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osunpk

osunpk

Since 2008 I have served as the Precision Nutrient Management Extension Specialist for Oklahoma State University. I work in Wheat, Corn, Sorghum, Cotton, Soybean, Canola, Sweet Sorghum, Sesame, Pasture/Hay. My work focuses on providing information and tools to producers that will lead to improved nutrient management practices and increased profitability of Oklahoma production agriculture

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Nitrogen rate and timing for a forage wheat crop. Year 1 Results.

Written by
Mr. Bronc Finch, PhD. Student, Precision Nutrient Management. 
Dr. Brian Arnall, Precision Nutrient Management Extension Specialist. 
In cooperation with Dr. James Rogers, Noble Research Institute. 

With the amount of wheat acreage in Oklahoma being utilized for grazing cattle, and much of that land grazed completely instead of harvested for grain, many questions have arose regarding the management of grazed cropland. A major question in the management of a graze-out wheat crop pertains to fertilizer management strategies. A study developed in co-operation with the Noble Research Institute is attempting to answer these questions among others. In 2019 the trial was established at three locations: near Lake Carl Blackwell in Stillwater, OSU South Central Research Station in Chickasha, and Noble Research Dupy farm in Gene Aurty, Oklahoma. Each of these three sites were setup with three nitrogen (N) treatments in Gallagher winter wheat, with 2 pre-plant applications of 60 and 120 pounds per acre, and a 60 pound pre-plant and 60 pound top-dress application. Grazing simulation harvests were taken at two times with the top-dress N being applied after regrowth was noticed following the winter season. The Dupy location was planted late and therefore only had a single harvest at the end of the season. Rising plate meter measurement were collected at feekes 7.5 and represented in the graphs below as Mid-season. The Chickasha location revealed unexpectedly high residual soil N levels, which resulted in no differences in dry matter biomass for the first harvest, which was delayed until early march due to excessive rains. The second harvest at Chickasha did show treatment differences with a 0.4 ton difference between the 60 and 120 lbs preplant N rates and increase of 0.8 ton increase over the 120 lb pre-plant when the additional 60 lbs of N was delayed. LCB had a timely first harvest in December resulting in the 120 lb N application outperforming the 60lb N applications by ≥0.33 tons. The second harvest further showed how the split application of N proves beneficial for biomass production. As the split application increased yields by 1.7 and 2.6 tons over the 120 lb and 60 lb preplant applications, respectively. The Dupy location revealed no significant difference in dry matter biomass yield between N treatments at the time of the rising plate meter measurements or for the final cutting.

Figure 1. Dry matter harvest results for each of the harvest dates from the graze out wheat trials from the Chickasha, Lake Carl Blackwell, and Dupy locations for three fertilizer treatments. 60: 60 lbs of nitrogen applied preplant, 120: 120 lbs of nitrogen applied preplant, 60/60: Split application 60 lbs of nitrogen preplant and 60 lbs applied top-dress. Dupy only had one harvest date, the Mid-season yield is estimated via rise-plate measurements taken at Feekes 7.5.

 

The Chickasha and Lake Carl Blackwell (LCB) locations produced an increase in total yield with both the increase of applied N and the split application of N. The 60 lb increase in applied N at preplant, 60 lbs vs 120 lbs, produced a 0.7 and 1.2 ton increase in total dry matter harvested at Chickasha and LCB, respectively. As expected an increase in N increased the yield of wheat biomass for grazing production. The top-dress application, which was made as a late season post Feekes 6 (hollow stem), produced more biomass for graze-out wheat production. The split application of 60 lbs of N preplant and 60 lbs of N top-dress increased dry matter by .8 and 1.3 tons over 120 lbs applied preplant at Chickasha and LCB, respectively. Chickasha yielded higher biomass production than the LCB location due to increased residual N.

Figure 2. Total dry matter harvest results for the graze out wheat trials from the Chickasha, Lake Carl Blackwell (LCB), and Dupy locations for three fertilizer treatments. 60: 60 lbs of nitrogen applied preplant, 120: 120 lbs of nitrogen applied preplant, 60/60: Split application 60 lbs of nitrogen preplant and 60 lbs applied top-dress.

For the following discussion remember that protein is determined by N concentration, so that a increase in N uptake is the same as an increase in protein. Evaluation of the N uptake (% N in the biomass x amount of biomass harvested) over the season revealed treatment effects at all locations, which was not seen from biomass yield. Chickasha and LCB revealed a 20% or greater increase in N uptake with the 120 lb application over the 60 lb application of N at pre-plant. The late season top-dress application yielded a 3, 27, and 27 percent increase in uptake for Chickasha, LCB, and Dupy locations, respectively, over the 120 lb pre-plant application. Although, these results are expected from these results, there are a few things we did not expect. The 120 lb N application did not increase the N uptake above that of the 60 lb application. However, the split application of N resulted in an additional >40 lbs uptake, aka increased protein.

Figure 3. Total nitrogen uptake results for the graze out wheat trials from the Chickasha, Lake Carl Blackwell, and Dupy locations for three fertilizer treatments. 60: 60 lbs of nitrogen applied preplant, 120: 120 lbs of nitrogen applied preplant, 60/60: Split application 60 lbs of nitrogen preplant and 60 lbs applied top-dress.

This study also includes summer forages with and without additional fertilizer. The study will be continued for multiple years on the same locations to evaluate the impact of management on production and soil characteristics.  But one surprising note has already been made, in all three locations a greatly delay top-dress still increased N-uptake. In two location it significantly increase yield and protein. This data is falling in line with the grain only data (How late can you wait) showing that an application of N at Feekes 6 (Hollow stem) and even shortly after can provide positive return on investments.

 

For any questions for comments please contact
Brian Arnall
b.arnall@okstate.edu
405-744-1722

 

Watch Forage Nitrate Closely on Certain Crops

Nitrate is one of the major nitrogen (N) forms utilized by plants. Excessive nitrate accumulation can occur when the uptake of nitrate exceeds its utilization in plants for protein synthesis due to factors such as over N fertilization and stressful weather conditions. It can be toxic to livestock when too much nitrate is accumulated in the forage crops. Sorghum and millet have been noted as having a high potential for accumulating nitrate. Producers should watch their forage nitrate closely to avoid cattle fatality and to better manage their hay crop since we have seen many high nitrate forage samples every year. Normally, drought stress, cloudy weather and other climatic conditions will enhance nitrate accumulation in the plant. In addition, forage planted in failed wheat fields with high soil residual nitrogen unused by wheat can result in high forage nitrate problem too.

Figure 1. Summary of our laboratory nitrate test results in the past on two major warm season forage crops.

It is considered potentially toxic for all cattle when nitrate in the forage is greater than 10,000 ppm. Producers should avoid grazing or feeding with high nitrate hays. More detailed interpretation can be found from OSU Extension Fact PSS-2903 Nitrate Toxicity in Livestock. The most reliable way to find out nitrate in the hay is to collect a representative sample and have it tested by a laboratory. OSU Extension Fact PSS-2589 Collecting Forage Samples for Analysis highlights the proper techniques to collect forage samples. Samples can be submitted for nitrate and other forage quality analyses to the Soil, Water and Forage Analytical Laboratory in Stillwater through the local county extension office. We normally have the results ready within 24 hours form the time when sample is received by the lab. However, many samples we receive at the lab were not sampled properly. More attention should be paid on sampling standing forage, such as a haygrazer by following the right procedures:

Clip at least 20 representative plants at grazing or harvesting height from the suspected area. Cut the whole plants (include leaves and heads) into 2-3” long pieces, combine and mix well in a bucket.

Fill the cut sample into a forage bag. Use quartering to reduce the amount if there is too much sample to send to a lab.

Put the forage bag into a plastic bag will give you more accurate moisture content, but never put plastic bags inside our forage bags.

There is also a quick screening test using diphenylamine at your county extension office. This video shows how to properly use the test kit: https://www.youtube.com/watch?v=vArUP6KFQFI&feature=youtu.be

 

Hailin Zhang

Department of Plant and Soil Sciences

Hailin.zhang@okstate.edu

Precision Nutrient Management in Forage Systems

Published in Progressive Forage http://www.progressiveforage.com/ 9.1.2016

First, let’s agree the term “precision” is relative. Forage is a diverse system with an even more diverse set of management strategies. Regardless, every manager should be constantly striving to improve the precision in which nutrients are managed. The ultimate goal of any precision nutrient management tool should be this: producing the highest quality output (in this case forage) with the least amount of input – ultimately, optimizing efficiencies and maximizing profits. Within this readership there are those who are soil sampling at a 1-acre resolution and others who have likely not pulled a soil sample in the past decade. For both spectrums we can make improvements – let’s start basic and move forward.

A soil sample should the basis for all nutrient management decisions. Is soil testing a perfected science? No, far from it. However, there must be a starting point. A soil sample is that first bit of information we can start with and the basic data collection for precision ag to make improved management decisions. When fertilizer is applied without a recent soil sample, it is done based upon pure guesswork. How many other management decisions are made on a farm or ranch by a guess?

The composite soil sample is a great start, but it is just that – a start. While there are some soils that are very uniform most are extremely variable. In a survey of 178 fields in the southern Great Plains on average the soil pH was 6.12; phosphorus (Mehlich 3 phosphorus [M3P] and Bray 1 phosphorus [B1P]) was 28 ppm while soil test potassium averaged 196 ppm. So on the average the primary components of soil fertility were okay. However, on average the 178 fields had a range in soil pH of 1.8 units, M3P and B1P both had range of a 52 ppm and STK had a range of 180 ppm.

Table 1 shows the minimum and maximum soil test values for the 178 fields.

  Average Range Min Max
Soil pH 6.12 1.77 5.23 7.01
Phosphorus 28 52 2 54
Potassium 197 180 107 287
Sulfur 15 24 3 27
Organic matter 1.9 1.2 1.3 2.5

 

This data helps support the concept that we should find ways to increase the resolution or decrease the number of acres represented by a single soil sample. Increasing soil sample resolution is typically done using one or two methods – zone or by grid.

Zone sampling

The basis of a zone sample is creating a smaller field. The biggest question with zones is how to draw the lines. There are dozens, if not hundreds, of possible methods, each having their own reasons and benefits. My basic recommendation is that before lines are drawn goals have to be established. For example, if phosphorus or soil pH management is important, the basis for the lines should be soil based. This could be based on soils map, soil texture, slope and on and on. If the target is improved nitrogen management, then the reason for drawing lines should be yield based. This could be based on yield maps, aerial images, historic knowledge or many soil parameters.

Why does it matter? Two reasons: First, across the broad spectrum of soils and environments two nutrients are hardly spatially correlated, which means the zone that is best at describing phosphorus variability does an extremely poor job describing potassium variability. Second, more theoretically the demand for nutrients are driven by different factors. Phosphorus (a soil immobile nutrient) fertilizer need is driven by the soil P concentration (look up Brays Sufficiency Concept). Many use yield as a parameter for phosphorus application, but this is not a plant need or even a yield maximizing practice. Fertilizing based on removal is done to prevent nutrient mining. However, nitrogen (a nutrient mobile in the soil) fertilizer need is based on yield and crop removal. Hence, the common Land Grant University N and sulfur recommendations are yield goal based.

Grid sampling

To be honest even the experts disagree on the hows, whys and ifs of grid sampling. I like data, therefore I naturally lean towards grid sampling if the field warrants it. For me, the biggest benefit of grid over zone sampling is that soils data from zone samples are biased to whatever parameter was set for the zone and therefore any resulting map for all nutrients must reflect the original zones. In a grid, each data point is independent therefore the maps of each nutrient can be independent, and (the science tells us) in most cases nutrients are independent of each other.

Ideally two pieces of information are available for determining whether a field is grid sampled or not. The first piece of information is a yield map from any previous crop. If yield is fairly uniform, I question the need for variable rate management, much less the expense of grid sampling. Regardless of the sampling method zone or grid, the discussion is moot if spatial variability does not exist across the field. However, many forage producers may not have access to this kind of data.

One of the most useful decision aid tools for grid sampling is the composite soil sample. The reason is simple statistics: A composite sample should be a representative average of the field. If the data is normally distributed, that means half of the field is above and half the field is below the sample average. So the optimum fields to grid are those in which an input falls at the point in which the benefit of applying is in question, because it suggests that approximately half the field needs the inputs while the other half likely does not. It is in this scenario that the return on investment can be greatest. As with pH, for example, fields with a very low value should have a flat broadcast application and should be sampled again at a later date. Fields with a composite pH well above 6.0 will unlikely have enough acres needing lime to warrant sending out an applicator.

Is grid sampling a lifelong activity? No. The initial activity of grid sampling will provide both an indicator of the variability level and overall needs of the field. From that point, decisions can be made and actions taken. Identify the greatest limiting factor in the field based on the samples, and focus on impacting change upon it. Zone sampling in subsequent years can be utilized to document change. When that issue is resolved, move to the next factor. It may require grid sampling again or using the original grid to develop new management zones. For instance, if the greatest issue first identified on the field is soil acidity then after the soil pH is neutralized the field should be grid sampled again. The reason is for this is that changing soil pH will influence many nutrients and the amount of change is not consistent but dependent upon many other factors.

In precision ag we tend to look at layers, yield, soil, etc. However, none of these tell the whole story independently. An area in a field may have moderate soil fertility and be under producing. Using the data collected the decision may be made to increase inputs; yet, the issue is a shallow restrictive layer limiting production. Therefore, the extra inputs will be of no benefit and could even further reduce production. It is at this point I like to bring out the importance of “getting dirty.” There is no technology that can take the place of “boots on the ground” agronomy.

For producers who have historically preformed intensive soil sampling there is still room for improvement. Soil testing and nutrient management is not an exact science; in fact, it was originally built for broad sweeping, statewide recommendations. As technology advances and inputs can be applied at sub-acre resolutions, all of the environment (weather, soil) by genotype inactions becomes more evident.

The next step in precision ag is to develop recommendations by upon site specific crop responses. This is where nutrient response strips can further improve nutrient use efficiencies and crop production. In Oklahoma, nitrogen-rich strips are applied across fields (grain and forage) to determine in-season nitrogen needs. Having a strip in the field with 50 to 100 extra units of N acts as a management tool which takes into account soil, environment and plant need. If the strip is visible the field or zone needs more N, if it is not visible then the crop is not deficient and at that point in the season does not need more N. Producers have taken this approach for N and adopted it for P and K with strips across the field with a zero and high rate of either nutrient. After a few seasons, responsive and non-responsive zones are developed and P and K applications are managed accordingly.

One misconception of precision ag is that the end result should be a field with uniform yield from one corner to the other. This is often not the case; in fact, in many cases the variability in production across the field can be increased. Theoretically, precision ag is applying inputs at the right rate in the right place. This means areas of the field which are yield limited due to underlying factors which cannot be managed have a reduction in inputs with no effect on yield. Other areas of the field have not been managed for maximum production therefore an increase inputs result in increasing yield widening the gap between the low and high yield levels.

Regardless of where a producer currently sets on the technology curve, there are potential ways to increase productivity and efficiency. There is nothing wrong with taking baby steps; it is often the simple things that lead to the greatest return.