What's Cropping Up? Blog

Articles from the bi-monthly Cornell Field Crops newsletter

Integrating Record Keeping with Whole Farm Nutrient Mass Balance: A Case Study

Jack van Almelo1, Quirine M. Ketterings2 and Sebastian Cela2
1Agricultural Consulting Service Inc., 2 Nutrient Management Spear Program, Department of Animal Science, Cornell University

Introduction

Many farms in New York have participated in the whole farm nutrient mass balance (NMB) assessment in the past ten years. The data from these farms have shown that NMBs can help farmers make strategic decisions, aiding in evaluation of management alternatives that address the farm’s long-term nutrient sustainability and farm profitability as was shown in earlier What’s Cropping Up? articles (Ketterings et al., 2016; Cela et al., 2015a,b; Soberon et al. 2015a,b). An NMB assessment determines the difference between nutrients imported onto the farm (via feed, fertilizer, animals, bedding, and manure), and nutrients exported (as animal products, crops, and manure) divided by the total tillable acres (indicator of the potential environmental footprint of the farm), and divided by total milk production (reflecting the farm’s overall production efficiency).

Past Experiences

van Almelo - Table 1Earlier evaluations for a cross-section of New York dairy farms (102 farms) resulted in the identification of the “optimum operational zone” where farms meet (i.e. not exceed) the feasible NMBs per acre and per cwt milk produced (Table 1). Evaluation of NMB trends over 4 to 6 years for 54 New York dairy farms showed that 63 to 76% of the NMB decreased over time (depending on the nutrient), and >50% of the farms did so while increasing milk production per cow. Farms were able to improve their NMB by making changes in feed and fertilizer imports, animal density, percentage of farm-produced feed and nutrients, and feed nutrient use efficiency. In a follow-up study with a set of 27 farms with 6 to 10 years of data, the percentage of farms with N and P balances in the “optimal operational zone” increased from 22-26% over the first 2 years (depending on the nutrient) to 43-56% over the last 2 years of assessments. Similar improvements were shown in longer-term trends. Thus, farmers who participated in the annual NMB assessment for multiple years in a row tended to make changes that improved NMBs over time.

Need for Automated Data Transfer

Despite recognition of the importance of knowing a farm’s NMB on an annual basis, the time required to conduct a NMB can deter farmers from participating. The information necessary to complete the NMB requires the gathering of information from multiple sources, including from record systems already being used on farms. Where those records systems are software based, there is opportunity for electronic collection and transfer of the necessary information into a NMB calculator, making it quicker to conduct the assessment and reducing the risk of data entry errors.

Project Goals and Methods

We evaluated software packages already in use on many dairy farms to see what information could be directly transferred into a NMB assessment tool. The software packages and data pieces evaluated included Fields & Crops Manager (information on acres, fertilizer, manure exports, yields), Dairy Comp 305, PCDART, and DHI Summaries (information on animal numbers), Feed Watch and TMR Tracker (information on purchased feeds fed) and Center Point Accounting and QuickBooks Accounting (information on purchased and sold feeds, fertilizer and livestock). The NMB assessment module was built into Fields and Crops Manager, an on-farm crop management program. The module used the algorithms of the Cornell NMB calculator (Soberon et al., 2015b). The software had to allow for entry of farm information as it became available (not all at once at the end), and needed a feature that allowed for easy identification of missing data. To address these issues, a spreadsheet-like data entry interface was created with color coded cells where cells with missing data were shown in orange, and cells with suspect data (outside of expected ranges) were in yellow. The interface allowed for addition and changing of information at will.

Six New York dairy farmers participated in the evaluation of the project by supplying their farm data for evaluation. The NMB results were presented and discussed in group meetings with each farmer. The farm teams present in the meetings included at a minimum the owner and one advisor, and for one farm included the owner, farm managers, the farm’s crop consultant and nutrient management planner, and the nutritionist.

Main Findings

Our main findings were:

  • Animal inventory information was typically complete and up-to-date. Animal numbers for milking cows from DHI will be reliable but young stock numbers and weights have to be verified with the farm.
  • Cropland acres were typically recorded in the farm’s Comprehensive Nutrient Management Plan (CNMP).
  • The best source for purchased and sold feed was the farm’s accounting software but to be useful for NMB assessment, the entries require recording of both quantities and nutrient content as invoices are entered. Many farms do not record quantities in their accounting program, or keep records of nutrient contents of each feed purchased or sold, complicating data collection for the NMB assessment.
  • An alternative to retrieve feed use is feed management programs such as Feed Watch or TMR Tracker. Caution is warranted when using such a feed management system to retrieve quantities of feed because any day the system is not functioning, the quantities of feeds fed are not recorded. Additionally, feeding systems only records what is loaded into the feed truck, not feed delivered to the farm but lost prior to feeding. Thus, the final accounting of total quantities fed will be lower than total feed imports.
  • Due to inconsistencies in naming of feed sources, aligning feed composition data and feed quantities required help from the operators and nutritionists. As feed imports are typically a large driver for NMBs, only farms that regularly record quantities of feed purchases or use a well-functioning feeding management system can efficiently conduct a NMB.
  • Fertilizer purchases were easy to retrieve from the farm’s financial accounting system because there are relatively few products involved at any given farm and the transactions occurred typically in narrow windows of time.
  • Crop acres, acres receiving manure, and manure exports (quantity and N, P and K content) were readily available from the farms’ CNMP. However, none of the participants recorded their cropping operations completely enough to generate reliable totals for fertilizer and manure inputs.
  • Bedding imports were difficult to determine as compositional data are typically absent but bedding is only a very small portion of the nutrient imports onto a farm and hence less important for NMB assessment. The only exception is when hay is purchased for bedding given its larger percentage and greater range in nutrient content.
  • Crop yield records were often not maintained, unless crops were sold off the farm and sales records were kept. Use of yield monitoring equipment for silage harvest (corn and alfalfa/grass) will aid in collection of more accurate yield data in future years and allow for improvements in NMB assessment over time.

In general, we found that the records in the Fields and Crops Manager program and other software packages were not complete enough to allow for automated transfer of data into the NMB tool. Instead, the new NMB module within Fields and Crops Manager was used as a platform to enter, calculate, and store the NMB, without direct linkages to other programs.

Farmer Feedback

Despite initial hesitation about participating, all six farmers concluded that (1) the NMB assessment was worth the data collection effort; and (2) meetings with farm advisors (crop planner, nutritionist) greatly improved the value of the NMB assessment. Survey results showed that the project made them think about changes in management that they could consider for the future:

“It makes you think of things in a different light or from a different perspective, than we normally look at things. Rather than think in either dollars and cents, or feed pounds, or feed pounds wasted, or what we are feeding, it makes you look at the bigger picture.”

This is a good check on environmental stewardship and another way to find the proverbial “lowest stave in the barrel”.

The farm meetings were essential to gaining a greater appreciation of the NMB assessment. In the meetings, farmers identified issues and asked many questions. The most dynamic meetings were those where one or more of the farm’s consultants attended the meeting. Some quotes from farmer feedback emphasized the value of the assessment and follow-up meeting:

“My N Balance surprised me, I didn’t think it would be as high as it was. This reinforces that we must export more manure.”

“I want to get a better handle on feed shrink and amounts of feed we move through the farm”

“I am thinking more about manure incorporation to capture more of the manure N and will begin looking into direct injection rather than using an airway.”

“I have to spend some time considering how to lower the %N in my rations and continue pursuing manure application on growing crops.”

 Conclusion

We conclude that successful adoption of the NMB assessments requires minimizing the amount of farmer time necessary to complete the assessment and maximizing the farm’s understanding of the NMB results through farmer meetings. Year-round recording of quantities of purchases in the farm’s accounting software as invoices are entered, and working with a knowledgeable NMB facilitator, are keys to successful adoption.

References

Join Us!

Farmers and farm advisors can access the NMB module via Field and Crops Manager. In addition, a stand-alone calculator and supporting information (manual, input sheets, etc.) are freely downloadable from the NMB project webpage of the Cornell Nutrient Management Spear Program: http://nmsp.cals.cornell.edu/NYOnFarmResearchPartnership/MassBalances.html. Download the input sheets and derive the balance yourself or let us join your evaluation! The software works on Windows OS computers and is currently not available for Macs.

Acknowledgments

This project was a collaboration between Agricultural Consulting Service and the Cornell Nutrient Management Spear Program. We thank the six New York dairy farmers who participated in this study and gave us their feedback on the process and findings. For questions feel free to contact Quirine M. Ketterings at qmk2@cornell.edu. The full article was published in the Journal of Agricultural Science (JAS) published by published by the Canadian Center of Science and Education. It can be found at: http://www.ccsenet.org/journal/index.php/jas/article/view/58723.

 

 

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