In a world where climate change is an everyday reality, careful resource management and getting the most out of whatever resources are available is essential.  Natural resources to grow food, whether that’s water or land, are precious and need to be managed effectively.  Feeding the world’s growing population is requiring more land, and more water to irrigate the crops. In a heating world, this is becoming ever more challenging. Moreover, once the food is grown it needs to be in the right place at the right time and in the right quantities.  Too much food goes to waste because of over production at any given time, or simply because it can’t be delivered in the right condition.

Managing the resources to grow food, and managing which and how much food to grow are two very different challenges. However, there is a common thread between them, which is to be smart about how we go about these.

Starting with actually growing the crops themselves, many times too much water is used due to indiscriminate irrigation, without taking into consideration other factors. Different plants require different amounts of water to grow at their best. Watering plants continuously (using drip irrigation) has been shown to help with plant growth, and is much more effective than watering in large amounts during a short period of time. However, watering plants on the soil surface leads to a lot of water evaporation before the water can trickle down to the roots where it’s then absorbed by the plant. Moreover, there are lots of other factors at play here, notably rainfall (or lack of it), sunlight intensity, air temperature and wind speed.  All of these will affect how fast a plant will grow, how much water and nutrients it needs, its water transpiration rate and so on. By implementing systems to measure, and process, all of this real-time data, one can introduce an automated system for irrigating plants.

This could control not only the quantity of water sent to irrigate the plants, but also the main nutrients needed (usually Nitrogen, Phosphorus and Potassium) as well as the micronutrients. This could be done via a continuous closed feedback loop, which is to measure the soil conditions in real time, and adjust accordingly. More advanced systems could include imaging the crops with drones, looking at leaf coverage and leaf health, and again adjusting accordingly. However, this can only be done if there is data available to know what the ideal conditions are, and then couple that with predictive and optimisation models. Such automated systems, using these optimisation models, have the power to reduce water use by careful water use, and land use by growing crops in the most efficient manner. 

But growing crops effectively is only half the picture. If we grow food that then goes to waste because there’s too much of it, or it can’t be delivered to the right place on time, then the sustainable use of water and land would have been absolutely useless. Good demand forecasting, and supply chain management, is absolutely key here. Predicting how much produce will be required in 6 to 12 months’ time will never be 100% accurate, but it can get pretty close if a robust and validated data model is built. The vagaries of weather (for example, different weather to that expected might give rise to demand for different foods) and new consumer trends are hard to account for, but in most cases seasonal demand for different crops is pretty repetitive. Throw in the fact that different regions of the world are growing at different rates, and different regions might grow and/or consume different crops, and this makes for a very interesting predictive model. Such models would help not only individual farmers to know what to sow when, but would also help governments and regional institutions with agricultural policies.

Collect data, but make sure it's data that can be used to build such models. If in doubt what data to collect, speak to an expert who will help you devise a data collection plan.  With good data come good models.