What's Cropping Up? Blog

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April 20, 2016
by Cornell Field Crops
Comments Off on What is the Nutrient Balance of Your Dairy Farm?

What is the Nutrient Balance of Your Dairy Farm?

Quirine Ketterings, Sebastian Cela, Karl Czymmek and Steve Crittenden
Nutrient Management Spear Program, Department of Animal Science, Cornell University

Nutrient balance is short for “whole farm nutrient mass balance”. While this is a mouthful to say, knowing a whole farm nutrient mass balance for a farm can help managers identify opportunities for improvements that impact farm profitability and the environment. A whole farm nutrient mass balance or NMB is a way to track the difference between nutrients coming to the farm (mainly in feed and fertilizer) and nutrients leaving the farm (mainly in milk). Dairy farm NMBs range widely in the Northeast. Farms with a high balance often have opportunities to save money and reduce potential losses to the environment. Other farms may be mining soil nutrients (farms with negative balances) and need to import more nutrients to sustain productivity in the long term. Knowing the NMB status of your farm can tell you if there is too much, not enough, or about the right amount of nutrients in your farm’s cycle.

When a milk truck pulls out of the driveway, nutrients are being exported off the farm. The same holds if the farm exports crops or animals. Each animal, bushel or ton of crop, or gallon of milk contains nitrogen, phosphorus and potassium. No animal is able to extract 100% of the nutrients consumed in its feed. A crop cannot capture all the nutrients used to fertilize it either. As a result, a farm cannot remain productive in the long run without importing more nutrients than it exports. Knowing what balance to strive for and what areas to look at more closely can help you improve or maintain sustainability at your farm.

For the last 10 years, we have worked with many New York dairy farmers and their advisors to better understand what real farms in New York can achieve without giving up on milk production. First, let’s look more closely at what a whole farm nutrient mass balance is.

Whole Farm Nutrient Mass Balance

Figure 1: Whole farm nutrient mass balance assessments include accounting for nutrients brought onto the farm as feed, fertilizer, animals and/or bedding/manure, and nutrients exported off the farm as milk, animals, crops, and/or manure.

Figure 1: Whole farm nutrient mass balance assessments include accounting for nutrients brought onto the farm as feed, fertilizer, animals and/or bedding/manure, and nutrients exported off the farm as milk, animals, crops, and/or manure.

A whole farm nutrient mass balance is the difference in nitrogen (N), phosphorus (P), and potassium (K) imported onto the farm in the form of feed, fertilizer, animals, and bedding, and nutrient exported off the farm in milk, crops, animals and manure. The difference between nutrients imported and nutrients exported can be expressed as N, P and K balance per acre of cropland, and per unit (cwt or hundred weight) of milk produced (Fig. 1). An NMB summary simply draws a boundary around the farm and accounts for only those nutrients being imported across the farm boundary and those exported off the farm. A whole farm nutrient mass balance calculator was first developed by Stuart Klausner at Cornell University about 20 years ago and has been updated several times since. Ketterings Table 1This calculator is targeted foruse by dairy farms, though NMBs can be determined for any type of agricultural operation. We have a questionnaire available to help gather the data. Briefly, these inputs are listed in Table 1.

Where Should Your Whole Farm Balance Be?

A sustainable nutrient mass balance should allow dairy farms to be economically profitable, environmentally sustainable, and flexible enough to allow for the many variations among farms. We defined “feasible” nutrient mass balances per acre and per cwt of milk produced as shown in Table 2, based on our work with a set of 102 New York dairy farms. Combining both balances (per acre and per cwt), the most efficient farms have balances in the green area (the “Optimum Operational Zone” or “Green Box”) in Figure 2. Ketterings Table 2This example in Figure 2 is for nitrogen. Evaluations so far have shown that farms operating outside of the green box have opportunities for improvements in nutrient use.

Figure 2: Feasible balances (optimal operational zone) for nitrogen based on 102 dairy farms in New York. The farms in the green box are in the optimal operational zone with relatively high nutrient use efficiency and low risk of loss of nutrients to the environment.

Figure 2: Feasible balances (optimal operational zone) for nitrogen based on 102 dairy farms in New York. The farms in the green box are in the optimal operational zone with relatively high nutrient use efficiency and low risk of loss of nutrients to the environment.

Farmer Feedback

Farms that complete the assessment for at least four years have shown great improvements over time without giving up on milk production. A number of farms have reduced balances while increasing milk production due to more precise feeding.  We don’t have farm financial records, but the fact that balances were improved without giving up on milk production strongly suggests that the farms gained by keeping track of their mass balances.  Each farm is unique so management practices that allow a farm to become more efficient with nutrients were also farm specific.  Some common themes for review are careful evaluation of cow feeding programs, change of feed imports where possible, focus on increasing homegrown forage production through better crop and pasture management and better allocation of fertilizers to fields to support the crops.  some farmers made crop rotation changes, while others decided to increase acreage, or export more crops and/or manure.

Farmers can do the assessment themselves by using the tool on our website and generate a report once all data are entered. The farmers that share their data with us or submit their completed input sheets to us receive a comprehensive report that includes a comparison of their farm data with the data of all other farms in the database (all farm names are kept confidential). Farmers can learn from each other by comparing their operations and by discussing the results in their own management team. Here is some feedback from participating farmers about the benefit of doing the balance and sharing results:

“With enough farms in the database that are similar in size and cropping program to ours, we can make valid comparisons. It helps us to see what can be achieved and gives us a good sense of where we stand in our goal to be as nutrient efficient as is possible.”

 “Immediately it makes you think of things in a different light or from a different perspective, than we normally look at things in either dollars and cents or feed pounds or feed pounds wasted or what we are feeding the cows it steps back one step further and makes you look at the big picture.”

 “When we share the information with the whole farm team it sparks useful and important conversations about our farm’s philosophy and practical applications to concrete practices such as manure application, crop sales and purchases.”

 Join Us!

Don’t hesitate, join us and let’s learn together. The software and supporting information (manual, input sheets, etc.) are freely downloadable from the whole farm nutrient mass balance project page of the Cornell Nutrient Management Spear Program (NMSP) website: 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 IBM computers and is currently not available for Macs.

Acknowledgments

NMSP ackThis work was supported by grants from the Northern New York Agricultural Development Program (NNYADP), Northeast Sustainable Agriculture Research and Extension (NESARE), Federal-Formula Funds, and a USDA-NRCS Conservation Innovation Grant. For questions feel free to contact Quirine M. Ketterings at qmk2@cornell.edu.

April 20, 2016
by Cornell Field Crops
Comments Off on Whole Farm Corn and Hay Yield Variability; a Dairy Farm Case Study

Whole Farm Corn and Hay Yield Variability; a Dairy Farm Case Study

Emmaline Long, Quirine Ketterings, Meghan Hauser, Willard DeGolyer
Nutrient Management Spear Program, Department of Animal Science, Cornell University, Ithaca, New York

Access to accurate yield records is essential if we want to identify limitations to crop production on individual farms, fields, or portions of fields, and to improve field and farm productivity over time. We also need to know yields to evaluate where investment of additional resources (labor, nutrients, seed, lime, tile, etc.) will result in an increase in yield.

Until the introduction of forage yield monitors, the only accurate way to determine whole-farm crop yields was with the use of farm scales combined with estimations of forage moisture obtained using microwave ovens or Koster testers. Portable axel truck scales can be used as well, but use of such scales (1) introduces greater error in yield estimates as typically not all axels can be weight simultaneously, and (2) slows down the harvest process. Driving trucks over permanent farm scales located close to the bunks causes less of a delay but still impacts the harvest process somewhat. Thus, few farms have long-term yield records. One exception is Table Rock Farm in Western New York where all truck-loads of all corn and hay fields have been weighed and recorded over the past fourteen years. Here we analyzed the yield data from this farm to: (1) determine the temporal variability of forage yields (corn silage, alfalfa/grass mixtures, and overall dry matter (DM) production); (2) assess yield and yield stability over time across all fields with at least two crop rotations; (3) evaluate soil physical and chemical properties as potential indicators of yield and yield stability over time; and (4) develop a method to analyze yield data.

Yield Data and Analyses

Yield was measured between 2000 and 2013. Spatial (field to field) and temporal (same field over years) variability was determined using 107 fields of which 61 had yield data for six corn years each and 71 fields had five full production years for alfalfa/grass mixtures. The average yield and coefficient of variation (CV for means over time) were calculated for each field. The fields were divided into four groups called quadrants (Q1-Q4), using the overall weighted mean yield and mean CV as cutoffs for the quadrants: (1) above mean yield, below mean CV (Q1); (2) above mean yield, above mean CV (Q2); (3) below mean yield, above mean CV (Q3); and (4) below mean yield, below mean CV (Q4). This methodology allowed us to identify fields that are consistently high yielding versus fields that sometimes yield high, sometimes low, or are consistently low in yield. The consistently high yielding fields are the fields with the greatest biological buffering capacity, able to produce also under challenging weather conditions.

Findings

Corn yields increased over time from 5.9 tons/acre dry matter (DM) in 2000 to 7.9 tons/acre in 2013 (from 16.9 to 22.6 tons/acre at 35% DM). The yield of alfalfa/grass mixtures did not increase, averaging 3.8 tons/acre DM (4.5 tons/acre at 85% DM). In 2013, the average yields for corn silage and alfalfa/grass mixtures on the case study farm were 37% and 22% higher than the state average that year. Growing degree days since planting and whole-season (March through October) rainfall were not correlated with yield of either corn or alfalfa/grass mixtures. Corn silage yield was impacted by rainfall during March and April, and during July and August. An increase in rainfall during March and April, just prior to corn planting, caused a decrease in overall yield. In contrast, an increase in rainfall during July and August, a time period in which tasseling occurs, was correlated with an increase in overall yield (Fig. 1). The yield of alfalfa/grass mixtures was not correlated with rainfall during individual months (data not shown), but increased with total rainfall in July and August (Fig. 1).

Figure 1. Yield trends of corn, alfalfa/grass mixtures and total dry matter production on a western New York farm from 2000 to 2013 as impacted by rainfall during March-April and July-August. Corn silage yield increased during the time period. Yield of alfalfa/grass mixtures remained constant. Total dry matter production increased over time, reflecting trends in corn silage yield. Corn yield was impacted by rainfall during planting and tasseling. Alfalfa/grass yield was impacted by rainfall during July-August. Total dry matter was impacted by both March-April rainfall and July-August rainfall. Adapted from Long and Ketterings (2016).

Figure 1. Yield trends of corn, alfalfa/grass mixtures and total dry matter production on a western New York farm from 2000 to 2013 as impacted by rainfall during March-April and July-August. Corn silage yield increased during the time period. Yield of alfalfa/grass mixtures remained constant. Total dry matter production increased over time, reflecting trends in corn silage yield. Corn yield was impacted by rainfall during planting and tasseling. Alfalfa/grass yield was impacted by rainfall during July-August. Total dry matter was impacted by both March-April rainfall and July-August rainfall. Adapted from Long and Ketterings (2016).

Figure 2. Average yield of corn silage (a) and alfalfa/grass mixtures (b) and coefficient of variation for each field. Dotted lines represent the overall average yield and coefficient of variation. Quadrants are labelled 1-4 and identify those fields which are high or low yielding, and exhibit high or low variability. Adapted from Long and Ketterings (2016).

Figure 2. Average yield of corn silage (a) and alfalfa/grass mixtures (b) and coefficient of variation for each field. Dotted lines represent the overall average yield and coefficient of variation. Quadrants are labelled 1-4 and identify those fields which are high or low yielding, and exhibit high or low variability. Adapted from Long and Ketterings (2016).

Corn silage average yield across fields and years was 7.0 tons/acre dry matter, with a mean CV of 16.4% (Fig. 2). In contrast, the overall yield for alfalfa/grass mixtures was 4.4 tons/acre dry matter, with a mean CV of 21.6% (Fig. 2). For corn and alfalfa-grass mixtures yielding above the farm average, there was a 74% and 86% probability of a CV below the farm average, respectively, indicating that high yielding fields tend to be more consistent in yield over time than low yielding fields.

The fields in Q1 and Q2 had a higher percentage of well-drained soils, versus primarily moderately and somewhat well-drained soils for Q3 and Q4. These results suggest that drainage of the soil and field yield and stability are correlated; higher yields are expected in better drained soils. It is important to keep in mind that these results are based on the predominant soil type in the field, and may not be the only driving force behind the overall performance. It is therefore important to also consider chemical properties when quantifying spatial variability.

Organic matter for consistently high yielding fields averaged 2.9 and 3.2% for corn silage and alfalfa/grass mixtures, respectively, versus 2.7 and 2.8% OM for low and variable yielding fields. Fields in alfalfa/grass mixtures with a lower than average CV had significantly higher OM levels suggesting a positive link between OM and yield and yield stability over time.

Figure 3. Yield of corn silage and alfalfa-grass mixtures on a western New York dairy farm, as impacted by Morgan extractable soil test phosphorus levels. As soil test phosphorus increases, the yield increased until approximately 32 lbs P/acre for corn silage and 29 lbs P/acre for alfalfa/grass. Adapted from Long and Ketterings (2016).

Figure 3. Yield of corn silage and alfalfa-grass mixtures on a western New York dairy farm, as impacted by Morgan extractable soil test phosphorus levels. As soil test phosphorus increases, the yield increased until approximately 32 lbs P/acre for corn silage and 29 lbs P/acre for alfalfa/grass. Adapted from Long and Ketterings (2016).

Consistently high yielding fields averaged 36 and 40 lbs P/acre on the Cornell Morgan soil test for corn silage and alfalfa/grass mixtures, respectively, versus 18 lbs P/acre for low yielding and more variable fields. Corn silage fields with a below average CV (less variable over time) had higher mean soil test P than those with a higher than average CV. High yielding fields with alfalfa/grass mixtures had higher soil test P than low yielding fields. However, across all fields for both crops, yield increased as Cornell Morgan soil test P increased up to 32 lbs P/acre for corn silage, and 29 lbs P/acre for alfalfa/grass (Fig. 3); there was no relation between yield and soil test P at soil test levels that were higher than these values reflecting past manure applications and indicating it is not the P in the manure that is linked with the high yielding fields but more likely the benefits of organic matter addition and stimulation of microbial activity with the addition of manure.

Summary and Conclusions

Corn silage yields increased from 2002-2013, while yields of alfalfa-grass mixtures remained constant over time. Yield varied both temporally with rainfall throughout the growing season and spatially among fields. The consistently high yielding corn fields exceeded 7.0 tons/acre dry matter with a CV less than 16.4%. Fields in alfalfa-grass mixtures that were consistently high yielding exceeded 5.5 tons/acre with a CV less than 21.6%. The highest and most consistently yielding fields had better-drained soils, optimum or higher in soil test P, and higher OM levels than the lower yielding and more variable fields. These results could suggest that farmer practices that improve soil drainage (tile drainage), conserve or even increase organic matter (reduced tillage and cover crops), and enhance soil test P (manure application) to optimal (not excessive) levels, might be effective in increasing the overall corn silage yield and yield stability. Similar assessments can be done (and much faster) when analyzing forage harvester yield maps. Such work is ongoing in the Nutrient Management Spear Program.

Acknowledgments

Variable Rate ACKFunding was provided by a USDA-Conservation Innovation Grant and a NESARE grant. For questions about these results contact Quirine M. Ketterings at 607-255-3061 or qmk2@cornell.edu, and/or visit the Cornell Nutrient Management Spear Program website at: http://nmsp.cals.cornell.edu/. For more details on the study, see our article in Agronomy for Sustainable Development DOI 10.1007/s13593-016-0349-y (Long and Ketterings, 2016).

 

April 5, 2016
by Cornell Field Crops
Comments Off on What’s Cropping Up? Volume 26 Issue No. 2 – March/April 2016

What’s Cropping Up? Volume 26 Issue No. 2 – March/April 2016

The full version of What’s Cropping Up? Volume 26 No. 2 is available as a downloadable PDF and on issuu.  Individual articles are available below:

April 5, 2016
by Cornell Field Crops
Comments Off on Water Quality Impacts Reduced with Adapt-N Recommendations

Water Quality Impacts Reduced with Adapt-N Recommendations

Aaron Ristow1, Shai Sela1, Mike Davis2, Lindsay Fennell1, Harold van Es1
1
Soil and Crop Sciences Section, School of Integrative Plant Science, and 2Cornell University Agricultural Experiment Station

Cornell University

Soil nitrogen (N) is both spatially and temporally variable, challenging farmers to meet optimal nitrogen (N) needs and minimize N deficiency risk. N typically is a large monetary input for corn production in part due to farmer tendency to over-apply N fertilizer and/or manure to maximize their returns to N applications in the presence of high uncertainty around the optimum N rate. This excessive N maybe be readily lost to the environment through volatilization, runoff and leaching. Not only do N losses negatively impact yield, we know a significant percentage of total N load is carried by ground water or discharged to streams, causing environmental costs. Therefore, a top priority should be the estimation of the optimum N rate that meets crop production needs while minimizing environmental impacts.

The optimum N rate depends on numerous factors including the timing and amounts of early season precipitation events, previous organic and inorganic N applications, soil organic matter, carry-over N from previous cropping seasons, soil texture, rotations, etc. There are several approaches to optimizing N rates and minimize N losses. These can be generally categorized as (i) static and (ii) adaptive. Static tools offer generalized recommendations that do not consider seasonal conditions of weather and soil/crop management, while adaptive approaches account for the variable and site-specific nature of soil N dynamics, including the effects of weather. Using data from two seasons of corn silage grown at the Cornell University research farm at Willsboro, NY, we compared the economic and environmental impacts of N rate recommendations from a conventional static approach (the Cornell Corn Nitrogen Calculator; CNC) with the adaptive Adapt-N approach (adapt-n.com).

Adapt-N and the Cornell Corn Nitrogen Calculator

The Cornell University Corn Nitrogen Calculator (CNC) is a static approach that includes a basic mass balance calculation of N demand (yield-driven crop uptake) and N supply (soil organic matter, manure, previous crops), combined with efficiency factors. The CNC approach has been the established corn N recommendation approach for several decades, and estimates can be derived from a spreadsheet downloaded from http://nmsp.cals.cornell.edu/software/calculators.html.

Adapt-N is a dynamic simulation tool that combines soil, crop and management information with weather data to estimate optimum N application rates for corn. Originally developed at Cornell University, the tool has been licensed for commercial use and is currently calibrated for use on about 95% of the US corn production area. When using the tool to inform in-season N application rates, early season weather effects and site-specific attainable yield can be incorporated into the recommendation, allowing N management precision to be improved.

The Adapt-N tool was compared to CNC recommendations in a spatially-balanced complete block design (4 replications) on two paired experimental sites for the 2014 and 2015 growing seasons. In each trial, the treatments were defined by the total amount of N applied, where the rates were:

(i)     the total N rate based on Adapt-N recommendations (including a 15 lbs/ac starter) for the date of sidedress, and

(ii)    the total recommended rate of the Cornell Corn Nitrogen Calculator (including a 15 lbs/ac starter), using realistic yield goals (rather than the database yield goals, which would have underestimated real yields for these sites).

The treatments were implemented on 16 plots, each on a Cosad loamy fine sand and a Muskellunge clay loam, in continuous corn (silage), under no-till and plow-till management. Drainage water samples were collected from the lysimeters at key time points in the spring (April 7th and April 23rd) and fall (October 1st, October 29th, and December 3rd). The lysimeters include drainage lines routed to a utility hole to allow for drain water samples to be collected. Nitrate (NO3) and Nitrite (NO2) concentration was quantified from the samples to allow us to assess differences in water quality in Adapt-N vs CNC plots. In this article, we will refer to NO3+NO2 concentrations simply as NO3 or “nitrate”, as the NO2 fraction is typically very small.

At the end of the 2014 and 2015 seasons, we measured corn yields and calculated associated partial profit differences for the two treatments. Corn yields were assessed by representative sampling (four 15 ft long row sections per plot). Partial profit differences between the Adapt-N and CNC practices were estimated using prices of $0.50/lb N and $50/T silage.

Results

Yield and Profit: The measured agronomic and leaching losses of the two recommendation approaches are presented in Table 1. Adapt-N recommended N rates were substantially lower than the CNC rates with an average reduction of 55 lbs/ac (183 vs 126 lbs/ac), while the average yields did not differ significantly (13.0 vs 13.1 T/ac; p=0.74). Reducing N rates without compromising yields resulted in $34/ac higher partial profit from the Adapt-N treatment. The economic and agronomic benefits of Adapt-N are similar to those from a larger study conducted in IA and NY using data from 113 on-farm trials (Sela et al., 2016).

Screen Shot 2016-04-05 at 11.26.41 AM

Lysimeter measured nitrate concentrations: In addition to the economic benefits, substantial environmental advantages were found with Adapt-N. When both seasons and soil textures were combined, the average NO3 concentration from the grab samples collected from the lysimeters indicated significantly lower water quality impacts under Adapt-N management vs CNC (11.0 and 15.3 mg/L, respectively; p<0.01). On average there was a 28% reduction in NO3 concentration from the Adapt-N treatments. When analyzing the clay loam and loamy sand plots separately but still combining the two seasons, NO3 concentration was significantly higher in the CNC loamy sand treatments (20.1 vs 13.7 for Adapt-N; p<0.01) and they trended toward higher concentrations in the clay loam treatments (10.0 vs 8.0 for Adapt-N; p=0.09).

Figure 1. Total Applied N recommended from two tools (Adapt-N and CNC) compared with measured NO3 leaching concentrations over two seasons from two soil textures. In general the Adapt-N recommended lower N applications resulted in lower average NO3 concentrations, and the loamy sand showed greater leaching losses with increasing N rates than the clay loam.

Figure 1. Total Applied N recommended from two tools (Adapt-N and CNC) compared with measured NO3 leaching concentrations over two seasons from two soil textures. In general the Adapt-N recommended lower N applications resulted in lower average NO3 concentrations, and the loamy sand showed greater leaching losses with increasing N rates than the clay loam.

Figure 1 shows nitrate concentrations for each drain water sample. Generally, there was a large range of losses throughout the year, but they trended up with more applied N. As could be expected, we saw that the loamy sand plots had higher losses, regardless of treatment, due to the lower water holding capacity of the coarse textured soil. Similarly, NO3 concentrations from the clay loam plots were less responsive to the amount of applied N compared to the sandy plots, but there were still substantial losses, especially at the higher rates. We conclude that the lower applied N rate in the Adapt-N treatments resulted in an overall lower concentration of NO3 in leachate from the lysimeters.

Conclusions

This study proves both economic and environmental gains from using Adapt-N’s adaptive approach to estimating in-season N rates across two distinct soil types in Northern New York. In all, the Adapt-N recommended rates were lower than the CNC rates but maintained the same yield and showed greater profits. Overall, the use of Adapt-N can significantly contribute to nitrogen reduction goals by reducing overall inputs, minimizing environmental losses, and improving farmer profits.

Acknowledgements

This work was supported by funding from the USDA-NRCS, New York Farm Viability Institute, USDA-NIFA, and USDA-Sustainable Agriculture Research and Extension, and the Northern New York Agricultural Development Program.

References

L. Fennell, S. Sela, A. Ristow, H. van Es, S. Gomes. 2015. Comparing Static and Adaptive N Rate Tools for Corn Production. What’s Cropping Up? 25:5

L. Fennell, S. Sela, A. Ristow, B. Moebius-Clune, D. Moebius-Clune, B. Schindelbeck, H. van Es, S. Gomes. 2015. Adapt-N Recommendations Reduce Environmental Losses. What’s Cropping Up? 25:5

Sogbedji, J.M., H.M. van Es, J.J. Melkonian, and R.R. Schindelbeck. 2006. Evaluation of the PNM Model for Simulating Drain Flow Nitrate-N Concentration Under Manure-Fertilized Maize. Plant Soil 282(1-2): 343–360

Sela. S, H.M. van Es, B.N. Moebius-Clune, R. Marjerison, J.J. Melkonian, D. Moebius-Clune, R. Schindelbeck, and S. Gomes. 2015. Adapt-N Outperforms Grower-Selected Nitrogen Rates in Northeast and Midwest USA Strip Trials. Agronomy Journal (accepted for publ.)

 

April 5, 2016
by Cornell Field Crops
Comments Off on No-Till Organic Wheat Continues to Have Low Weed Densities in Early Spring (March 31) at the Tillering Stage (GS 2-3)

No-Till Organic Wheat Continues to Have Low Weed Densities in Early Spring (March 31) at the Tillering Stage (GS 2-3)

Bill Cox1 and Eric Sandsted1
1Soil and Crop Sciences Section – School of Integrated Plant Science, Cornell University

Photo: Organic wheat on 3/31 at the tiller formation stage (GS 2-3). Recommended management (1.2M seeds/acre and a single spring N application) has the right orange stake and high input management (1.6M seeds/acre plus a fall and spring application of N) has the left orange stake.

Photo: Organic wheat on 3/31 at the tiller formation stage (GS 2-3). Recommended management (1.2M seeds/acre and a single spring N application) has the right orange stake and high input management (1.6M seeds/acre plus a fall and spring application of N) has the left orange stake.

We initiated a 3-year study at the Aurora Research Farm in 2015 to compare the corn, soybean, and wheat/red clover rotation in different sequences under conventional and organic cropping systems during the 3-year transition period (2015-2017) to an organic cropping system. This article will discuss weed densities determined in early spring (March 31, about 2 weeks after green-up) at the tillering stage (GS 2-3 stage) in conventional and organic wheat, the second year crop if soybean is the first year transition crop (soybean-wheat/red clover-corn rotation), under high and recommended management inputs.

We provided the management inputs for wheat in both cropping systems under high and recommended input treatments (http://blogs.cornell.edu/whatscroppingup/2015/11/23/wheat-emergence-early-plant-populations-and-weed-densities-following-soybeans-in-conventional-and-organic-cropping-systems/), but we will briefly review them. We used a John Deere 1590 No-Till Grain Drill to plant a treated (insecticide/fungicide seed treatment) Pioneer soft red wheat variety, 25R46, in the conventional cropping system; and an untreated 25R46, in the organic cropping system on 9/24 at two seeding rates, ~1.2 million seeds/acre (recommended management treatment for a September planting date) and ~1.6 million seeds/acre (high input treatment). The wheat was no-tilled in both cropping systems because of the paucity of visible weeds after soybean harvest (9/23). We also applied Harmony Extra (~0.75 oz/acre on 11/5) to the high input conventional treatment at the tiller initiation stage (GS 2 ) for control of winter annuals (chickweed, henbit, and common mallow) and winter perennials (dandelion).

11/03/15) in conventional and organic wheat, averaged across the three previous crops in 2014 and two management treatments (high input and recommended management) at the Aurora Research Farm. Error bars represent standard errors of the means.

11/03/15) in conventional and organic wheat, averaged across the three previous crops in 2014 and two management treatments (high input and recommended management) at the Aurora Research Farm. Error bars represent standard errors of the means.

We previously reported that we walked along the entire wheat plot (~100 feet X 10 feet) to count all the weeds on 11/03 at tiller initiation, prior to the Harmony Extra application to the high input conventional wheat plots (http://blogs.cornell.edu/whatscroppingup/2015/11/23/wheat-emergence-early-plant-populations-and-weed-densities-following-soybeans-in-conventional-and-organic-cropping-systems/). We were very surprised that there were fewer weeds in no-till organic wheat (0.02 weeds/m2) compared with no-till conventional wheat (0.12 weeds/m2, Fig.1). Management inputs did not affect fall weed counts. Most of the weeds were dandelion with some common mallow and chickweed also observed. We speculated that the last cultivation of soybean on July 16 removed existing or late-emerging dandelions or mallow, whereas the observed weeds in the conventional wheat may have emerged after the June 26 Roundup application to conventional soybean.

Early spring weed densities were taken on 03/31, about 2 weeks after green-up, again by counting all the weeds along the entire length of the plots. As in the fall, we found very few weeds in no-till organic wheat, 0.04 weeds/m2 in recommended and 0.05 weeds/m2 in the high-input management treatments (Fig.2). Clearly, seeding rates (1.2M seeds/acre in recommended and 1.6M seeds/acre in high input management) did not affect early spring weed densities in no-till organic wheat.

Fig.2. Early spring weed densities at the tillering stage (GS 2-3, 03/31/16) in conventional and organic wheat under high input and recommended management treatments at the Aurora Research Farm. Error bars represent standard errors of the means.

Fig.2. Early spring weed densities at the tillering stage (GS 2-3, 03/31/16) in conventional and organic wheat under high input and recommended management treatments at the Aurora Research Farm. Error bars represent standard errors of the means.

Weed densities were virtually non-existent in the high input conventional management treatment (0.01 weeds/m2), attesting to the efficacy of a timely fall application of Harmony Extra (Fig.2). In contrast, 0.46 weeds/m2 were counted in the recommended management treatment. Most of the weeds were winter annuals, including henbit, chick weed, and common mallow. We are not sure if 0.46 weeds/m2 are of sufficient magnitude to reduce yields. Nevertheless, we will not apply an herbicide to conventional wheat under recommended management because we inter-seeded red clover in early March, which reduces herbicide options.

In conclusion, no-till organic wheat and conventional wheat (with a fall Harmony Extra application) had very low weed densities at the end of March, 2 weeks after green-up. Organic wheat in the recommended and high input management treatments had similar weed densities, indicating that recommended seeding and N rates were just as effective as high seeding and N rates for weed control at the GS 2-3 stage. Conventional wheat, which did not receive an herbicide application, had 10 times the number of early-spring weeds compared with organic wheat. We are not sure why there were greater weed densities in conventional wheat, which did not receive a fall herbicide application, compared with organic wheat. Again, perhaps a July 16 cultivation in organic soybean compared with a June 26 Roundup application in conventional soybean may have influenced weed densities in the subsequent wheat crop. On April 1, organic wheat looked as good, if not better, than conventional wheat (picture). It remains to be seen, however, if Kreher’s composted chicken manure, the N source for organic wheat (60 lbs/acre of actual N pre-plant +75 lbs/acre of actual N on 3/17 in high input and the single 75 lbs/acre of actual N as a spring application in recommended management) can provide enough available N for maximum yield in organic wheat.

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