How Big Data Improves Yields in Agriculture

The agricultural sector is seeing major shifts in its supply chain, roles of different players and power relations between them, and the whole supply chain due to Big Data.

Smart sensors and devices powered by the latest technologies like artificial intelligence, the Internet of Things, and Cloud Computing, accumulate massive volumes of data of different types that are revolutionizing the cyber-physical farm management cycle. Big Data is being used to:

  • predict the best practices in farming operations,
  • support operational decision-making processes in real-time, and
  • re-design business processes to improve business models and develop innovative ones.

Here are some of the latest trends in the world of agriculture that are emerging due to Big Data:

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1. Smart Farming

Farms and other agricultural enterprises are just as sensitive to prices in the market as any other industry. Knowing which key metrics or KPIs (key performance indicators) to study can help farmers keep a check on rising costs or tumbling revenues. The continuous feed of data allows them to take timely action and increase their productivity and profit.

Agriculture researcher and assignment helper Vinay Ahuja says, "KPIs help agricultural businesses save time and make optimal use of their resources by taking evidence-based decisions." He suggests some of the metrics that aid the smart farming trend:

  • Feed and water consumption: Water, manure, feeds, pesticides, and insecticides cost a significant sum of money in agriculture. Data-driven agricultural decisions can help minimize their use without damaging the crop or stock.
  • The wages to revenue ratio: People that all farmers would like to pay less and earn more but it has been observed that those who pay higher wages to their employees see more productivity and profit. This metric can guide farmers to see how the wages to revenue ratio impact their profitability.
  • The yield of stock: Most agricultural businesses have limited resources. The yield of stock can guide them to assess how many bushels of corn you grow per acre or how many cattle-heads can you rear in your farm. You can relate this with data related to weather, rearing or farming processes, and natural disasters to find out how different factors affect the yield.
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2. Sustainable and Resilient Agricultural Production

Agricultural productivity can strengthen a country's food security and economy and can also help with conserving its environmental resources. Three types of productivity metrics that can help a country achieve sustainable productivity are:

  • Partial Factor Productivity (PFP),
  • Total Factor Productivity (TFP), and
  • Total Resource Productivity (TRP)."

PFP measures output per unit of one input. TFP measures total marketable outputs vs total marketable inputs in an agricultural production process. TRP measures non-market factors (such as environmental factors) and their impact on production efficiency.

Studying data over the long term and understanding inter-relationships between different ecosystems can help make agriculture more sustainable and resilient to challenges like changing weather conditions. Data related to soil quality and moisture, water quality, wide-area mapping, biodiversity, non-cropped vegetation, and pests and diseases can help policymakers in coming up with safe operating practices, setting local thresholds, and deciding early warning indicators.

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3. Open Data Sharing

The agricultural sector is struggling with severe data gaps right now. To bridge these gaps, a global information system is coming up which allows open data sharing and real-time learning. This systematic and reliable collection of data at the local, national and global level opens up many new opportunities for agricultural innovation.

Investing in the digital exchange of real-time information and learning can democratize information and empower farmers to make informed choices. It can also help in achieving Sustainable Development Goals (SDGs) and accelerating the pace of development. Existing technologies like Geographic Information Systems (GIS), Global Positioning Systems (GPS), and Remote Sensing can help in shaping up the digital agriculture world where data and information are transparent, easily shareable, and disaggregated.

Population and agricultural census data, national household surveys, surveys on market and consumer behavior, and composite indices that keep a track of multiple functions of agriculture and food systems are some of the other records that can be used to anticipate, track, and manage agricultural challenges.

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4. More Crop per Drop

Tanja Folnovic, an agronomy expert, writes in a blog post that rain-fed agriculture accounts for 60% of food production in developing countries from 80% of arable land. On the other hand, 20% of arable land which is irrigated produces 40% of all crops and almost 60% of the total cereal production.

It is expected that by 2050, the demand for freshwater would increase by 18% in developed countries and by 40% in developing nations. Hence, achieving water efficiency needs to be a priority for agriculturists.

Data-driven irrigation systems can achieve high water use efficiency and help farmers in optimum irrigation scheduling. Water productivity data can help farmers:

  • predict loss of yield due to water stress,
  • predict improvement in yield with supplemental irrigation, and
  • improve the allocation of water supply among farmers, among crops, and during different growth stages of crops.
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5. Stewardship Index for Specialty Crops (SISC)

This tool allows sustainable and optimal production of specialty crops, such as fruits, nuts and vegetables. It consistently measures on-farm metrics accurately. Run by a coalition of producers, buyers and public-interest groups, SISC develops and refines tools for farmers that measure specific outcomes that address the unique needs of the specialty crop industry.

Eight key metrics SISC measures are:

  1. Applied water use efficiency
  2. Habitat and biodiversity
  3. Energy use
  4. Nitrogen use
  5. Phosphorus use
  6. Soil organic matter
  7. Simple irrigation efficiency
  8. Food loss

The scope of big data applications in agriculture is growing every day. The granular data on rainfall patterns and fertilizer requirements can go a long way in feeding the growing population worldwide. It can also help farmers mitigate the side-effects of pesticides by advising when to use them and by how much.

This is just the tip of the iceberg of what Big Data is already doing in the field of agriculture. Soon, we will see many more breakthroughs and innovations. Until then, keep exploring.