Agriculture Analytics Market worth $2.5 billion by 2028 - Exclusive Report by MarketsandMarkets™
The increasing demand for food security due to a growing global population is pushing farmers to optimize crop yields and resource management using data-driven insights. Technological advancements, including IoT and remote sensing, generate vast amounts of data that can be analyzed to improve farming practices will further drive the Agriculture Analytics Market.
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342 - Tables
54 - Figures
318 - Pages
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Scope of the Report
The solution segment is expected to hold the highest market share
Agriculture analytics solutions utilize advanced technologies and data analysis techniques to optimize and improve agricultural operations. These solutions collect, process, and analyze vast amounts of data from multiple sources, including weather patterns, soil conditions, crop growth, and machinery performance. By leveraging this data, agriculture analytics solutions provide valuable insights and recommendations to farmers and agricultural stakeholders.
By technology, Remote sensing and satellite imagery to hold the highest market share
The use of remote sensing and satellite imagery has revolutionized agriculture analytics by providing valuable insights and information for optimizing farming practices. These technologies allow farmers and researchers to gather data on crop health, land use, and environmental conditions without extensive on-the-ground monitoring. Remote sensing and satellite imagery also enable monitoring of environmental factors affecting agriculture, such as temperature, precipitation, and sunlight availability
By end users, Farmers segment to hold the largest market share during the forecast period
The emergence of precision agriculture, which integrates technologies like GPS, remote sensing, and data analytics, has revolutionized how farmers manage their operations. These tools enable farmers to gather real-time data, generate field maps, and precisely apply inputs like fertilizers and pesticides. By optimizing resource allocation and reducing waste, farmers can improve efficiency and environmental sustainability.
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North America to dominate the market during the forecast period
North America is estimated to account for the largest share of the market in 2023. The region comprises developed countries, such as the US and Canada, and is considered the most advanced region in terms of adopting digital technologies. The presence of many agriculture technology providers, such as IBM, Microsoft, Deere & Company, Granular, and The Climate Corporation, is driving the growth of the market in North America. Also, the rapid developments in infrastructure, high adoption of digital technologies, and demand for data-driven solutions contribute to the Agriculture Analytics Market growth in the region.
Agriculture Analytics Market Dynamics:
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Top Key Companies in Agriculture Analytics Market:
The major vendors offering agriculture analytics solutions are Deere & Company (US), IBM (US), Bayer Ag (Germany), SAP (Germany), Trimble (US), Accenture (Ireland), ABACO (Italy), DeLaval (Sweden), Oracle (US), DTN (US), Farmers Edge (Canada), SAS Institute (US), Iteris (US), PrecisionHawk (US), Conservis (US), Stesalit Systems (India), Agribotix (US), Agrivi (UK), Granular (US), FBN (US), Gro Intelligence (US), Resson (Canada), AgVue Technologies (US), Taranis (US), CropX (Israel), Trace Genomics (US), Fasal (India), AgEye Technologies (US), HelioPas AI (Germany), OneSoil (Switzerland), Root AI (US) and AgShift (US).
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Agriculture Analytics Market Advantages:
Numerous data sets about agricultural yields, weather patterns, soil conditions, and market trends are accessible through agriculture analytics. Utilising this information, farmers may choose the best crops, determine the best times to plant them, and decide how best to fertilise, irrigate, and control pests. Agricultural practises are optimised, production is increased, and costs are decreased through data-driven decision-making.
Agriculture analytics assists in identifying and reducing risks related to weather-related occurrences, disease outbreaks, insect infestations, and market variations by analysing historical and current data. Farmers may better manage crop insurance, modify planting dates, respond pro-actively to possible hazards, and apply preventative measures. The stability and resilience of agricultural operations are improved by this component of risk mitigation.
Agriculture analytics optimises the use of water, fertilisers, and energy to support effective resource management. Farmers may accurately schedule irrigation and only apply water when necessary by keeping an eye on crop growth trends, weather patterns, and soil moisture levels. Similar to how data-driven insights allow for focused application of fertilisers and other inputs, they also minimise waste and maximise resource utilisation. Cost reductions, a lessening of the influence on the environment, and sustainable farming methods are the results of this optimisation.
Beyond the farm gate, agriculture analytics aids in the optimisation of the entire supply chain for agriculture. As a result, farmers are able to better match production to market demands by better projecting crop yields, market demand, and pricing patterns. Analytics also helps with logistics planning, inventory control, and transportation optimisation, ensuring that agricultural products are delivered to markets effectively. Increased consumer happiness, decreased food waste, and higher profitability are all effects of supply chain optimisation.
Farmers who use data analytics can increase production and profitability. Resource allocation, risk mitigation, and supply chain optimisation lead to higher yields, lower costs, and better financial outcomes. A farmer's bottom line can profit from decisions made utilising agriculture analytics that are supported by data.
By facilitating improved resource management, reducing waste, and minimising environmental effect, agriculture analytics supports sustainable farming practises. Farmers may use less water, use less agrochemicals, prevent soil erosion, and maintain biodiversity by maximising inputs and implementing precision agriculture practises. The use of analytics in agriculture supports the objectives of environmental protection and sustainable development.
Report Objectives
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