Everything you wanted to know about fertilization and plant nutrition!
World population is expected to grow by over a third by 2050. This means that market demand for food will continue to grow.
Projections show that feeding a world population of 9.1 billion people in 2050 would require raising overall food production by some 70 percent (Fig.1). Production in the developing countries would need to almost double.
In recent years, yield growth rates have slowed down notably in many countries and for major crops.
Figure 1: Projected gap between current food productivity growth and needed growth
There are many reasons why yield gaps exist, including lack of access to information, extension services and technical skills.
Fertilizers are a crucial input driving global food production. There is a clear correlation between increased production and use of fertilizers.
However, excessive fertilizer use results in negative environmental impacts and loss of yields (Fig. 2).
The broad misuse of fertilizers is a global phenomenon which results in decreased yields, fertilizer waste, and damage to soil and water pollution.
Figure 2: Effect of fertilizer use on crop yields
From the standpoint of a single farmer, the main objective is becoming more profitable in a competitive environment, which requires greater efficiency, productivity and sustainability.
When making decisions, farmers face many uncertainties and run risks. Such uncertainties include climate, weather events, water availability, yield uncertainty, equipment failures, commodity and inputs prices and more.
Most of the time, farmers cannot accurately predict what impact their decisions will have on the resulting crop yields.
Because of the complexity of finding the "optimal fertilization range", majority of farmers still rely on trial and error, guesswork and estimation. The result is crops that do not meet their yield potential, and increased environmental pollution.
To minimize the risk, farmers must make better, informed decisions. This requires the farmers and their crop advisers to rely more on accurate data and its analytics, rather than on their intuition.
While general guidelines and cumulative local knowledge got most growers through the growing seasons in decades past, new technologies are taking the guesswork out of growing.
Precision agriculture or “smart farming” involves a technologically-mediated study of how variations within the field impact the growth of plants.
Such technologies allow farmers to increase productivity, save resources, while minimizing environmental impacts, such as leaching of nitrogen and phosphorus runoff.
Optimizing fertilizer management is a three steps process, where each step uses a different type of technology:
1. Collecting relevant data
2. Data analysis and decision making
3. Fertilizer application
The ability to collect digital data is revolutionizing agriculture. It allows collecting very large amount of data, integrate data sources, access the data from practically anywhere and analyse large sets of data to make ‘Big Data-driven decisions’. Here are some of the leading modalities of data collection and analysis:
Satellite and drone imagery – using remote-sensing technologies, such as satellite images, provide data that can be used for nitrogen optimization.
Different spectral indices are being developed and analyzed. The most common ones are NDVI (Normalized Difference Vegetation Index) which estimates vegetation cover, CCCI (Canopy Chlorophyll Content Index) and CNI (Canopy Nitrogen Index).
Field-sensors – nitrogen sensors can be installed in the field or on machinery, such as tractors, and collect data on nitrogen levels.
Weather data – weather data from thousands of weather stations is being collected and analyzed. Accurate weather forecast is important for many decisions that farmers make, including timing of planting and fertilizer applications.
Data libraries – Several international organizations published data libraries on soils and their properties, some digital-ag companies create proprietary data libraries of crop nutrient requirements, yields and soils, based on data-analytics.
Farmer inputs – today, farmers use many cloud-based applications on which they record their data. Aggregated data from farmers creates new opportunities for knowledge-sharing and dissemination.
Big Data analytics of research results, actual and historical field data and continuously updated knowledgebase, revolutionizes the way farmers make daily decisions.
Today, farmers can already have direct access to cloud-based decision making tools, that translate huge amounts of data and analytics into best practices and actionable information.
In contrast to most other tools in agriculture. software algorithms and data analytics do not require any hardware installation. Ideally, software solution should be easily integrated with any external source of data, such as satellites, sensors, drones, machinery and robots.
Digital Ag and Big Data analytics bring new opportunities to yield optimization by precision fertilizer management. Fertilizer optimization requires a continuous and complex analysis of large, dynamic data sets.
Digital-Ag software are designed to help farmers tackle complex problems in crop production, utilizing the accumulating big-data and knowledge.
With little effort, farmers can benefit from such analysis and substantially improve their efficiency and decision making.
Such software can incorporate inputs on climate, soil, water, genetics, energy, economic resources, field history, yields and more.
for example, nitrogen use can become more efficient by analysing soil properties and the rate in which water infiltrates through it, and linking it with intra-field soil testing, mapping of soil and plant nitrogen content, data on nitrogen uptake rates by the crop, yield maps, temperatures, precipitation and more data.
But it is not only nitrogen that matters. There are 13 essential nutrients that are required for proper plant development. The level of each of them should be optimized, and Big-Data analytics can support such complex analysis.
The rapid analysis of the large sets of data also enables farmers to react to real-time events. For instance, the efficiency of nitrogen fertilizers is affected by temperature; a rain event might change the decision regarding the timing of fertilizer application and which type of fertilizer to apply; analysis of satellite imagery can detect problems in specific areas in the field, helping the farmer to locate the problem more easily than ever before etc.
Another example for using Big-Data analysis is the ability to estimate the yield potential for each area of the field. This greatly affects the optimal fertilizer rates needed. Areas in the field with lower yield potential will require lower fertilizer rates than areas with higher yield potential. The same applies to different crop varieties that have different yield potentials.
Predictive analytics uses statistical models and algorithms to predict future events and behaviours. This capability was made possible due to Big-Data collection.
Analysing historical data, such as yields, weather, trends in soil, fertilizer inputs and more, together with real-time data, gives the farmer powerful tools to make informed decisions and manage risks. For instance, minimizing nitrogen leaching can be achieved by using predictive analytics models.
Research results and scientific publications are not readily available to farmers. When they are available, data is often difficult to interpret, is partial or is not relevant to the farmer’s specific field conditions. So, farmers cannot usually benefit and gain insights from such data.
Today, farmers can have direct access to cloud-based decision making tools, that translate huge amounts of data and analytics into best practices and actionable information.
Data and knowledge become available to everyone, big or small farmers, in developed or developing countries. Even traditional farmers can practice precision fertilization to some extent and substantially improve their productivity and sustainability.
Research on plant nutrition has decreased dramatically in recent decades. This is just one of the reasons why old handbooks and manuals are still being used.
Often, research results are inconsistent, making it very difficult to draw conclusions and for a single farmer to make decisions.
With Big Data analytics, as updated global data is continuously flowing in and being analysed, tools are now being developed to transform the data into insights and conclusions that can significantly support plant nutrition research.
There is an ongoing discussion about ‘who owns the data’. Farmers are very much concerned with this question. Data collected using sensors on their farms goes to cloud-based servers, satellites are taking images of their field and analysing that information.
What if this data is manipulated not to the benefit of the farmers? Who will have access to this data? This issue is still being debated.
Various strategies and technologies for precision agriculture have been implemented in the United States since the early 1980s, and expanded to Europe, Canada, and Latin America in the late 1990s.
Obtaining the data and the ability to analyse it, created an opportunity to assess the variations in soil nutrients and crop development within the field and make real-time and site-specific decisions.
In large fields, variable rate application (VRA) of fertilizers improves the efficiency of fertilizer application, where the machinery applies different rates of fertilizer to each section of the field.
Ultimately, this smart environmental choice allows growers to save money and increase yields, by using the right rate and type of fertilizers where they are required, instead of as a broadcast application.
Although there are great opportunities in Big-Data and Digital-Ag, the technologies do not stand by themselves. For the technology to be adopted by the farmer, the solution must be easy to use, add real value and can be easily integrated with his existing equipment and day-to-day workflow.