Synopsis:
Global food producers must build supply chain resilience as climate change threatens disruptions. Here, we look at the scope of geographic expansion to achieve resilience. This proactive approach ensures readiness for securing future food supplies. We deep-dive into how Cropin offers agri-intelligence at a regional level for essential insights to de-risk food supply chains by identifying suitable regions for crop cultivation and fostering partnerships with local farmers.
Impact of climate change on global food production
Climate change significantly challenges global food production, manifesting in heat waves, droughts, floods, and storms. The record-breaking heat of 2023, coupled with the return of El Nino, is causing disruptions that threaten the food supply chains.
Agribusinesses must prioritize risk mitigation to build supply chain resilience, emphasizing responsiveness and readiness. Responsiveness involves promptly identifying and responding to disruptions, supported by pre-disruption readiness strategies that empower food producers to minimize impact.
Earth observation and weather data combined with Artificial intelligence (AI) in agriculture can enable data-driven decisions critical for expanding to new geographies—a readiness approach.
Food security challenges
About 90% of the global food energy intake comes from the 15 most common food staples (cereal grains and tubers). Rice, wheat, and corn (maize) account for two-thirds of this. Rising temperatures reshape land suitability for crops, jeopardizing staples like rice, wheat, soybeans, maize, cocoa, and coffee.
Estimates suggest climate change could increase global food prices by 20% by 2050, an inherent risk, emphasizing the need for greater industry responsiveness and readiness.
Inherent risks in food supply chain management
Reliance on established production zones in food supply chains leaves businesses exposed to significant risks. While large food companies use hedging strategies to manage short-term disruptions, long-term climate uncertainties demand a proactive approach.
A real-world example: The cocoa crunch: The International Cocoa Organization (ICCO) reports a projected deficit of 374,000 tonnes for the 2023-24 cocoa season (74,000 tonnes in 2022-23). This highlights the urgency of diversification. However, brands face challenges like maintaining quality and avoiding delivery disruptions when sourcing from new regions.
Need to de-risk supply chain by expansion, stay a step ahead in the future of food
Expanding into new geographies can mitigate production risks. But choosing the right regions is crucial. Here’s where agri-intelligence steps in. It helps identify suitable areas and establish partnerships with local farmers, ensuring responsiveness to future challenges.
Expanding brings its own hurdles – regulations, logistics, and environmental impact. Evaluating potential regions requires considering producer expertise, resource availability (water, labor, infrastructure), and potential impact on the local environment.
The business case for geographic expansion
In a world of unpredictable weather patterns, businesses must adapt. this means proactively developing resilience by:
- Identifying vulnerable supply chains
- Collaborating to devise comprehensive strategies with actionable steps
Contingency plans should include:
- Yield improvement through climate-resilient practices
- Exploring alternative cultivation regions
- Implementing logistics redundancies
Benefits of expansion to new geographies based on crop suitability analysis
Traditilnal approaches of business expansion and its limitations
Traditionally, food companies have identified geographies for expansion based on historical data and then established relationships with farmers. While valuable, these methods lack the foresight needed to navigate a changing climate. They don’t account for potential disruptions or identify alternative regions with future suitability for specific crops.
Agriculture intelligence: a proactive solution for business expansion for food companies
Today, cutting-edge technology can be leveraged to gain data-driven agri-intelligence, which empowers users with knowledge of ideal geographies for expansion.
Cropin, a global Agtech leader, empowers stakeholders to make informed expansion decisions with its regional intelligence supported by AI/ML-powered geospatial analysis. Due to the diverse responses of various crop varieties to changing geographical, environmental, and climatic conditions, a continuous stream of insights is crucial. Cropin uses its proprietary crop knowledge graphs, built over 14 years and spanning 10,000 crop varieties, to identify suitable regions for cultivating specific crops.
Cropin’s platform feeds data from satellite imagery and weather forecasts to cutting-edge AI/ML algorithms to identify a Cultivation Potential Index for geographic expansion. The index assesses the land’s suitability for specific crops, providing insights into spatial productivity. It considers sunshine, humidity, soil type, farmer profile, water availability, etc. It maps the location of water, suitable soil, and accessibility, as well as potential hazards such as flooding and pollution. Here’s how it works:
Data powerhouse: Cropin’s Regional intelligence leverages a vast pool of data – weather patterns, soil analysis with open-source maps (like SoilGrids), satellite imagery, and historical crop yields.
Analytical edge: By applying advanced AI/ML models to this data, Cropin’s regional intelligence can predict agricultural suitability in different regions.
Crop suitability grid intelligence
Cropin Cloud’s Crop Suitability Grid Intelligence comprehensively analyzes every grid within your chosen region of interest. This empowers you to make informed crop diversification, expansion, or risk mitigation decisions.
Crop suitability grid analysis:
a) Crop map & yield map:
- Acreage of the chosen crop and its historical yield performance for each season within each grid.
- Comparison between historical trends and the current/immediate last season.
- A minimum of two years of historical data is recommended for robust trend analysis.
b) Long-term weather normals:
- Weather data for each grid across the analyzed time frame.
- Comparison of seasonal weather data with long-term normals and suitable growing conditions.
- Enables attribution of potential yield and crop trend variations to weather patterns.
c) Crop potential score:
- Score is based on weather and climatic conditions, along with available soil pH, soil texture, soil organic carbon (soc), elevation, slope, and others.
- Assesses the potential of each grid for the chosen crop beyond historical trends.
d) Disease pressure map:
- Risk category score for crop-specific diseases within each grid.
- Available for each season, covering the past two years and the current/immediate last season.
Cropin’s regional intelligence harnesses the power of data to unveil hidden regions with high potential. Cropin analyses Weather and topography models, Dynamic land-use land-cover patterns (LULC), Crop Identification, and more to monitor crop growth across vast areas and connect your businesses to suitable regions with similar parameters in emerging sustainable markets.
Cropin’s regional intelligence provides:
- Dynamic LULC
- Crop identification
- Identification of Best-performing regions
- Insights on high-stress and high-yield areas
- Understanding the correlation between crop performance & weather and other contributing factors
Cropin AI Team’s efforts with improved systemic efficiency and parallel processing of models in batches deliver a unique platform view in minutes, not months. This globally scalable, completely non-invasive method offers businesses:
- Diversification Opportunities: Identify under-supplied crops with high demand in eco-conscious markets.
- Weather Patterns: Use weather data provided by third-party sources to identify weather patterns in a region with in-built intelligence about events like possible droughts and storms.
- Sustainable Supply Chains: Collaborate with regional farmers with best-performing grids to source ethically grown produce with first-mile traceability.
- Precision Resource Allocation: Optimize water use, minimize waste, and boost productivity based on regional climate and soil conditions.
Case point:
Cropin conducted a pilot project utilizing remote sensing data, proprietary knowledge graphs, and advanced AI/ML models in Kenya and Nigeria. The study unlocked valuable insights into acreage trends and the root causes of yield decline. Learn how Cropin zoomed in on 5×5 km grids to unearth the causes of the production decline.
Time travel to the future with “Cropin Sage” to understand the future potential of a region
Having identified a geographic region with high potential for a specific crop in today’s climate-erratic environment, Cropin takes you a step further with Cropin Sage. This powerful tool lets you time travel (virtually, of course) to understand how future climate will impact a region’s potential for a specific crop. Cropin goes beyond simply identifying potential regions for a specific crop – it analyzes future agricultural trends like yield in correlation with temperature, precipitation, DEWS, etc., so you can confidently plan for future harvests.
CropinSage helps customers query and get any data related to the agricultural domain in a particular region. This platform allows users to query large datasets using natural language queries. The data is shown at a 5 x 5km grid level on a world map, categorized based on the relevant columns. For the grids queried by the user, trend analysis for various properties like crop area, average yield, etc., is also provided in the form of bar charts.
No more guessing games—Cropin Sage tells you what’s thriving today and whether it will flourish tomorrow. All you need to do is ask Cropin Sage the question, and viola, it fetches the prediction.
Figure: Cropin Sage in action
Looking ahead: building a sustainable future
In conclusion, regional intelligence is not just about mitigating risks; it’s about building a more sustainable food future. It can encourage investment in sustainable practices like water-efficient irrigation and climate-smart crop varieties by providing insights into future agricultural suitability.
Regional intelligence is a powerful tool for food companies to navigate the challenges of climate change; this data-driven approach offers a path toward a more secure and sustainable food system for generations to come.
