Yield maps in Precision Agriculture

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!

You’ll excuse me for the bibliographical references that I didn’t reclassify specifically for this post… but you should be able to find them without any problem =)

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