Problem
With population growing exponentially around the world, the FAO estimates that world food production would need to rise by 70% by 2050. In particular, developing countries would have to increase output by 2X. In contrast, agricultural production in developing countries is lagging far behind the rest of the world. An estimate of corn production shows that yield in the United States are 5X of those in Africa.
A key factor driving this disparity is the low levels of mechanization in emerging economies across the agricultural value chain. In particular, farming practices tend to be reliant on unpredictable traditions (what does this mean? if it is what I think it is, maybe unreliable is a better word?). While high tech and precision farming are growing in the United States, where effective soil maps and understanding of environmental factors is enabling higher yield through lower resources, many resource-poor countries are limited by inconsistent rainfall, poor input quality, and historically ineffective farming practices.
A lack of basic understanding of the soil being cultivated on is a key deterrent. With most agricultural work in developing economies being done by smallholder farmers with less than 1-2 hectares, soil quality can vary significantly from one farm to another. However, practices being adopted tend to be standard across communities.
To be able to ensure that food production rises to meet the increasing demand from growing population, as well as ensure yield parity across the globe – technology must be harnessed.
Solution
SoilMapr will collect multidimensional data about soil through a low-cost device with sensors that can measure soil quality and nutrient mix. Small sensor devices will be placed in farmland, and through a combination of optical, electrochemical, and mechanical sensors will be able to create a soil profile.
The use of SoilMapr would include the following steps:
Before cultivation:
o Place the SoilMapr sensor in the ground for 30 minutes
o SoilMapr will give a complete read of the nutritional qualities of the soil
o Input the crop to be cultivated
o SoilMapr will give a recommendation of nutrients that require additions, and specific fertilizers that could work
o SoilMapr would also predict expected yield based on current conditions, versus improved conditions – allowing users to make a judgment on need for investment.
o Place the SoilMapr sensor in the ground for 30 minutes to effectively monitor soil quality throughout
o SoilMapr would give a read of the nutrient quality, and also expected yield at current levels
SoilMapr has three sensors:
- Optical sensors: Measure infrared levels, organic matter, and moisture content
- Electrochemical sensors: Measure ions, pH levels and nutrient mix
- Mechanical sensors: Measure soil resistance and compaction
Data collected from these sensors is modelled against datasets collected over time of ideal crop production, optimum soil mix, expected yields, harvest time, etc. Analysis will focus on leveraging robust data from evolved agrarian systems in developed markets to developing countries where this data is inaccessible. For example, historical corn production in the USA with irrigation of X liters per day grown on soil with specific characteristics, can serve as a reference and benchmark for what Ethiopian production could be under sim
ilar or slightly different conditions. These ballpark figures would create predictability, and over time feed back into the dataset as reference for other production centers – establishing average performance metrics, as well as best-in-class.
Demonstration
With large cycle times in agriculture, SoilMapr may be able to effectively display the ability to measure soil quality in real time, but would need a long window to validate its predictive qualities. The ideal demonstration would involve working on two identical plots of land, one where conventional farming methods are used, and another where SoilMapr is used. This would allow results to be compared easily to identify the value SoilMapr brings.
The farmer could then leverage SoilMapr’s interface to understand (a) what nutrients need to be added to the soil to be effective for their intended crop, (b) predict expected yield based on those specific environmental factors.
Screenshot of SoilMapr’s potential interface >>>>
Source
http://www.thisisafricaonline.com/News/Closing-Africa-s-agricultural-yield-gap?ct=true
https://www.populationinstitute.org/resources/populationonline/issue/1/8/
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