise. But take their decisions, in spite of the data and analysis says differently, because, of their passion to their invention than focus on feasibility. What I mean is moderation or modification in their product/services to suit market needs. Farmer must be the focus and not supeirority of company's new invention over others.
Data support our decision making process . We can plan our activity based on available data with considering or persiving risk and how to control or minimise the risks.
When making decisions on data gathered in agriculture , you should look at trends of market and seasons more than any other thing. Production and market trends are directly linked. As if you produce when market demand is low. You will sell at odd prices , but if you produce when everyone is not producing and there is high demand. Your going to make money
Utilize the data and insights obtained to enhance your agribusiness by aligning them with your set goals and evaluating various scenarios. Implement decision-making by testing hypotheses, seeking feedback, and comparing against benchmarks. Monitor the outcomes to measure the impact and effectiveness of these data-driven decisions on your agricultural operations.
Once data is analyzed and insights are generated, it is possible to establish an alert system for variations in monitored parameters. An anomaly outside the acceptable standard (measured by standard deviations, for instance) can trigger an alarm, notifying the operator to make real-time decisions, provided continuous monitoring and data collection are in place. The advantages of these alerts lie in the ability for producers/companies to address issues as they occur, thereby preventing losses.
The sixth and final step is to review and update your data and decisions. How can you ensure that your data and decisions are still relevant and effective over time? You should review your data and decisions regularly, and update them as needed. You should also consider new data sources and methods, new goals and objectives, or new challenges and opportunities that may arise in your agribusiness environment and market.
If a crop is a regulated crop like tobacco, one needs to know the regulatory and legal framework governing it's production. The relevant ACT and regulations become important data points.
To maintain the relevance and effectiveness of your data and decisions in agribusiness, conduct regular reviews and updates. Adapt to new data sources, methods, evolving goals, and emerging market challenges. Stay responsive to changing conditions in your agribusiness environment, ensuring your strategies and decisions continue to align with current realities and opportunities.
Only strategic thinking would help in creating a sustainable and profitable enterprise. Extension of business in a matrix form would establish a forward thinking organisation and offer necessary width and depth. Once the fabric is made and organisation is able to weather any constraints and tumultuous times and sails along. It is necessary that good brainstorming leads to a vibrant idea which when commercially deployed will take in profits. It is therefore necessary to gather the latest data and evaluate without any bias and self aggrandised approach .
Si nuestra base de datos crece lineal o exponencialmente, es importante prepararnos con los servidores que estén preparados en capacidad y eficiencia, con esto poder disponer de los datos en el futuro.
In agribusiness, key data points would only be guided by the enterprise chosen and the extend to which it operates. The common denominator wil include and not limited to: 1. Defining the business goals and objective 2.define a framework of data sources and methodology. 3. The quality of data and validation 4. Analysing the data in line with business objectives 5. Using the data analysis outcomes in the business decision making. 6. Monitor and evaluate outcomes from decisions made.