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Week Four: Fertilizer Trials

With the improved weather over the weekend, we were able to get into the field and place some stakes for nitrogen fertilizer trials this week! I attached a picture of the stakes below. They will be used as a reference for field treatments along with GPS coordinates to be taken at a later date. To place stakes, Greg and I followed a map indicating where each plot was located in the field. Plots have randomized locations over several trials in order to avoid environmental impacts from certain field sections influencing the final results.

Fertilizer stake and label

Top: Plot or treatment number
Middle: Pre-plant nitrogen treatment in pounds per acre
Bottom: Sidedress nitrogen treatment in pounds per acre


The trial features rye terminated at several different stages along with different volumes of both pre-plant and sidedress nitrogen fertilizer. Cover crops like these are important because they can improve soil health and increase soil organic matter, both of which provide a ton of value to New York State farmers. It was really cool to see the corn poking out from under the rye stubble, especially considering the difficult planting and field work conditions that farmers have faced this year.

Week Three: Extension and Communication

This week, several technicians from a NYS digital agriculture company came in for a yield cleaning training session. This was the first time that I have had a major role in a training session so I was a bit nervous. Using Yield Editor, I walked one technician through the initial settings selection process. There are 4 distinct settings used by Yield Editor to clean harvest maps. Flow delay (as pictured above) is caused by the gap in time between the actual harvest of the crop and the moment when the crop is massed by a sensor. Moisture delay is caused by a similar issue, and is especially important when cleaning silage data (as the moisture of silage is significantly higher and more variable than corn grain moisture). Start Pass Delay and End Pass Delay are both caused by the slowing down and speeding up of the harvester at the edge of the field, which often leads to unreliable yield data.

This is an image pulled from a PowerPoint presentation that I created to help farm consultants learn the data cleaning process. As more and more farms in New York State get yield monitors on their corn harvesters, it will become increasingly important for consultants to feel confident working with that data.

This is an image pulled from a PowerPoint presentation that I created to help farm consultants learn the data cleaning process. As more and more farms in New York State get yield monitors on their corn harvesters, it will become increasingly important for consultants to feel confident working with that data.


By using both the Automated Yield Cleaning Expert (a Yield Editor feature that estimates the proper delay settings) and a more guess-and-check method, yield cleaning technicians are able to determine the proper initial settings after manually examining only 10 fields from each farm annually. After finding the proper settings for the farm, technicians are able to use low-level programming to automatically clean the harvest data from the remaining fields. This saves farmers a lot of time working with data and still provides the high level of accuracy needed for yield estimation.

Week Two: Intro to R

This week, I spent a couple of days working with Dilip to learn the process for creating farm reports from yield data sets. This is the way that NMSP shows farmers what the program does with donated harvest data and makes it easier to understand the impact of the data cleaning process. Using RStudio (pictured below) and the R programming language, I am now able to take cleaned yield data files from Yield Editor and quickly create graphs that illustrate which parts of the field contained the best data and which sections had to be removed (commonly called headlands). The reports also contain several interesting pieces of analysis. The average yield for each soil type in each field is listed on one page, with the average yield for each soil type across the whole farm listed later in the report. This information helps farmers to better understand why certain fields are high yielding and other fields continually underperform compared to the whole farm average.

Example of R code

This is an example of R code. I also went to a free R coding workshop put on by the Cornell Statistical Consulting Unit this week which was very helpful. There are a lot of good resources to start learning R, but practice is key!

So Much FRECin’ Fruit

Hey y’all, my name’s Bethany and I will be a junior this fall. I am from Biglerville, Pennsylvania and I have a background in fruit trees, but specifically apples. This summer I am an intern at FREC, Penn State Extension’s Fruit Research and Extension Center, conveniently located also in Biglerville, PA. At FREC, there are 5 lead scientists, each conducting research on horticulture, plant pathology, agricultural engineering, or entomology. I am working as an intern for Dr. Schupp, the horticulturalist there. He works specifically with apples, pears, and peaches, but mainly with apples because they are popular in the surrounding area. Within apples, I noticed that he has mainly been working with different types of thinning, pruning, and tree training to produce the most amount of apples. Additionally, he is also working on some interesting projects with the agricultural engineers for automated pruning and picking.

I spend most of my days outside, tending to the trees or modifying them to the produce the desired result. Recently, I have been thinning and pruning a lot of peach trees, thinning apple trees to define a strong terminal bud, and clipping up the recently planted Ever Crisp and Premiere Honey Crisp apples.


Pictured above is the NBlosi, a scissor lift with a shifting platform that allows us to easily reach into the trees. On the right are the Gala apple trees that we trained.

Further, I have also become fascinated by the different bugs around the orchard; I usually take a picture of them and show them to the entomology interns during lunch. I’ll post some of my favorites below:

Pictured above on the left is a leather wing, also known as a soldier beetle. In the middle is a common leaf beetle that caught be by surprise. On the right is a praying mantis nest that I found pruning.

Every year, Dr. Schupp hands his summer interns their own project to take charge of. This year, we get to run a project that deals with a new club variety of apple called Sweet Cheeks. However, it has many fruit finish issues. Our job is to determine whether the russetting is early or late onset and suggest ways to prevent it. I am excited to explain about it more in my next post!

Week One: Yield Editor

My first week was a bit of a rush! I started out using Yield Editor (a free software published by USDA-ARS) to clean corn data from a farm that works with NMSP on a couple of projects. Cleaning harvest data is important because it allows farmers to know exactly how much they are able to produce in certain fields. It is also important to have very accurate data for making decisions about which fertilizers to use and how much of each nutrient to apply in different sections of the farm.

Example of flow delay correction in Yield Editor

Left: Raw corn harvest data. Right: Data cleaned following the Kharel et al. protocol


Data technicians at NMSP follow the yield cleaning protocol written by Dr. Tulsi Kharel, one of my supervisors at the lab. The manual is very helpful because it’s easy to forget steps in the yield data cleaning process if you are just working from memory. Following the protocol also ensures that data sets cleaned by different technicians will still be consistent and highly accurate. Tulsi is leaving for a position with the USDA in Arkansas soon which will be sad but he has already taught me a lot about working with agronomic data. Dilip, the lead data analyst in the lab, has also been very generous with his time and has started to teach me how to use the programming language R. This will hopefully allow me to start creating farm reports based off of the cleaned yield data, which would be a new and exciting skill!

Applied Precision Ag – Data that Works for Farmers

Ben Lehman – CCE 2019 Precision Ag Intern

Hi, my name is Ben! I’m a rising junior in Agricultural Sciences at Cornell. This summer I ‘ll be keeping a blog of my experience interning with the Nutrient Management Spear Program (NMSP). Dr. Quirine Ketterings leads the lab and has already assigned a few cool new projects for me to work on this summer! A lot of my work will be processing corn harvest data, using AgLeader SMS for pre-processing, Yield Editor for correcting errors and delays in yield data, and RStudio to create reports and graphs for farmers. I’ll also be doing some field work and getting a better understanding of the lab’s mission as a whole.


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