Yield maps in Precision Agriculture

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This post should have been published a long time ago, especially since it was the subject of my thesis. It’s now done…

First of all, one might ask why a thesis work was proposed on yield data when yield sensors have been around since the 1990s? First, it is clear that yield information is in itself of primary interest to producers. Yield does indeed quantify the level of production in a field and can be easily related to the gross margin of the farm (this will be the subject of a future post). Secondly, from a more general point of view, yield sensors have been available since the early 1990s, which means that historical yield mapping databases are likely to be available on many plots. It might therefore be interesting to return to this yield information with all the knowledge and feedback potentially available. We had imagined that rethinking the processing and analysis of this data by linking it to all the expert knowledge that has been gathered could help generate new information and perhaps raise new relevant questions and perspectives. It should also be noted that the yield map can be seen as a symbol of Precision Agriculture. Knowing that yield sensors were born over two decades ago but are still struggling to be used properly by field operators may also call into question the legitimacy of Precision Agriculture to meet the demands of professionals.

Yield monitors: one of the pioneer sources of PA

Yield monitors have been available since the early 1990’s. They have been key in the development of Precision Agriculture because they were one of the first means to define, quantify, and characterize the within-field variability in crop production

Figure 1. Yield map showing the within-field yield spatial variability

These monitors are mounted on combine harvesters and measure in real-time the amount of grain that passes through the combine when the crop is being harvested. Note that the type of yield measurement that is performed depends on the location of these sensors inside the machine. When the combine passes through the field, the crop (stems and grains) is cut at the header level and flows in the combine through the feed conveyer. The threshing systems then separate the grains from the stems. Grains are cleaned with the fan and sieve tables and work their way to the storage tank, the hoper, flowing through the grain auger trough and grain elevator. Stems are rejected from the combine.

Figure 2. Diagram of a conventional combine harvester (Source : Wikipédia).

Acquisition of within-field yield data: combine harvesters and yield monitors

Yield monitors are usually installed near the grain elevator (Figure 3). Two main systems are usually reported: the volume-flow meters (Figure 3, a, b) and mass-flow meters (Figure 3, c, d,e,f) [Berducat, 2000; Chung et al., 2017].

Volume-flow sensors estimate the volume of grain either on a paddle wheel situated right after the grain elevator (Figure 3, a) or directly within the grain elevator using a one-way light barrier (Figure 3, b). In the first case, a level sensor measures the level of grain that is flowing through the wheel. In the second case, the volume of grain is estimated by the duration of light interruption as the grain flows through the grain elevator. Grain volumes are then converted into grain mass using the specific weight of the grain.

Mass-flow sensors rely either on the force measurement principle (Figure 3 , d,e,f) or on the absorption of gamma rays by mass (Figure 3, c) (Kormann et al., 1998 ). In the first case, the grain weight is estimated using a force transducer that measures the impact force of the grain at the end of the grain elevator. In the second case, a radiation detector measures the absorption of gamma rays (emitted by the radiat ion source) by the grain, which is then used to estimate the grain weight.

Figure 3. Yield monitors: mass and volume-flow sensors (source: Kormann et al., 1998)

All the combine harvester’s systems that come into play to calculate the crop yield are displayed in Figure 4. Moisture sensors are used to provide a yield record at a reference moisture level. These sensors are generally placed near the grain auger or grain elevator to estimate the grain moisture using the dielectric properties of the harvested grain. Note that the positioning systems enable to associate a location in space to yield records and consequently enable to generate yield maps.

Figure 4. Yield mapping technologies within a combine harvester (source: Kormann et al., 1998; Chung et al., 2017)

Characteristics of within-field data

The acquisition of within-field yield data can be understood as a sequential procedure through time during which a combine acquires yield spatial information. T he data collection process follows a temporal dynamic, i.e., observations are recorded in a specific order one at a time as the machine passes through the field (Figure 5). The machine can simply be modelled by a structuring element that moves through the field, i.e., a rectangle whose dimensions are defined by the characteristics of the combine and the associated on-board sensors (yield monitor in this case). On-the-go yield measurements are punctual observations and each point synthesizes the yield response over the corresponding structuring element. The yield spatial resolution is controlled by the distance between consecutive records and determined by the distance between adjacent passes of the machine. The spatial distance between consecutive observations is related to the speed of the machine and the sampling frequency of the sensor . In a given field, this frequency of acquisition is generally stable, meaning that the distance between consecutive records only relies on the travel speed of the combine. On the other hand, when a combine harvester with an on-board grain yield monitor passes through a field, the distance between adjacent passes is related to the width of the cutting bar because the whole field has to be harvested.

Figure 5. Acquiring within-field yield data (blue dots) with a combine harvester (source: Leroux et al., 2018a)

These observations are therefore irregularly-distributed in space because

the intra-row and inter-row distances are different and
(ii) the acquisition conditions, such as the GNSS accuracy or variable combine speed, can impact the spatial distribution of the observations, and
(iii) some observations can be missing (loss of positioning signal, full memory card).

The yield information is also very dense (thousands of points per hectare) and very noisy because of stochastic error in sensor operation, the intrinsic local variab ility in production and errors associated with the combine harvester passing through the field (Simbahan et al., 2004; Sudduth and Drummond, 2007). Nevertheless, within-field yield data usually exhibit quite a strong spatial structure, i.e., spatial observations are well-structured within the fields and yield spatial patterns are clearly visible (Pringle et al., 2003). As most arable crops need to be harvested each year, historical databases of yield mapping are likely to be available on many arable systems. However, it must be said that temporal within-field yield data might not be collocated in space (the yield monitor is not measuring the yield information at the exact same location each year)

Provision and usages

In the Precision Agriculture scientific community, yield data are generally used to (i) quantify and characterize within-field variability, (ii) correlate the yield with an auxiliary variable, and (iii) validate the suitability of a modulation application. And it should be said that it is not very complicated to find research that uses these within-field yield data at some point in time. Nevertheless, a recent scientific mapping study (a kind of mind-map) also showed that the interest of the precision farming scientific community in yield maps had decreased between the periods 2000-2009 and 2010-2016 (Pallottino et al., 2017).

When one is interested in the use of yield sensors in the field, it is another matter… There are already almost no statistics for France (this is why the French observatory of digital uses in France will soon release an infography on the subject). Nevertheless, more or less recent statistics for a number of countries – other than France – can be found in technical reports and scientific bibliography. I invite you to take these statistics with a little hindsight!

First of all, we must be clear on the fact that these trends in use vary greatly between countries (and sometimes even regions) and the cultures being monitored. American farmers may have been the first users to engage themselves in such yield mapping technologies (Griffin et al., 2004; Fountas et al. , 2005). These authors have reported that, by 2005, about 90% of yield monitors in the world were in the US. Griffin and Erickson (2009) have also provided some adoption rates from an Agricultural Resource Management Survey . According to the study and available data, 28% of U.S. corn planted acres (in 2005), 10% of winter wheat (in 2004), and 22% of soybeans (in 2002) were harvested with a combine equipped with a yield monitor. Norwood and Fulton (2009) have concluded in their study that 32% of US farmers w ere using yield monitoring systems. Figure 6 displays the results of another study investigating the adoption of yield mapping systems per crop in United States (Schimmelpfennig, 2016) . Even if the estimates are not exactly the same, trends can be considered similar. Regarding the investigated crops, it clea rly appears that the production of crops such as corn, soybean and wheat has been increasingly followed by farmers from the beginning of 2000’s through yield mapping technologies. Given the observed trends, the adoption in more recent campaigns (2017, 2018 ) should be expected to be again higher. A more recent study also stated the fact that rice farms in USA had been largely adopting yield monitoring technologies, by more than 60% (USDA, 2015).

Figure 6. Adoption of yield mapping technologies per crop in United States

Adoption rates of yield mapping technologies are not as widely reported in other countries, but some national studies intended to provide some detailed numbers. According to the Department for Environment, Food & Rural Affairs, English farmers have experienced a small increase in yield mapping adoption from 7 to 11% between 2009 and 2012 (DEFRA, 2013). In Australia, McCallum and Sargent (2008) have reported a very low adoption rate of yield mapping tech nologies (less than 1%). Within the same country, it was estimated that about 800 yield monitors had been used in the 2000 harvest year (Mondal & Basu, 2009). Fountas et al. (2005) have evaluated that About 400 Danish, 400 British, 300 Swedish and 200 German farmers had adopted yield monitors by the year 2000. Yield mapping technologies have also been reported in developing countries (Say et al., 2017). In Argentina, Mondal and Basu (2009) have reported that about 4% of the grain and oil seed area had been harvested by combines with yield monitors in 2001 (560 yield monitors were in use). According to Keskin and Sekerli (2016), about 500 combine harvesters (3% countrywide) are equipped with yield monitoring systems in Turkey farms. Akdemir (2016) provided a lower adoption rate of yield mapping technologies (310 combines instead of 500) in the same country.

Advantages and limits of within-field yield data

While it is clear that the adoption of yield mapping technologies is increasing in both developed and developing countries, one may wonder which factors and aspects of within-field yield data may have contributed to such a slow adoption of yield mapping technologies. Yield monitors mounted on combine harvesters have been available since the early 1990’s. How ever, yield data still have difficulties in being a decisive component of the decision-making process in precision agriculture studies. In terms of the utility of yield data, multiple issues have been reported by the scientific community. First of all, it is clear that spatial yield patterns originate from an interaction between, management, climate and environmental (soil, landscape, pest attacks, etc) conditions within a cropping season, which means that it is not possible to derive variable-rate applicat ion maps directly for a year n by solely relying on yield data in year n-1. Secondly, it is acknowledged that in annual and perennial crops, the yield temporal variability is often stronger than the yield spatial variability, which can hinder analyses over short and long-time periods (Blackmore et al., 2003; Bramley and Hamilton, 2004; Eghball and Power, 1995; Lamb et al., 1997). This temporal variability is essentially due to non-stable factors, such as climate patterns or the type of crops being grown eac h year (Basso et al., 2012). Multiple authors have stated that the number of years of yield data available to conduct yield temporal analyses was critical (Bakhsh et al., 2000; Kitchen et al., 2005) and some have even tried to propose a minimum number of y ears necessary to obtain reliable results (Ping and Dobermann, 2005). On top of that, yield data often come with a large number of defective observations resulting from the pass of the combine harvester inside the fields, which do not correspond to the yield that should have been obtained under the growing conditions in the field (this will be discussed in the next post). Some of these erroneous observations are widely reported in the literature, e.g., flow delay, filling and emptying times, abrupt speed changes or partially-used cutting bar (Arslan and Colvin, 2002; Sudduth and Drummond, 2007). Some improvements have been proposed, e.g., sensors to measure in real-time the cutting width (Zhao et al., 2010), but most of the combines are not equipped with these new technologies. These errors, if not accounted for, can influence agronomical decisions over the fields (Griffin et al., 2008). From a more practical perspective, it can also be argued that end-users can solely get the yield information at the end of the growing season, which might constitute a limitation in terms of decision support tool.

However, from a precision agriculture standpoint, these high-resolution yield data are a very valuable source of information that would be aberrant not to consider (Florin et al., 2009). Yield spatial patterns are a valuable piece of information to better characterize the sources of spatial variability across the fields. Farmers are interested to know about the mean yield spatial and temporal patterns over their fields so they can make informed and reliable management decisions. It has been shown that, despite a strong temporal variability, it was often possible to detect consistent yield spatial patterns across years (Kitchen et al., 2005; Taylor et al., 2007). Some yield patterns were found consistent even under different crops and varying climate conditions. Furthermore, yield spatial patterns can deliver relevant information with respect to soil characteristics within the field or can help depict the influence of other external factors, such as managemen t practices and weather conditions (Diker et al., 2004). For instance, Taylor et al. (2007) showed that, in specific portions of their field study, crop rotation management in previous years originated variations in yield spatial patterns. Other authors have found that high-yielding areas in dry years could, at the same time, be low-yielding areas in wet years which could give critical information with respect to within-field soil characteristics (Colvin et al., 1997; Sudduth et al., 1997; Taylor et al., 20 07). Another strong advantage of these yield datasets is their accessibility. Something that was considered as a flaw in the previous paragraph can also be seen as a strong asset. Indeed, in most cases, harvest has to be made which means that these data can be collected yearly once farmers have invested in yield monitors , and consequently that large databases of yield mapping can be built. Finally, it should be argued that within-field yield data are directly related to the crop performance and so to the gross margin of the field . As such, these data bring a very comprehensible and practical information to farmers and advisors.

How to valorize yield maps?

Without going into the details of all the projects that could be carried out using yield maps, here is a small outline of what could be done. Some of these ideas have been addressed in the thesis manuscript that you will find on the website. Some of these ideas are quite operational, others are more exploratory. The list is obviously not exhaustive!

Spatialize agronomic models with high-resolution yield data. For example, work had been done on P/K fertilization plans to assess the extent to which within-field yield information could be used to refine fertilization plans, including refining within-field yield potentials and within-field P/K exports.
Spatialize performance/economic profitability maps on farms (this will be the subject of a forthcoming post)
Use yield time series to better understand yield potentials and within-field yield gaps. This work was addressed in the framework of the thesis
Evaluate the potential of modulation actions in a plot of land
Validate the relevance of field experiments
Improve knowledge of the yield at a given spatial scale (region, territory, etc.) for a cooperative or an elevator that would like to obtain supplies.
Use yield maps to guide field sampling campaigns
Use yield time series to improve understanding of yield limiting factors in the plots. Leads were evoked during the discussion of the thesis manuscript.
Use yield time series to assess the risk to a farmer of not changing his practices or not engaging in modulation or Precision Farming practices. Leads were evoked during the discussion of the thesis manuscript.

– ….

One last criticism for manufacturers.

We’ve just talked about accessibility of yield data; let’s talk about interoperability. If you start working with yield data, you’re going to realize very quickly that there are an impressive amount of data formats provided by manufacturers…. But these are mostly private formats ! If you don’t have the proprietary software that goes with it, good luck… You will then have to develop specific modules to be able to read them. Add to that the fact that each constructor measures the variables that interest him, and that the units of measurement are different and you will tear your hair out pretty quickly.

Manufacturers, if you read this post, make your data accessible in an open, free or at least standardized format!

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