What's Cropping Up? Blog

Articles from the bi-monthly Cornell Field Crops newsletter

February 7, 2017
by Cornell Field Crops
Comments Off on What’s Cropping Up? Volume 27 Number 1 – January/February 2017

What’s Cropping Up? Volume 27 Number 1 – January/February 2017

The full version of What’s Cropping Up? Volume 27 No. 1 is available as a downloadable PDF and on issuu.  Individual articles are available below:

February 3, 2017
by Cornell Field Crops
Comments Off on Within-Field Profitability Analysis Informs Agronomic Management Decisions

Within-Field Profitability Analysis Informs Agronomic Management Decisions

Rintaro Kinoshita1, Aaron Ristow1, Harold van Es1, John Dantinne2, and Michael Twining3
Soil and Crop Sciences Section, Cornell University; 2Millersville PA, 3Willard Agri-Service, Inc.

Digital agriculture is a new concept that focuses on the employment of computational and information technologies to improve the profitability and sustainability of agriculture. A promising opportunity is the use of advanced analytical methods on data that are routinely collected on farms, which allow insight into ways to improve management.  One example is the use of combine yield monitor data that are now customarily collected as part of harvest operations.

Crop fields have high variations in crop performance due to varying soil types and topography, which interact with climate and management. A major objective of most growers is to maximize profit. Understanding the underlying profitability potential in varying areas of agricultural fields allows managers to construct zone-specific strategies using information on yield potential and yield-constraining factors.  One might consider two management interventions to optimize profitability: (i) take field areas that are known in advance to be unprofitable out of crop production, and (ii) make underperforming field areas more profitable through improved management implementation.

Therefore, our objectives were to (i) evaluate the variation in spatial patterns of within-field profitability as well as field-average expected profitability, and (ii) determine opportunities for site-specific management change to increase overall field profitability.

The study fields are located in Delaware, Maryland, Virginia, West Virginia, and southeastern Pennsylvania within three physiographic provinces: Coastal Plain, Piedmont, and Blue Ridge. All fields had similar crop rotations: corn, soybean, and wheat or barley. In some cases, double crop soybean was cultivated following the harvest of small grains. Soil nutrient, pest, weed, and irrigation water management on each field was based on individual farm’s management schemes.

Yield data were collected for corn and soybean on 18 fields throughout the study region with well-calibrated yield monitors on combine harvesters. The fields ranged in size from 14.3 to 115.9 acres. For a particular field, the number of growing seasons for which digital yield data were available ranged from 3 to 12, with a range of 1 to 7 growing seasons for a single crop. Irrigation occurred on 6 of the 18 fields. Post-processing of yield data was done using the Yield Editor 2.0.7 software and data were then rasterized (15×15 ft) using the SAGA function within the QGIS environment.

We calculated site-specific profitability using:

Profitability = E[Yield]xPrice – Cost          (1)

where E[Yield] is the expected value of yield estimated from the multi-year average yield, Price is the average price of the crop (corn or soybean), and Cost is the average cost of production. We utilized the 10 year average (2004-to-2013) price of corn and soybean for the profitability calculation, which were $4.73 and $411.45 per bushel, respectively (University of Illinois, 2015). The cost of production was determined using the Farm Resource Regions (USDA-ERS, 2000) for 2014, and ranged from $590.6 to $666.7 per acre for corn and $395.3 to $437.0 per acre for soybean.

We adopted different scenarios for profitability calculation for both rented and owned fields, where we subtracted the land rental rate from the total cost. We also estimated the cost of irrigation to be $138.0 per acre (Tyson and Curtis, 2008), which was added to the total annual cost of production when appropriate.

Results and Discussion

Field-Scale Profitability
In the first analysis, the variation in spatial patterns of within-field profitability as well as field-average expected profitability was evaluated. Expectedly, profitability was affected by owned versus rented field status (Table 1) in both corn and soybean scenarios. In the owner-field scenario, 76% of the field area was on average profitable compared to 57% for the rented scenario.  Profitability was higher overall in soybeans compared to corn for both owned and rented scenarios, partly due to the assumed lower cost of production by approximately $200 per acre (USDA-ERS, 2015) and mostly related to the exclusion of N fertilizer cost.

Irrigation effectively improved profitability (not shown here), even under rented scenarios, indicating that soil moisture shortage is a major yield limiting factor in the Mid-Atlantic region and irrigation can achieve positive profits even after accounting for the added cost.

Spatial Patterns of Profitability and Opportunities for Alternative Land Uses
A second analysis focused on the identification of profitable vs. unprofitable zones within fields.  I.e., based on multi-year yield data, can we consistently expect certain parts of the field to be money losers?  We identified three general categories of within-field profitability patterns: “economically sensitive” (Fig. 1a and 1b), “distinct profitable-unprofitable zones” (Fig. 1c and 1d), and “all-profitable” (Fig. 1e and 1f).

Fig. 1. Selected maps of within-field profitability for the owned-field scenario for corn representing three profitability categories; a and b) economically sensitive; c and d) distinct profitable-unprofitable zones; and e and f) all-profitable.

The economically sensitive fields generally showed high temporal variation in yield pattern due to irregular precipitation, and due to most areas being on average, either slightly profitable (Fig. 1a and 1b; green zones) or slightly unprofitable (yellow zones). This indicates that profitability at a field location strongly depends on the growing season’s environmental conditions and the relative prices of inputs and grains.  The small margins in profitability suggest that modest changes in production efficiencies, grain prices, input costs, or localized yields can turn areas from unprofitable to profitable, or vice versa.  For example, in fields 1a and b most profitable green zones would turn unprofitable with a $0.50 drop in grain prices.  Conversely, reducing fertilizer costs through precision N management could change yellow zones from unprofitable to profitable.

In fields with distinct profitable-unprofitable zones, areas exist that are either consistently profitable or unprofitable (Fig. 1c and 1d). The profitable areas (light and dark blue) presumably have favorable growing conditions, while the consistently unprofitable areas of the fields (orange and red) experience yield-limiting conditions. In some fields, money-losing zones of $200 per acre (red) existed along with $200 per acre money-making zones (blue), resulting in a $400 per acre total profitability range.  The very profitable areas in these fields have higher than field-average yield potential, which may warrant increased site-specific inputs like fertilizer and possibly seed.

The consistently low-yielding zones are possibly compacted headlands, areas experiencing shading from adjacent woods, damaged by wildlife, erosive or poorly-drained. Since profitability for those field zones is predictably negative, overall field profitability would be enhanced by taking those field areas completely out of production. For example, herbaceous buffer strips on the field borders or in swales could be installed to enhance environmental benefits while still providing equipment turnaround space and minimal effects on yield in the rest of the field. We evaluated the removal of low profitability areas (< -$200 per acre) from the field in Figure 1c and found an increase in overall field profitability from $41 to $63 per acre.

Alternatively, potential areas of yield-constraining factors, like compaction, poor drainage or low organic matter, may be identified and managed to make those field areas profitable.  For example, season-specific yield constraints were identified for the two fields in Figure 1d from excessive early-season precipitation combined with poorly-drained soil from concave field areas. Over time, improving the soil health status of these areas could make them profitable.

A third profitability pattern shows field areas being all profitable (Fig. 1e and 1f). These are the most preferred conditions where no additional considerations are warranted and fields can be managed uniformly.

Adoption of yield monitoring has accumulated large amounts of data.  Based on our analysis of multi-year site-specific data, yields vary spatially and temporally at the field scale. We assessed within-field spatial patterns of profitability using grower collected yield data and input cost information for fields in the Mid-Atlantic USA. Three types of profitability pattern categories were identified: economically sensitive, clear profitable-unprofitable zones, and all-profitable.  For fields with areas of permanent yield constraints, the removal of consistently unprofitable areas can increase overall field profitability. Conversely, high-yielding zones may justify more inputs, notably higher fertilizer and possibly seed rates.  Other fields showed high sensitivity to prices and may benefit from improved management efficiencies. In conclusion, the combination of site-specific profitability and yield constraint information can inform future management optimization, including removing field areas from crop production entirely and improving management efficiencies.

This article is based on a paper titled Within-Field Profitability Analysis Informs Agronomic Management Decision in the Mid-Atlantic USA (Kinoshita et al., 2016).

We are grateful to the participating growers for providing the yield data. This work was supported by grants from Willard Agri-Service, Inc., the New York Farm Viability Institute and the USDA-NRCS.

Kinoshita, R., H. van Es, J. Dantinne and M. Twining. 2016. Within-Field Profitability Analysis Informs Agronomic Management Decisions in the Mid-Atlantic, USA. Agricultural and Environmental letters. 1:160034. doi:10.2134/ael2016.09.0034.

Tyson, T.W., and L.M. Curtis. 2008. 60 acre pivot irrigation cost analysis. Department of Biosystems Engineering, Auburn Univ., Auburn.

University of Illinois. 2015. farmdoc. http://www.farmdoc.illinois.edu/manage/pricehistory/price_history.html (accessed 5 November 2015).

USDA-ERS. 2000. Farm resource regions. USDA-ERS, Washington D.C.

USDA-ERS. 2015. Commodity costs and returns. http://www.ers.usda.gov/data-products/commodity-costs-and-returns.aspx (accessed 29 November 2015).


October 5, 2016
by Cornell Field Crops
Comments Off on What’s Cropping Up? – Volume 26 No. 5 – September/October Edition

What’s Cropping Up? – Volume 26 No. 5 – September/October Edition

The full version of What’s Cropping Up? Volume 26 No. 5 is available as a downloadable PDF and on issuu.  Individual articles are available below:



September 12, 2016
by Cornell Field Crops
Comments Off on NYCSGA Precision Ag Project Update

NYCSGA Precision Ag Project Update

Authored by: Savanna Crossman, Research Coordinator, CCA
Statistical Analysis by: Margaret Krause, Cornell University PhD Student

 *This article is part of an ongoing series.  Previous articles can be found at www.nycornsoy.org/research*

crossman-fig-1New York State has always presented a unique challenge to grain growers due to the large amount of in field variability.  In recent years, growers have also added adverse weather conditions to that list.  From the project’s perspective, two of the past three growing seasons have fallen far outside the conditions of a normal year.  The 2015 season brought early precipitation amounts far above than the historical average while the 2016 season is setting up to be one of the driest in decades.  These conditions have resulted in significantly lower, less uniform yields than a typical year such as 2014 (Figure 1).  Variable rate seeding technology is one of the many tools that NYS growers can use to help overcome these challenging conditions.  However, mainstream companies have yet to design a prescription writing software that is developed to meet the unique conditions of New York State and the Northeast.  This project seeks to address this void by developing a software that will do just that.

The project has been collecting data on a large scale since 2014 in order to create a model that will select hybrids and population rates given certain soil properties and characteristics.  To do this, six major data types are being examined; seeding rate, hybrid, topographical information, NRCS soil survey maps, Veris soil sampling data, and grid soil sampling data.  Each data type consists of many variables which are analyzed individually and as interacting networks.

Figure 2. This example random forest regression analysis demonstrates that phosphorus is the variable with the largest effect on yield in this field.

Figure 2. This example random forest regression analysis demonstrates that phosphorus is the variable with the largest effect on yield in this field.

To examine the effect that each variable has on yield, a statistical approach called random forest regression is being used.  This method essentially ranks each variable based on its importance to yield.  The greater the importance number that is assigned to a variable, the larger effect that variable has on yield (Figure 2).

The project has seen that the variables can rank very differently given the field, crop type, or year.  Each field location is unique and thus has a unique combination of variables influencing yield.  Some fields exhibit a very strong yield response to seeding rate, while others exhibit a strong yield response to fertility factors or topography.

Figure 3. 2014 and 2015 resulted in similar population curves on this corn field in Clyde, NY.

Figure 3. 2014 and 2015 resulted in similar population curves on this corn field in Clyde, NY.

Though each field may be different, it is important to see stability within a field across years.  For example, this 80 acre corn field in Clyde, NY produced similar population curves in two drastically different seasons.  The first year, 2014, resulted in high and uniform yields across the field.  The second year, 2015, yielded dramatically lower with a large variance in yield uniformity.  Though the two seasons were very different, both demonstrated a negative yield response to increased seeding rate (Figure 3).  The lowest rate of 27,000 sds/ac yielded the highest across the two years and which was 5,000 sds/ac lower than the grower’s typical rate.  The random forest regression confirmed that seeding rate was the most important variable influencing yield across both years.

This year to year stability in yield response to seeding rate has been seen between crop types as well.  This 60 acre field in Pavilion, NY is managed as a conventional till field in a corn-soybean rotation.  In 2014, its soybean crop exhibited a strong positive yield response to increased seeding rate.  The random forest regression confirmed that seeding rate held a dramatically greater importance than any of the other variables.  The next year, 2015, the field was planted with corn and again exhibited a positive yield response to seeding rate.  This time, the analysis showed that while seeding rate was still the most important variable, many other factors were also important.  This difference could be due physiological preferences between the two crops or the different weather conditions between the two years. (Figure 4)

Figure 4. Random forest regression analysis of 2014 soybean and 2015 corn of a sixty acre field in Pavilion, NY.

Figure 4. Random forest regression analysis of 2014 soybean and 2015 corn of a sixty acre field in Pavilion, NY.

To explore the idea of physiological differences between corn and soybean, some further analysis was conducted.  In this same Pavilion field, soybean exhibited positive relationships with calcium and pH, while corn exhibited negative relationships with the same variables.  These observations are likely related to the differences in crop preference for pH.  Soybeans grow best in more neutral soils where the rhizobia bacteria that provide the soybean plant nitrogen are most active.  Whereas the corn plant is known to prefer a slightly acidic soil where some key micronutrients, such as zinc and manganese, are more available.  It is understanding relationships such as these from an agronomic and a statistical perspective that will result in a reliable model for NYS growers.

This year has marked the first infield testing of the model which will provide side by side comparison of grower practice to the model’s prescriptions.  Each year of additional data collected will serve to further the development of the model into a robust and reliable resource to growers of the State.

The project is currently looking to bring on additional participants for the 2017 season and encourages any interested growers to contact Savanna Crossman at (802) 393-0709 or savanna@nycornsoy.com .



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