How CropIn is Impacting The Maize Value Chain in the EU and Latin America Markets


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Think of your favourite popcorn that you can’t seem to stop yourself from munching at the movies. Okay now go into that cereal killer mode and remind yourself of your daily energising cornflakes. Now just throwback to your moments of having corn on a cob. Drool much? Same here. Corn is definitely something that nobody really can say no to.

Often termed as the ‘Queen of Cereals’, the maize crop is one of the most in-demand crops in the world, with the global demand for the crop increasing by 45% in 2020. There has been a 72% increase in demand for maize crops in geographies like the EU and Central America, and 18% growth in developing geographies from the LATAM, Asia and African regions.

The versatility in its usage and the commercial viability in the production of maize makes its value chain of key importance for agribusinesses to understand and analyse. The production of maize is affected by many constraints including abiotic and biotic stress factors, low soil fertility, lack of access to healthy inputs, inaccessibility to mechanisation and poor post-harvest management. The abiotic stresses due to dry spell, saltiness, corrosive soils and insufficiency or harmfulness of micronutrients have hindered the production of maize in many of the developing nations. Smallholder farmers constitute the majority growers of the maize crop. They are faced with a lack of access to proper data-driven farm management, market linkage and crop intelligence. Other imperatives incorporate the need of access to progressed assortments and great quality seed, restricted access to regularly costly manures and pesticides, the requirement for reasonable, small-scale automation to supplant manual work and critical post-harvest misfortunes due to destitute capacity, bug assault and deterioration.

Maize cultivation is affected by a range of biotic stresses including Diseases such as downy mildew, rust, leaf blight, maize streak virus (MSV) and maize lethal necrosis (MLN). Insect pests such as the stem borer which can cause losses of 20-40% in production.

The maize crop is predominantly a cross-pollinating than a self-pollinating crop. Unless carefully controlled, maize plants in a field will differ from one generation to another and from each other. This may cause differences in the quality of the harvest. The problem requires a system that can recognise deviations from the set standard of values that define the quality of the product.



The Solution

Technology is an essential core element in any crop production strategy that aims to increase maize productivity. It is our experience from the past that proves that farm management practises can vastly influence the growth in revenue through systematic methods of agriculture.

Production techniques used in the cultivation of maize decide the output quality and yield as well. Farmers need to keep in mind various factors like the selection of cultivars based on Kharif or Rabi season, sowing time, crop geometry, irrigation techniques, water management, fertigation, Integrated Nutrient Management or INM, Integrated Pest Management or IPM to name the most important ones. The maize crop naturally fits well with diverse crop rotations and intercropping methods as well. So agribusinesses must keep this in mind and plan the upcoming season for the next generations.

Farm Management solutions like SmartFarm Plus and SmartRisk have been engineered keeping in mind the crop necessities and specifications, studied over a long period of time. Adoption of such agritech solutions will most certainly prove lucrative for agribusinesses.

CropIn implemented its solutions in a similar challenge with a client. For maize crop value chain optimization, CropIn deployed its solution SmartRisk and enabled the client to remotely monitor the maize crop farms over various districts of Karnataka, India. Village level acreage identification for maize allowed the company to identify the villages to concentrate for internal sales and inventory management along with deciding on marketing strategy for the upcoming seasons enabling better management of delinquencies. The manual intervention in crop yield estimation was replaced with data-driven decision making by SmartRisk and this made a significant impact in the productivity levels.

Traceability plays a very important role in elevating productivity levels in farm management. Looking at an enterprise level, farm to fork traceability can be achieved through integrable systems that monitor pre-to-post harvest activities, storage and supply chain activities, health and market linkage as well. Introducing traceability into the value chain will bring major changes in farmer livelihoods. Systems like SmartFarm Plus are built with capabilities to integrate multiple tech implementations to blend traceability into the value chain.

Keeping in mind the sustainability factor as well, the systems provide advisories to farmers to ensure they produce without bringing down soil fertility, soil health and purity and adhere to the sustainable development of their geography without fail. SmartFarm Plus is designed keeping in mind the SDGs prescribed by the UN.

To learn more on the plethora of opportunities in sustainable maize crop production and understanding its value chain deeply, get in touch with our expert solution provider today!

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Cropin Apps

Applications for

Cropin Apps is an integrated portfolio of highly customizable apps and solutions that capture and digitise agri-data from the farm to the warehouse to the fork. These applications are designed to scale digital transformation across agriculture and allied industries including forestry, commodity, banking and insurance.


ML-ready data pipelines for enhanced analytics

Cropin Data Hub is designed to deliver the power of unified data by enabling interfacing with all agri-data sources from on-the-field farm management apps, IoT devices, mechanization data from farming resources, drones in agriculture, remote sensing satellite information, weather data, and many more.                                                      


Access to field-tested machine learning models

Cropin Intelligence enables access to over 22 of Cropin’s contextual deep-learning AI models to help agri-businesses with insights and predictive intelligence. Built using the world's largest crop knowledge graph, these models have been field-tested and deployed worldwide while being fine-tuned to work with a range of specific crop varieties, conditions, and locations.