Improving Fertilizer Recommendations for Corn - The
Nebraska Soil Fertility Project (NSFP)
A. Dobermann1, J. Blumenthal1,2, R. Ferguson1,3, C. Shapiro1,4, D. Tarkalson1,5, C. Wortmann1,6, D. Walters1
1Dept. of Agronomy and
Horticulture, University of Nebraska-Lincoln
2Panhandle Research and Extension
Center, Scottsbluff
3South-Central Research and
Extension Center, Clay Center
4Northeast Research and Extension
Center, Concord
5West-Central Research and
Extension Center, North Platte
6Southeast Research and Extension
Center, Lincoln
The fertilizer recommendations presently used by the University of Nebraska (UN-L) have not been thoroughly documented. Written documentation is not available because the process involves statistical analysis of research data mixed with the judgment of individuals or by a committee (Hergert et al., 1997). The primary database for the UN-L fertilizer recommendations for corn is about 25 (N) or 40 years (P, K) old. LB 284 passed by the legislature in 1949 established the “Outstate Testing Program” within the Department of Agronomy for fertility and crop variety research. A public soil testing service was initiated in 1949, reaching a maximum of 21,000 samples analyzed annually in the mid 1950’s. Commercial soil testing operations began in the state in the mid to late 1950’s. Since then, the number of commercial soil samples analyzed by the university laboratory (Soil and Plant Analytical Laboratory SPAL) has declined to about 6000 samples per year, whereas private laboratories currently process about 140,000 soil samples collected in Nebraska. In the early 1960’s annual meetings between the university agronomy staff and commercial laboratories were held to share soil fertility research information. However, after a few years these collaborative efforts slowed down and the different laboratories operating in the state developed their own fertilizer recommendations. Collaboration between the soil fertility faculty at UN-L and the private soil testing laboratories became strained due to different opinions about fertilizer recommendation philosophies. Due to the publication of research comparing various fertilizer programs, relationships were further strained (Olson et al. 1982, Olson et al. 1987)
Under the Outstate Testing Program, calibration and correlation research was conducted in Nebraska until 1964 and formed the basis for N, P, K, Zn and other essential plant nutrient guidelines for Nebraska. Limited availability of competitive funds for research on fertilizer recommendations has hampered regular updates of fertilizer recommendations. The only exception is the present N recommendation (third revision, 1995). Research for this was conducted from 1976 to 1982 and made possible by the so-called “energy” funds given to Nebraska because of overcharging by energy companies in the late 1970’s. Fertilizer check-off programs have not been used in Nebraska to support soil testing and plant nutrient management research and education needed for regular updates of fertilizer recommendations.
On May 22, 2001, the Nebraska Legislature approved LB 329, which allocated $300,000 of excess funds accumulated in the Fertilizers and Soil Conditioners Administrative Fund to the University of Nebraska for conducting research on more precise nutrient management in irrigated corn systems. The project described below will use these funds for verifying and possibly updating the fertilizer recommendations for corn.
Currently used soil testing and fertilizer management recommendations are the result of many years of research and field verification conducted in Nebraska and other Midwestern states. However, many recommendations and Best Management Practices (BMP) in use today were developed decades ago. It is reasonable to question whether these recommendations are applicable with precision agriculture technology, particularly at yield levels that exceed those achieved during the original calibration research. We need to validate and update our knowledge for high yielding production systems and for changing production technologies. The sections below summarize some of the key issues to be addressed.
The University of Nebraska’s algorithm for estimating N fertilizer recommendations in corn predicts the amount of N needed for achieving a certain yield goal as a function of soil organic matter (SOM), soil nitrate content in spring, and N credits from previous crop, manure and irrigation (Hergert et al., 1995; Shapiro et al., 2001).
Nrate (lb/A) = 35 + (1.2 EY) – (8 NO3-N) – (0.14 EY x OM) – other N credits
EY = expected yield, 105% of 5-year average (bu/A)
NO3-N = root zone soil residual nitrate-N in 2-4 ft depth (ppm)
OM = soil organic matter (%)
The N algorithm has been validated to generally estimate N needs well in numerous on-farm demonstration studies in Nebraska during the past 15 years, but there are also situations where in over- or under-estimates N need (Ferguson et al., 1991). In the Mid-Nebraska Demonstration Project (1992-1997), the N algorithm under-recommended N 14% (24 of 170 sites) and over-recommended N 52% of the time. In regional studies, soil-test-based N algorithms were shown to have worked well for yield levels up to about 14 Mg ha-1 (220 bu acre-1), but tended to overpredict N rates in years with low response to fertilizer due to unfavorable climate or inadequate soil NO3 testing at sites with recent manure history or where alfalfa was the previous crop (Bundy et al., 1999). Uncertainties include:
a) Current farm yield averages exceed the average maximum yields achieved in the original N response studies. The present N algorithm for corn is based on 81 site-years of N rate experiments conducted on irrigated and rainfed land from 1976 to 1982. Nitrogen rates ranged from 0-280 lb/A (irrigated) or 0-180 lb/A (dryland) in 40 or 20 lb/A increments. However, the majority of sites were located in eastern Nebraska, particularly in the Northeast and the data set included 30 dryland sites with lower yields and different N response than that at irrigated sites. Maximum yields ranged from 55 to 196 bu/A, but averaged 153 bu/A for irrigated and 102 bu/A for dryland corn or 134 bu/A for all data sets together. This compares to average dryland corn yields of 111 bu/A and irrigated corn yields of 157 bu/A achieved in Nebraska during the 1996 to 1999 period. In other words, current farm yield averages exceed the average maximum yields achieved in the original N response studies and many irrigated corn farmers routinely harvest more than 200 bu/A corn, for which we have no adequate calibration.
b) Spatial and temporal variation of indigenous N supply. Nitrogen rates to achieve the maximum yield ranged from 0 to 220 lb/A, but averaged 79 lb/A. This compares to a statewide average N use on corn of 145 lb N/A in recent years (USDA, 2001). Moreover, at 28 sites there was no yield response to N and those sites mostly included dryland (21 sites). Nitrogen rates for maximum yield ranged from only 23 lb/A for dryland corn or 90 lb/A for irrigated corn on fine-textured soils to as high as 153 lb/A for irrigated corn on sandy soils. Check yields ranged from 35 to 168 bu/A (average of 96 bu/A for dryland, 102 bu/A for irrigated sandy soils, 125 bu/A for irrigated fine soils), indicating large spatial and temporal variation in the indigenous N supply. Understanding climatic effects and previous crops on indigenous N supply (Peterson et al., 1990; Yamoah et al., 2000) would be a key for fine-tuning of N recommendations according to major agroecological zones (AEZ) or major differences in cropping practices.
c) Predicting maximum yield potential based on natural resources. The N algorithm assumes a constant internal crop N requirement (bu yield per lb N taken up by the plant) and is very sensitive to specifying the expected yield (yield goal) in advance. The recommendation is that EY should be about 105% of the past 5-yr yield average. Knowing the “true” climatic yield potential (theoretically achievable yield only limited by solar radiation and temperature) and its variation across the state and among years would allow (i) adjusting yield goals according to AEZ, (ii) identifying upper limits of attainable yield for irrigated corn production in each AEZ, (iii) assess yield probabilities for dryland corn, and (iv) adjust crop internal N requirements depending on the yield potential. Season-specific yield goals should not exceed 70-80% of the yield potential because the internal crop nutrient requirements increase as yields approach the yield potential (Dobermann, 2001). This is also the yield level at which financial returns are greatest under most market conditions.
d) Understanding and predicting legume credits. Uncertainty about appropriate legume credits, particularly soybean credit. The N algorithm uses a 45 lb N/A credit for soybean as the previous crop, but other studies suggest that a higher credit of about 65 lb/A or 1 lb/A for each bu/A of previous soybean yield may be more appropriate (Peterson and Varvel, 1989; Varvel, 2000).
e) The N rate calculated cannot account for the temporal variation in crop N demand. The algorithm aims at predicting the optimal N rate for average climatic conditions and some general suggestions for time and method of N application are provided (Shapiro et al., 2001). Studies with corn in Nebraska indicate the importance of early season N management (Varvel et al., 1997), depth distribution of residual soil nitrate (Walters and Goesch, 1999),(Binder et al., 2000) as well as delayed N sidedressings (Bigeriego et al., 1979) for increasing N use efficiency, but the currently recommended a-priori N algorithm does not provide sufficient management criteria for N timing or in-season adjustments. Most methods proposed for in-season N management are “corrective methods” that employ diagnostic tools such as a chlorophyll meter (Peterson et al., 1993; Varvel et al., 1997; Shapiro, 1999), remote sensing (Blackmer et al., 1996), or on-the-go sensors (Lammel et al., 2001) to determine the need for an N topdressing.
Although technology development is proceeding rapidly, these techniques are not yet widely used in Nebraska. At present, this approach relies on empirical comparison with an over- or under-fertilized reference strip to assess whether an additional yield response to N is likely to occur. Moreover, corrective approaches require careful N management at all key growth stages to avoid that N deficiency occurs at critical growth stages. If N deficiency occurs during early vegetative growth of maize, correcting it with late-season N applications is unlikely to fully compensate for the yield loss associated with yield components formed during early growth (Binder et al., 2000). However, if the diagnostic tools used would allow establishing quantitative relationships between reflectance and biomass (Bouman et al., 1992) and between reflectance and nitrogen status (Blackmer et al., 1996), future improvements in interpretation can be made by applying concepts such as critical N dilution curves for a certain yield target (Greenwood et al., 1990) or by using sensed plant N status information as a forcing function in crop simulation models (predictive-corrective N management).
f) Prices are not build into the UN-L N recommendation. What is the best response model to apply for determination of optimum N rate (Sander et al., 1994)? We assume that the N algorithm was based on multi-year studies and should therefore represent an average economic N response at typical grain : N price ratios. Although optimal fertilizer rates tend to be not very sensitive to normal price fluctuations, yield has far more influence on profitability than prices of fertilizer or rates applied. In years with large disturbance of price rations (e.g., in spring 2001) and for high-yielding systems the ability to account for prices of grain and N would improve fertilizer recommendations. Prices may not have been the direct subject of the research below but risk is also an important factor (Helmers et al., 2001).
UN-L recommendations for P and K are based on the sufficiency concept, whereas many private laboratories use buildup-maintenance recommendations or recommendation that include a yield goal. At present, the UN-L recommendations suggest not to apply P above 15 ppm Bray-P and not to apply K above 124 ppm exchangeable K. These recommendations were mainly based on the outstate testing fertilizer experiments conducted in Nebraska in the 1950s and 1960s, and perhaps also inspired by the work done by Bray and colleagues in Illinois (Bray, 1944; Bray, 1945).
The original raw data are not available to us, but treatment means were published in annual Outstate Testing Circulars. The research base for establishing calibrations for the Bray-1 and Olsen-P soil tests was published by R.A. Olson et al. in 1952 (Olson et al., 1954). Supplemental research conducted later refined them in terms of categories used and rate differences according to P placement. Similarly, K recommendations published in 1964 had three levels (<75 ppm – 25-50 lb K2O/A; 75-150 ppm – usually no K recommended, but check strips should be tried in the field; >150 ppm – no K recommended), which were later further divided into a total of 5 categories and two application methods (Hergert et al., 1995).
From 1973 to 1984, these recommendations were compared with buildup-and-maintenance strategies used by many private laboratories operating in Nebraska in a 12-year study at four sites (Mead, Concord, Clay Center, North Platte) and some accompanying studies at other locations. These experiments suggested that (i) the sufficiency concept was more profitable than buildup-maintenance recommendations because yields were similar and less P and K were applied, (ii) the soil test categories used were sufficient to describe the response to P, and (iii) no response to K was found. (Olson et al., 1982; McCallister et al., 1987). These and other studies concluded that deep, temperate region Mollisols common in Nebraska appear to supply significant amounts of K and often also P from native mineral reserves for an indefinite period so that efforts should concentrate on refining sampling, methods for sample analysis and interpretation, and methods for fertilizer placement (Olson et al., 1987). Nutrient balance calculations suggest, however, that, except where manure is applied, many producers in Nebraska have been running negative P and K input-output balances during the past 20 years, increasing the risk potential for marginal deficiencies at increasing yield levels. Uncertainties include:
a)
Shortage of calibration data for specific production
systems. The validity of the existing soil test categories for different
crops, different tillage systems, and high yield levels is unknown. Correlation
and calibration research has not kept pace with changes in cropping systems and
technologies (Hergert et al., 1997). The present P and K
recommendations are mainly based on the fertilizer trials conducted during the
1950’s and 1960’s, with highest yields up to about 170 bu/A. In many studies,
yields were well below 100 bu/A and the data included more dryland than
irrigated corn experiments. More recent research conducted in Iowa gives
support to the present recommendations, but also illustrates that arbitrary
choice of percentage sufficiency levels is a questionable practice because
direct economic analysis cannot be applied to relative yields (Mallarino and Blackmer,
1992). The Following give support to current P recommendations:
In 21 no-till trials conducted in Iowa, phosphorus fertilizer increased no-till
soybean yield (yields ranged from 1.8-4.3 Mg/ha) when soil test P was less than
9 ppm for 0 to 15 cm depth (Borges and Mallarino, 2000). In 26 no-till trials conducted in Iowa, corn yield was
increased in 8 of 11 cases with application of P in starter fertilizer where
soil test P was less than 12 ppm, but in no cases where soil test P was more
than 12 ppm (Bordoli and Mallarino, 1998). Corn yields ranged from 5.4 to 12.9 Mg/ha. The response was similar for 14 kg P ha-1
as for higher rates.
b) Need to include additional soil physical and chemical properties in recommendations. No consideration of different soil types and variation in subsoil P and K in present recommendations. Studies conducted from 1951 to 1956 at 139 locations of the Outstate Testing Program showed that differences in soil fertility status (pH, P, K) of Nebraska soils were closely associated with soil series (Olson et al., 1958). Although it was recognized that subsoil nutrient supply affects crop nutrient uptake under some conditions, this information did not produce P and K recommendations that included subsoil values. Recommendations were not made because there was close correlation of subsoil P and K status with that of topsoil. (Olson et al., 1958). Thirty to 40 years of farming and fertilizer additions have likely changed the topsoil to subsoil ratios of nutrients and also introduced more variability in P and K. The original correlation of the unfarmed soil probably no longer exists and may be management dependent. Similarly, although differentiations of recommended rates were made for band placement of P or K vs. broadcast application, these were rate recommendations and not differentiated by soil properties.
c) Insufficient differentiation by tillage. There has not been a coordinated effort for corn to conduct P or K correlation and calibration research for no-till or ridge-till systems in Nebraska. Research conducted included an experiment on ridge-till and P at North Platte (G. Hergert) and a significant body of research on P placement (Sleight et al., 1984; Raun et al., 1987; Eghball and Sander, 1989a; Eghball and Sander, 1989b; Eghball et al., 1990), but this has not led to differentiated fertilizer recommendations.
d) Conflicting response to K, even on low-testing soils. It is generally believed that illite clays are a major source of K supply in Nebraska soils, including coarse-textured soils (Fawzi and Drew, 1966), which often lead to no response to K fertilizer applications. Studies on potassium did not find any significant yield response to K on sandy soils in northeast Nebraska testing low in exchangeable K (Rehm et al., 1981; Rehm et al., 1983; Rehm and Sorensen, 1985). More recent studies at a similar site provided conflicting results. Yield response to K occurred in one hybrid in 2000, but preliminary 2001 data suggest little effect of the K rate on corn yields (Dobermann and Shapiro, unpublished). However, variability in soil test K was large and marginal P deficiency may have masked the K rate effects. More statistical analysis will be done to clarify this. In these recent studies, however, K affected stalk strength characteristics in certain hybrids, which is an important consideration for reducing harvest losses (Dobermann, 2001). On Mollisols at irrigated and non-irrigated sites testing high in K, there was often a yield depression at the highest K rate (McCallister et al., 1987), but reasons for this are not well understood. In contrast, however, research on very high yields (>250 bu/A) conducted at Lincoln from 1999 to 2001 suggests much larger crop K requirements per unit yield as yields approach the yield potential (Dobermann, 2001).
e) Uncertainty about profit maximization, i.e., does the sufficiency concept optimize yield at the right level? Are critical levels derived from relative yield response curves truly independent from absolute yield levels and therefore yield goals? The economics of fertilizer use are not built into current UN-L or industry corn recommendations in an explicit way, so profit optimization in making a recommendation or assessing long-term P and K management strategies are not possible. For P and K, the sufficiency approach appeared more profitable in the long-term lab comparison studies (Olson et al., 1982), but it includes some risk (reliance on soil testing only). The sufficiency approach has not been evaluated for its long-term implications. The fertilizer recommendation approaches used by private laboratories in the lab comparison studies were not documented. They may have overestimated the true crop nutrient needs for the yield achieved, which obviously made them less profitable (see below).
f) Crop nutrient requirements. Management concepts that include a yield goal require an assumption about the amount of total plant nutrient uptake per unit yield. At present, these are typically single coefficients that were derived from earlier field experiments and they assume linearity between crop yield and nutrient accumulation. However, such constants tend to overestimate the simulated optimal nutrient requirements (Dobermann, 2001). There is also a tendency that data from research experiments conducted on soils with high background levels of P and K may overestimate true crop P and K requirements for a situation of optimal balanced nutrition. Recent analysis of published data suggests that earlier estimates may have been too high and that those numbers also depend on the yield level in relation to the yield potential. For example, in earlier publications the Potash and Phosphate Institute (PPI) cited P removal with grain as 0.44 lb P2O5 per bu yield, a number which was derived from maximum-yield studies. However, review of a large number of published experimental data produced an average of 0.31 lb P2O5/bu for all studies or 0.38 lb P2O5/bu for yields >200 bu/A (Scott Murrell, PPI, unpublished data). For a yield goal of 200 bu/A, these three different estimates would translate into a fertilizer P range of 62 to 88 lb P2O5/A to replenish grain removal. Obviously, which number to choose is not a trivial issue because this greatly affects the recommended P rate in a replenishment of crop removal approach and therefore the economic performance over time.
g) Prediction of soil test changes due to crop removal and fertilizer/organic additions. Buildup-and-maintenance strategies for P and K management require the definition of empirical functions that describe the increase or decrease in soil test P or K as a function of the amount of fertilizer P or K applied and crop removal, as well as the definition of a target soil test level. There is no generally accepted, reproducible method for establishing such functions and it is unclear whether most of the empirical choices currently in use properly account for the economics of fertilizer use.
Interest in sulfur
mainly emerged in the early 1960’s, particularly on sandy soils in northern and
northeast Nebraska. Earlier soil test research found that Ca-phosphate worked
best as a S extractant for Nebraska soils (Fox et al., 1964), but the usefulness of this
soil test has been questioned because no relationships between test results and
the rate of S needed for optimum production has been developed. Soil texture and
organic matter appear to be reliable enough to predict probable response to S,
with little extra gain achieved by the Ca-phosphate extractant (Rehm, 2000). Research on sulfur in
Nebraska was mainly conducted by G. Rehm (Rehm,
1978; Rehm, 1984; Rehm, 1993). There is some controversy about the present S recommendations that
justifies more research, especially for no-till on fine-textured hillside
soils. Some producers seem to apply more sulfur that what UN-L recommends.
UN-L lime recommendations are not based on maximizing profit, but rather
increasing the pH to approximately neutral 6.5. This has the appearance of a
build and maintenance approach. The UN-L soil test results will generate a lime
recommendation at pH 6.2 or lower, while advising that lime use is likely to be
profitable if pH is less than 5.7. This was done because different crops have
different critical pH levels, liming is costly, and soil pH changes slowly.
However, field research has not shown consistent yield response unless surface
soil pH has been below 5.5. Liming recommendations for several important
Nebraska crops were given in Penas (1988) and were probably based on a
review of the literature and expert opinion. In other research, the lime requirement values of 74 acid sandy
soils from northern Nebraska were assessed by eight different laboratory test,
suggesting that the currently used soil tests work well as compared to incubating
soil with CaCO3 (Alabi et al., 1986). However, correlation studies of lime
requirement values for five methods with selected soil properties showed that
no single property was correlated with all methods. While we need additional liming research, we should perhaps
also consider revising our lime recommendations to maximize profit. Because
liming is a long-term investment, land ownership and tenancy issues influence
the liming decision. Other issues that are important to the discussion are the
effect of surface soil pH on herbicide efficacy and the effectiveness of liming
for no-till situations.
Research on starter fertilizer in corn started prior to 1950 and was expanded through field trials conducted from 1951 to 1960 (Langin et al., 1962). Results of these studies were conflicting because both yield increases and yield depressions with starter fertilizer were found. However, no or negative yield response was typically observed at soil test P levels >15 ppm, which was also confirmed in studies conducted in Minnesota (Rehm et al., 1988). Most research on starter fertilizers for row crop production in Nebraska has been for tilled conditions. Soybean yield was increased with band application of P on low P soils but there was no P effect on medium P soil (Sander et al., 1990); the average increase due to knifing P was 0.12 Mg ha-1 over 8 locations. Trials were conducted on farmers’ fields to compare their application of starter fertilizer to no starter (Penas, 1990). The rate, composition, and method of application varied by farm. The average corn response to starter fertilizer when available soil P was less than or equal to 10 ppm was 0.23 Mg ha-1 with yield increases in 60% of the trials; otherwise, the mean gain was 0.13 Mg ha-1 with significant increases in 17% of the trials. Corn yield was increased by an average of 0.62 Mg ha-1 when starter fertilizer was applied to sandy and sandy loam soils, while the mean response was 0.08 Mg ha-1 on fine texture soils. In all cases, the starter fertilizer applied on the sandy and sandy loam soils contained N, P and S. Six of these 52 on-farm trials were under no-till conditions; there was a response in only one case, and S was included in the starter fertilizer in that case.
UN-L
recommendations (Penas and Hergert,
1990)
are based on the assumption that starter fertilizer is a good method to get P
in the ground but it often does not add much benefit when soil tests are above
the critical level. Considering the low probability
of a yield response for tilled situations, research on starter fertilizer
should have less priority at this stage, although we recognize that there is
more evidence for yield response under no-till farming in states such as KS,
SD, IA, and MN. We recommend that this is a good area for fertilizer dealers
and others to conduct their own applied studies.
Both the sufficiency approach and buildup-maintenance strategies for P and K management rely on soil testing. Therefore, current fertilizer recommendations are very sensitive to the frequency and spatial density of sampling as well as the quality of sample analysis. Log-normal soil nutrient distributions may exist within as well as across fields, increasing the likelihood of areas with marginal deficiency, particularly if average soil test values are close to critical limits (Hergert et al., 1997). It is possible to observe a substantial corn yield response to P fertilization when the field avg. Bray-1 P is greater than 15 ppm, because in actuality much of the field is 15 ppm or less, and hot spots from manure or uneven fertilizer application drive the mean higher. Over 170 demonstration sites from the Mid-Nebraska Project, the Bray-1 P mean was 27 ppm, while the median was 22 ppm. This indicates that there were some high numbers bringing up the average. A similar pattern does not exist for K. Over 170 sites, the mean K level was 418 ppm and the median 421 ppm. Other sampling issues relate to depth of sampling and location of samples for different tillage systems, procedures for stratification of sampling, and often unexplainable large fluctuations of soil test values reported from year to year. General guidelines for soil sampling have been developed and are included in the fertilizer recommendations. Of particular concern are changes in sampling depth. With soil sampling becoming more and more mechanized and mostly done by private consultants, we have observed more cases where samples were taken deeper than recommended, leading to nutrient dilution in the sample analyzed and a wrong interpretation with regard to P and K needs. Other important issues mainly refer to spatial sampling techniques such grids sampling or management zones and how to use other information to improve sampling (aerial photos, Veris data, yield maps, etc).
Currently used soil testing methods are mainly based on procedures recommended for the North-Central Region (NCR-13, 1998). Due to funding limitations, UN-L has conducted little additional calibration and correlation research for soil testing methods in the past 10 or 20 years. Research in other states has raised questions about currently used methods and also produced few new methods that warrant more field validation. Analysis of dry soil samples appears to overestimate P and K availability (Iowa) and NaTPB may be a more suitable extractant for K (Indiana). The new amino sugar N test developed in Illinois has shown promise for identifying sites that may test low for soil nitrates, but will not respond to N application (Mulvaney et al., 2001). Its potential use in a routine fertilizer recommendation program remains is unknown outside of the eastern cornbelt (Hoeft et al., 2001). A promising soil test for sulfur has been developed in Australia (0.25 M KCl extract heated at 40 şC for 3 hours), which includes some of the ester-S compounds contributing much to S-mineralization (Blair et al., 1993), but it has not been tried in Nebraska or neighboring states.
An alternative fertilizer management approach was proposed in the QUEFTS model originally developed for corn in Africa (Janssen et al., 1990) and later improved and used for developing a strategy for field-specific nutrient management in rice in Asia (Witt et al., 1999; Wang et al., 2001; Dobermann et al., 2002). This approach attempts to quantify nutrient requirements of N, P, and K simultaneously and in absolute terms by solving the general equations
Ya = f (Ym, U1, U2, ….Ux)
F1 = (U1 – I1)/R1
…
Fx = (Ux – Ix)/Rx
where Ym = climatic and genetic yield potential, Ya = attainable nutrient-limited yield, Fx = amount of fertilizer, Ux = amount of nutrient in the plant, Ix = supply of nutrient from indigenous sources, Rx = fraction of nutrient recovered in the plant, and 1 to x denote each of the essential plant nutrients. The actual model is a sequence of equations arranged in four successive steps:
1. Estimation of the potential nutrient supply of each nutrient (N, P, K), which is the sum of indigenous nutrient supply and the fraction of fertilizer nutrient becoming plant available during one crop. Indigenous nutrient supply is either estimated from crop-based measurements (nutrient omission plots) or soil test values using locally calibrated empirical models.
2. Estimation of actual nutrient uptake as a function of potential supply (generic interaction model).
3. Estimation of possible yield ranges as a function of actual nutrient uptake of N, P, K based on envelopes describing the range of possible yield per unit uptake.
4. Estimation of the final yield by integration of the possible yield ranges obtained for N, P, and K.
In routine use, information needed to produce a fertilizer recommendation includes (1) climatic yield potential, (2) field-specific yield goal, (3) field-specific estimates of the indigenous N, P, and K supplies, (4) estimated recovery efficiencies of fertilizer N, P, and K, and (5) prices of grain, N, P, and K fertilizer. Using the expected prices of grain and fertilizer, a linear optimization procedure is then used to find the best combination of N, P, and K fertilizer rates to (i) achieve the yield goal (final yield) under the constraint of optimizing the internal efficiencies of N, P, and K in the plant or to (ii) simply maximize profit. The fertilizer rates and yield for profit maximization are typically slightly below those suggested for (i), but the magnitude of the difference depends on price ratios. Because actual growth conditions may significantly deviate from average conditions assumed in the fertilizer recommendation model, in-season adjustment of the N amount recommended may be done to further increase N use efficiency. Strategies for splitting and timing of N applications can either be based on (i) in-season measurement of leaf N status or (ii) standard N management profiles that accounted for major differences in crop management practices.
Potential advantages of this approach are:
·
Simultaneous estimation of the fertilizer requirements
of N, P, and K using the same theoretical approach and using common units such
as lb/A or bu/A in all steps rather than relative terms such as relative yield.
·
This alternative concept may
also recommend zero fertilizer rates. For situations where the indigenous
supply exceeds the crop requirements for a certain yield goal, mining of soil
nutrients is recommended for a limited time period, but nutrient
recommendations are revised every 4-5 years by soil testing to re-establish the
actual level of indigenous supply. However, the fertilizer recommendation is
very much affected by the relationships between yield and crop nutrient uptake,
which reduces the risk associated with relying on a purely soil-test based
concept. It is possible to use crop-based estimates of the indigenous
nutrient supply such as grain yield in a check plot instead of relying on soil
tests alone, assuming the check area has been treated like the rest of the
field up to the testing year.
·
Flexible, customizable usage. The economics of
fertilizer use, both in terms of immediate response (annual) and long-term management
and investment strategies, are built into the model. A P and K recommendation
can be designed for a period of about 4-5 years (or longer) because soil
testing for SOM, pH, P and K and irrigation water analysis should be done in
such intervals only. In addition to this, the N recommendation can be updated
annually, either by (a) measuring residual nitrate and, therefore, updating the
estimate of indigenous N supply, (b) changing the crop rotation/previous crop,
or (c) adjusting N rates through in-season application of N sidedress. The model can also be constrained to include
upper and lower possible limits of fertilizer rates. In the case of N, an upper
limit can be set to avoid excessive N rates that could increase the risk for
leaching losses or pest damage. Upper limits can be set for P and K so that
fertilizer rates remain within affordable ranges.
·
Accounts for regional differences in the climatic yield
potential and crop nutrient demand. A yield potential
map (lookup table) for corn and soybean in Nebraska can be directly used for
making fertilizer recommendations, i.e., selection of a location (by
coordinates or county) results in an automatic regional adjustment of the
climatic yield potential and therefore of the optimal relationship between yield
and nutrient uptake.
·
Many components are easy to update with no or minimum
of field research as conditions change. For example, if an increase in genetic
yield potential occurs, the relationship between yield and nutrient uptake is
the main component that changes for a particular site. If
there is a significant change in soil tests or varieties, than the calibration
equations for estimating indigenous nutrient supply would be modified. These
changes can be accomplished easily by conducting multi-location trials over a
short period of time. There would be no need for a long-term yield response
trials since a specific component is needed and not an empirical relationship.
Potential uncertainties associated with this approach are:
·
Can suitable empirical models
be found to predict the indigenous nutrient supply from soil tests and other
information (soil type, crop rotation, climate, irrigation water)? How
consistent is the soil nutrient supply capacity for different nutrients at
sites in Nebraska? Does a lack of response to the applied nutrient reliably
indicate that supply is consistently adequate for the yield goal? Are producers
likely to accept no-nutrient added check plots needed in the research phase to
estimate indigenous supply? Such check plots may be most feasible for P and K,
less so for N. Much year to year variation in check
plot yield and nutrient uptake may occur within sites.
·
Recovery efficiencies of fertilizer decline in a
non-linear fashion as more nutrient is added or with increasing levels of
indigenous nutrient supply. How can they be estimated with reasonable accuracy?
·
In modeling the nutrient interactions, the same weight
is given to N, P, and K, whereas it is likely that nutrients such as P or K can
be diluted relatively more in the plant than N before a significant reduction
in growth occurs. This may lead to overestimation of true K requirements. A
related issue is whether the linear range of the optimal relationship between
grain yield and plant nutrient accumulation will simply extend further with
additional increases in yield potential? Moreover, is there a theoretical
justification for using a boundary line describing maximum accumulation of a
nutrient in the plant because those appear to be situations of disturbed growth
due to factors other than nutrients?
Our null hypothesis is that current fertilizer recommendations for corn are also valid for high yields and therefore robust enough to fulfill the future needs of farmers in Nebraska. This hypothesis must be compared to an alternative one where future fertilizer recommendation algorithms must be revised periodically. It is assumed that the precision of UN-L recommendations will be improved by becoming more specific and accurate than current approaches to achieve high yields. Such refinements can be made at different levels of complexity such that a general recommendation can be broken down into more meaningful and detailed recommendations for a variety of standard conditions. Ideally, future fertilizer recommendations in Nebraska should fulfill many of the following criteria:
a) Profit maximization and minimal negative environmental impact as the major goal of nutrient management.
b) Account for differences in the climatic yield potential across Nebraska.
c) Account for principal differences among soils in Nebraska.
d) Account for principal differences among crop rotations in Nebraska.
e) Account for principal differences in soil tillage.
f) Flexibility to provide users different, customizable choices based on economic analysis and producer interests.
g) Allow comparative analysis of different management scenarios for managing P and K over periods of up to about 10 years, including cost/benefit analysis.
h) Based on verifiable experimental data with a minimum of subjective adjustment.
i) Continuous mathematical expressions that are applicable to both homogeneous field treatment and variable rate application of nutrients to account for spatial and/or temporal variability in soil supply and crop demand.
j) Joint recommendations used by private and public sector institutions, including those in other states.
Our research will concentrate on fine-tuning recommendations for irrigated corn, with particular emphasis given to high yield levels. Priorities are:
1. The UN-L N algorithm needs to be validated at yield levels beyond those used for the original calibration (>200 bu/A). Options for simplification and geographical zoning should be explored, including adjustment for variation in indigenous N supply, yield potential and crop internal N requirements. We should also develop better recommendations for dynamic adjustment of the N rates suggested by an “average” N algorithm. The latter should provide two basic options to producers:
(i) Standard N management profiles for key cropping systems. This would be BMPs with specific recommendations for optimal splitting of the total N rate, optimal timing of N applications, and recommended methods of application. Our goal is to summarize experimental data and expert knowledge to develop rules of thumb for N management in the most important corn systems in Nebraska, differing by crop rotation, soil type, and perhaps tillage. Presumably, the most important irrigated systems to differentiate are (1) continuous corn on fine-textured soils with ridge till and furrow irrigation, (2) continuous corn on fine- or coarse textured soils with conventional tillage, (3) corn-soybean rotation on fine-textured soils with no-till, (4) corn-soybean rotation on fine-textured soils with conventional tillage, (5) corn-soybean rotation on coarse-textured soils with no-till, (6) corn-soybean rotation on coarse-textured soils with conventional tillage, and (7) corn-dry bean rotation on coarse or fine-textured soils with conventional tillage.
(ii) In-season N management based on either corrective methods (sensing of plant N status or soil testing to determine need for sidedress N) or predictive modeling. In the latter case, a crop simulation model may be used in combination with actual weather data to update yield potential and crop N need in real-time mode. UN-L is currently developing an improved corn model (HYBRID-MAIZE) with potential usage for this purpose.
For updating the UN-L “sufficiency” concept for managing P and K, the key issue is to validate existing soil test categories for high corn yields. Other issues are to evaluate the sensitivity of such recommendations to sampling and analytical error, define proper sampling strategies for tilled and no-till systems, and explore under what situations subsoil nutrients should be included in routine soil testing.
3. Results of this research would also allow improvements of “buildup-maintenance” recommendations by identifying adequate critical soil test levels for different situations. Moreover, implementing a more quantitative approach for estimating crop nutrient requirements (see 1.2) would potentially avoid overuse of P and K in crop-removal based management concept. A specific issue is to clarify the inconsistencies in corn response to K on low and high testing soils.
4. Explore whether NPK fertilizer recommendations can be improved by accounting for geographical variation along agroecological gradients within Nebraska. Such geographical variation is expected to affect crop response to nutrients through (i) variation in the climatic yield potential and therefore internal crop nutrient requirements and (ii) major differences among soil types in their native soil nutrient supply, contribution of subsoil nutrients, and transformation of fertilizer in the soil.
5. Explore whether NPK fertilizer recommendations can be improved by making recommendations more specific to different crop rotations and tillage systems that account for differences in nutrient dynamics (N) and nutrient stratification (P, K).
6. Develop an objective quantitative concept and software for economic assessment of different N, P and K management strategies, both for a single crop year and over longer time periods.
7. Establish a centralized database for past and current soil nutrient – crop response studies to document past work and enhance the process of updating fertilizer recommendations.
A key issue is whether this can be achieved with the classical approaches used in soil fertility research. The classical correlation/calibration yield-response approach would require frequent empirical verification in response to management changes, but the requirement for repeated multi-year and multi-location evaluation is both costly and slow. Therefore, improvements could possibly follow a two-stage approach that involves (1) updating existing soil test methods and fertilizer recommendations with information that has already become available or can be obtained quickly and (2) conducting research towards recommendations that are based on a more quantitative understanding of crop nutrient needs at higher yield levels.
Our goal is to
develop improved nutrient management strategies and recommendations for corn in
Nebraska, including new software tools for fertilizer management. Our focus is
on improving the UN-L recommendations, but the field research conducted will
also provide updated data to potentially improve fertilizer recommendations
used by private soil testing laboratories or fertilizer dealers and agronomy
service companies. Over the longer term, this should lead to more uniform,
validated recommendations offered to farmers in Nebraska.
Specific objectives are:
1. To establish a geo-referenced database on soil fertility and crop response to nutrients.
2. To verify or revise existing N, P, and K fertilizer recommendations for corn in Nebraska at current and future yield levels and cropping technologies.
3. To assess the potential for an alternative approach for management of N, P, and K based on quantitative relationships between nutrient supply, crop nutrient demand, yield, and economics of fertilizer use, including the interactions between nutrients.
4. To fine-tune fertilizer recommendations for corn to major agroecological zones and specific crop management practices in Nebraska.
5. To develop software tools for nutrient management.
In addition to those general objectives, this research will also address some specific questions such as:
· Should fertilizer recommendations be based on a yield goal? If so, what is the best approach to define a yield goal? Should the yield goal (i) be based on the climatic yield potential and economics (e.g., 70 to 80% of climatic yield potential), or (ii) be based on past field performance (e.g., 105% of mean yield of the past 5 years), or (iii) be a combination of (i) and (ii)?
· What are correct crop nutrient removal estimates and soil nutrient levels to be used in buildup-maintenance concepts and how are they affected by yield level in relation to yield potential?
· Is our soybean N credit too low? Should it be more like 60-70 lb/A or 1 lb per bu/A soybean yield?
· In-season N management: how can a pre-season N recommendation for corn be modified to decide on optimal timing and rates of N in response to actual crop growth and N demand? Can we specify typical best N management schemes for the most common situations in Nebraska based on (i) yield potential (climate), (ii) crop rotation, and (iii) tillage?
· What are optimal soil test P and K values for high yields? Can better yield be expected on soils testing high or very high than on soils testing low but with fertilizer P added to compensate for the differences in soil nutrient level? What is an optimal long-term management strategy that also provides high early returns on investments?
· Are the P recommendations liberal enough to apply sufficient P without significant yield loss on unidentified low P areas within a field? To what extent should fertilizer recommendations accomodate imprecise sampling and application procedures?
· How should fertilizer rates and application strategies differ between tilled, ridge-till, and no-till systems?
· What is the potential for new soil N tests such as the PSNT or the Illinois N test (amino sugar-N) and how could they be used in N management recommendations? What are easily measurable/available criteria for identifying sites at which response to N is unlikely and at what yield level?
· Does the NaTPB soil K test provide better information about crop response to K and is it worth considering it for routine soil testing in Nebraska? Why does our research consistently show no benefit to K application and producers who use K consider it a profitable investment?
The data gathered during the first phase of this project (2002 to 2004) will focus on improving our understanding of crop response to nutrient supply in production fields, its spatial variability and effects on the fertilizer economy of different corn systems. We will first compile a database of past fertilizer experiments conducted by UN-L staff. Detailed nutrient trials will be conducted to gather data sets that cover a wide range of soil nutrient supply and yield response by (a) using sites along an agroecological gradient, (b) measuring the spatial variation within each field, and (c) applying combinations of nutrient treatments that, together with the underlying variation in soil nutrients, will create a wide range of yield response within each site. In this experiment we will attempt to create a wide range in yield (e.g., from 100 to >250 bu/acre in corn) by using several levels of NPK treatments. Data collected in the field will be used to verify and possibly modify existing fertilizer recommendation algorithms to more accurately reflect the range of soils and climatic conditions present across the state. This includes information to improve both sufficiency and buildup-maintenance approaches. Additionally, the data will be used to evaluate the potential for the proposed alternative NPK management approach (see 1.2). Armed with these improved principles for nutrient management, research will then continue beyond 2004 to assess the performance of (site-specific) N, P and K management in production fields in terms of profit, yield, nutrient uptake, N use efficiency, and potential environmental impact.
We will pursue our objectives
with a series of standardized on-farm experiments that cover the major crop
production environments in Nebraska. All
experimental work will be conducted at 12 locations annually along the general
climatic trend in Nebraska. Sites will be located on the major soil types used
for corn and soybean production and site selection will also include coverage
of predominant crop rotations and tillage methods. Sites 5-7 are also used in
the USDA-IFAFS project on precision farming. At each site, the experiment proposed
will move to a different location within the same field or to a neighboring
field operated by the same cooperator after each crop. All sites will be on
irrigated land, either sprinkler or furrow irrigation. Sites
should not have had manure applied in the previous 3 years.
Table 1. Experimental sites.
|
Site |
County |
District |
Soil |
Crop Rot1 |
Tillage |
Investigator |
|
1 Mead ARDC |
Saunders |
Southeast |
Tomek, siCL |
C-S |
No-till |
Wortmann |
|
2 Wymore |
Gage |
Southeast |
Otoe, CL |
C-S |
No-till |
Wortmann |
|
3 Brunswick |
Antelope |
Northeast |
Thurman, lS |
C-S |
No-till |
Shapiro |
|
4 Concord NEREC |
Dixon |
Northeast |
Crofton, Nora SiL or Alcester siL |
C-S |
No-till |
Shapiro |
|
5 Bellwood 2 Siffring |
Butler |
Northeast |
Brocksburg/Muir, siL |
C-C |
Ridge-till |
Dobermann |
|
6 Cairo 2 Hinkson |
Hall |
South-Central |
Hall, siL |
C-C |
Ridge-till |
Ferguson |
|
7 Clay Center 2 von Spreckelsen |
Clay |
South-Central |
Hastings, siL |
C-C |
Ridge-till |
Ferguson |
|
8 Clay Center SCREC |
Clay |
South-Central |
Crete, siL |
C-S |
Conventional |
Ferguson |
|
9 North Platte WCREC |
Lincoln |
West-Central |
Cozad siL |
C-C |
Ridge-till |
Tarkalson |
|
10 Paxton |
Keith |
West-Central |
Vetal loamy fine sand |
C-C |
Conventional |
Tarkalson |
|
11 Scottsbluff Darnell |
Scottsbluff |
Panhandle |
Mitchell siL |
C-DB |
Conventional |
Blumenthal |
|
12 Alliance Laursen |
Boxe Butte |
Panhandle |
Creighton very fine sL |
C-DB |
Conventional |
Blumenthal |
1 Crop rotation. C – corn, S – soybean, DB – dry bean. 2 Site also used in USDA-IFAFS
project
Treatments:
1. N0P1K1 N omission plot with moderate P and K dose. This treatment is used (i) as a reference plot for estimating N use efficiencies in treatments 4 to 7 using the difference method and (ii) to estimate the N supply from indigenous sources (soil, irrigation water, atmosphere) measured as total crop N uptake.
2. N2P0K1 P omission plot with moderate N and K dose. This treatment is used (i) as a reference plot for estimating P recovery efficiencies in P1 and P2 treatments and (ii) to estimate the P supply from indigenous sources (soil, irrigation water) measured as total crop P uptake.
3. N2P1K0 K omission plot with moderate N and P dose. This treatment is used (i) as a reference plot for estimating K recovery efficiencies in K1 and K2 treatments and (ii) to estimate the K supply from indigenous sources (soil, irrigation water) measured as total crop K uptake.
4. N1P1K1 Low N input with moderate P and K dose. Used for establishing N response curve and measuring N use efficiency at assumed optimal P and K supply.
5. N2P1K1 Moderate N input with moderate P and K dose. Used for establishing N response curve and measuring N use efficiency at assumed optimal P and K supply. N2 rate is likely to be similar to that estimated for a yield goal of past yield +5%.
6. N3P1K1 High N input with moderate P and K dose. Used for establishing N response curve and measuring N use efficiency at assumed optimal P and K supply. N3 rate is likely to be the one required for breaking through 200 bu/A yields.
7. N4P1K1 Very high N input with moderate P and K dose. Used for establishing N response curve and measuring N use efficiency at assumed optimal P and K supply. N4 rate is likely to be the one required for yields near the attainable yield potential.
8. N4P2K2 Very high NPK input to test the upper yield limit of attainable yield (80-90% of the theoretical climatic-genetic yield potential). Also used to assess whether P1 and K1 rates would be sufficient for very high yields (compare with N4P1K1).
9. NUPUKU UN-L recommendation for N, P and K (YG based on past yield +5%), in many cases no P and K application. N rate is probably similar to N1 or N2.
10. OPT Optional treatments that will vary from site to site. Typically, this will be an additional factor to be compared with any of treatments 4 through 8. Key options include:
a. Assess a second level of higher plant density in comparison with the normal plant density in a treatment aiming at very high yield (N4P1K1 or N4P2K2 with 35,000 to 37,000 plants/A).
b. Assess a different N management strategy (more splits or in-season N management) in comparison with the standard N management used in a moderate standard NPK treatment (N2P1K1 or NUPUKU with in-season adjustment based on SPAD and/or remote sensing).
c. Combination of (a) and (b), i.e. a “maximum fine-tuning treatment” with N4P1K1 or N4P2K2fertilizer levels, high plant density, and in-season N management.
d. Assess response to additional sulfur (S2) in comparison with a standard treatment that receives a blanket amount of S (e.g. N3P1K1S2)
e. Assess response to additional P in comparison with a standard treatment that receives a moderate amount of P (e.g. N3P2K1 or N3P3K1).
f. Unfertilized check
List of optional treatments and blanket nutrient applications per site (2002)
|
Site |
County |
Blanket
applications |
Treatments 10-12 |
|
1 Mead ARDC |
Saunders |
|
T10:
N3P1K1 with three N splits (60-20-20%) |
|
2 Wymore |
Gage |
20 lb S/acre as CaSO4 pelletized; |
T10: N3P1K1
without S |
|
3 Brunswick |
Antelope |
20 lb S/acre as CaSO4 pelletized; |
T10: N2P1K1
without S T11: N3P1K1
without S T12: N3P1K1
+ 40 lb S/acre |
|
4 Concord NEREC |
Dixon |
|
T10: N2P1K1
+ 20 lb S/acre T11: N3P1K1
+ 20 lb S/acre T12: N4P2K2
+ 40 lb S/acre |
|
5 Bellwood Siffring |
Butler |
20 lb S/acre as CaSO4 pelletized; |
T10: N3P1K1
without sulfur; T11: N4P2K2
at 37,000 plants/acre (strip) T12: N3P1K1
at 37,000 plants/acre (strip) |
|
6 Cairo Hinkson |
Hall |
|
T10: 36 Mg/ha
composted paunch manure T11: 146 Mg/ha
composted paunch manure T12:
unfertilized check |
|
7 Clay Center von Spreckelsen |
Clay |
|
T10: N4P1K0,
P band-applied on surface T11: N4P2K0,
P band-applied on surface T12:
unfertilized check |
|
8 Clay Center SCREC |
Clay |
|
T10: N4P2K2
high pop T11: N4P2K0
high pop T12: check |
|
9 North Platte WCREC |
Lincoln |
|
T10: N4P2K2
high pop T11: check |
|
10 Paxton |
Keith |
20 lb S/acre as CaSO4 pelletized; |
T10: N4P2K2
double S T12: check |
|
11 Scottsbluff |
Scottsbluff |
|
T9: 74N, P1,
K1 T10: check |
|
12 Alliance |
Boxe Butte |
|
T9: 74N, P1,
K1 T10: check |
N rates:
|
Corn after corn or dry bean |
Corn after soybean |
|||
|
|
kg N/ha |
lb N/A |
kg N/ha |
lb N/A |
|
N0 |
0 |
0 |
0 |
0 |
|
N1 |
140 |
125 |
84 |
75 |
|
N2 |
196 |
175 |
140 |
125 |
|
N3 |
252 |
225 |
196 |
175 |
|
N4 |
336 |
300 |
280 |
250 |
P and K rates:
|
Rate |
Phosphorus |
Potassium |
||
|
|
kg P/ha |
lb P2O5/A |
kg K/ha |
lb K2O/A |
|
P0 K0 |
0 |
0 |
0 |
0 |
|
P1 K1 |
20 |
41 |
40 |
43 |
|
P2 K2 |
40 |
82 |
80 |
86 |
Experimental design:
· Randomized complete block design with four replicates, blocked along major trends in the field such as a slope. Blocking along a slope would also allow us to do more sophisticated statistical analysis to look at relationships between soil nutrient and crop response as affected by landscape position and variation in soil type. Use prior information such as yield maps, remote sensing, soil testing, cropping history to identify the most suitable location for the experiment within each field. Avoid areas affected by previous management (old farmstead, areas with heavy manure application history, old roads).
· Minimum plot size: 8 rows x 50 ft, but actually size may be larger depending on planting and fertilizer application equipment.
Hybrid and plant density:
· Hybrid chosen together with the farmer. In most cases, we will use what he selects to plant on the whole field, but we encourage use of high-yielding hybrids and also Bt-corn to minimize corn borer problems. Avoid seed corn or popcorn.
· Planted by farmer, but plant density must be high enough to ensure full yield response under irrigation. As a standard, the experiment should be planted at a density of 30,000 to 35,000 plants/A. At some sites, this may require planting the experimental area separately from the surrounding field area because plant density there is likely to be lower than 30,000 pl./A.
· Use the HYBRID-MAIZE model to simulate optimal planting dates for each site based on historical climate data obtained from the nearest weather station.
Fertilizer management:
· Ask farmer to skip all fertilizer application within the experimental area and apply all fertilizer by hand or with small plot equipment using granular fertilizers (Barber spreader). Avoid starter fertilizer application by switching it off when the experimental area is planted. Avoid fertigation of the experimental area through a center pivot. Avoid sites with high groundwater nitrate content, maximum of 5 ppm?
· Nitrogen: use NH4NO3 (or urea). Apply pre-plant N broadcast before last tillage operation. Apply sidedress N at V6 or V10 before irrigation, dribbled as a band between rows. At sites with ridge-till, apply N at V6 just before final hilling so that it gets incorporated into the soil. Standard application schedules:
o Coarse-textured soils: 40% pre-plant, 30% at V6, 30% at V10 stage
o Fine-textured soils: 60% pre-plant, 40% at V6
·
Phosphorus: use 0-46-0 (triple superphosphate).
All applied broadcast before last tillage operation or planting.
·
Potassium: use 0-0-60 (MOP). All applied
broadcast before last tillage operation or planting.
· Sulfur and micronutrients: as required to ensure that none of those limit response to N, P, and K. Conduct soil testing to quantify initial micronutrient levels. At potentially deficient sites, apply a blanket dose to all treatment plots. This includes (i) applying sulfur to all sites on sandy soils or on eroded hillsides and (ii) applying Zn on all soils with high pH (western Nebraska).
Crop
management:
·
The experimental area should not receive manure in the
year of study nor
for 3 years previously.
·
Follow standard guidelines for weed and pest
management, but take extra measures if needed to avoid any interference with
the nutrient response
·
Provide full irrigation at all growth stages to avoid
any interference of moisture stress with nutrient response. Irrigation is
critical for this experiment, particularly during grain filling. Sites either
have center-pivot irrigation or furrow irrigation. Monitor soil moisture,
irrigation and precipitation. Schedule so soil never reaches 50% available
moisture in the rootzone.
·
Use sites that have pH in the range appropriate for
corn (pH >5.5).
Soil profile characterization
Date: once during the season
Locations: one representative location within the experimental area.
Sampling: Giddings probe, large 3” diameter core from 0-4 ft depth. Characterize soil type, depth of layers/horizons, restrictions to root growth. Collect one sample per soil horizon identified.
Processing: All
soil samples will be dried to constant weight at 40-55 şC, ground and passed through a 2 mm soil sieve. Subsamples will
be fine-ground on a roller mill for use in C/N analysis. Archive about 200 g of
each sample.
Analysis: Diagnostic horizons, soil classification, color, structure, particle size (clay, silt, sand), C, N, CEC, exch. K, Ca, Mg, Na, pH, buffer pH, EC, Bray-1 P, Olsen-P, NaTPB-K, oxalate Fe, Al, Mn, dithionate-citrate Fe, Al, Mn
Basic soil
fertility
Date: March/April each year
Locations: each experimental plot
Sampling: Handprobe, 3/4” diameter. In each plot, collect 10 cores each from 0 to 8” and 8-16” depths, each centered between corn rows. In ridge-till systems, sample on ridge shoulder, about 6” from the top of the ridge. Remove surface litter. At no-till sites, split the first core into 0-4” and 4-8” segments. Combine the same depth segments of all cores into one composite sample per depth and plot (tillage and ridge-tillage: 0-8 and 8-16”; no-till: 0-4, 4-8, and 8-16”). Gently break the field-moist soil apart. Separate all coarse organic matter from the soil. Mix, sub-sample, and fill about 300 to 400 g into soil sampling boxes for further processing.
Hand probe (23 mm diameter, 0-8” and 8-16” depth, with plastic sleeve), similar to Doran and Mielke (1984). At 15 locations within the trial area, use a hand probe to sample one core for bulk density determination. In ridge till, make sure that cores represent different position across ridges and between ridges. Cap and transport the whole 8” long cores to the lab for further processing.
Processing: Destructive soil samples: Air-dry soil samples to constant weight at 40-55 şC. Remove all large particles of fresh organic matter Grind and pass through a 2 mm soil sieve. Make sure that most of the soil is passed through and losses remain small. Transfer all soil samples to labeled plastic containers for use in analysis and archiving.
Soil cores: Carefully push the cores out of the plastic tubes. Dry each sample to constant weight at 105 şC and calculate bulk density. Calculate average bulk density and SD for 0-8 and 8-16” depths at each site.
Analysis: 0-8” texture class, SOM, exch. K, pH, buffer pH, EC, Bray-1 P, Olsen-P, DTPA- Zn, Cu, Fe, Ca-monophosphate-S (all done by SPAL). Complementary analysis at selected sites: NaTPB-K, hot KCl-S (UNL)
8-16”: texture class, SOM, exch. K, pH, EC, Bray-1 P, Olsen-P (all done by SPAL)
Nitrate and sulfate
in the soil profile
Date: March/April each year
Locations: each experimental plot
Sampling: Giddings probe, 1 Ľ” diameter. In each plot, collect two cores from 0 to 3 ft depth, each centered between corn rows. In ridge-till systems, sample on ridge shoulder, about 6” from the top of the ridge. Remove surface litter. Split each core into the following depth segments: 0-12, 12-24, and 24-36”. Combine the same depth segments of the two cores into one composite sample per depth and plot. Gently break the field-moist soil apart. Separate all coarse organic matter from the soil. Mix, sub-sample, and fill about 300 to 400 g into soil sampling boxes for further processing.
Processing: Air-dry soil samples to constant weight at 40-55 şC. Grind and pass through a 2 mm soil sieve. Make sure that most of the soil is passed through and losses remain small. Transfer all soil samples to labeled plastic containers for use in analysis and archiving.
Analysis: NO3-N and SO4-N (water extract, ion chromatograph)
Soil nitrogen
tests (see protocol by Walters):
Date: late April/early May for N mineralization indices. Before V6 stage for PSNT. Fall for post-harvest soil NO3-N
Locations: Treatment
1 (N0P1,K1) for N mineralization indices and PSNT. Treatments
1, optimal N rate, and high N (T8) for post-harvest NO3
Sampling: Pre-emergence: 0-6” and 6-12” depths, field moist soil stored cool
PSNT: as per protocol (Magdoff, 1991; Bundy and Andraski, 1995): 1-ft depth, corn 8 to 12 inches tall.
Post-harvest: 0-4’ in 1’ increments
Processing: As per protocol (Mulvaney et al., 2001, Magdoff, 1991; Bundy and Andraski, 1995).
Analysis: Pre-emergence: amino sugar-N (Mulvaney et al., 2001), arylamidase activity, PO4-B4O7, Na2B4O7, aerobic incubation
PSNT: NO3-N.
Standard plant
sampling and tissue analysis:
A 6-plant sample at R6 stage (physiological maturity) is used to determine nutrient concentrations in grain and stover, obtain the harvest index, and determine components of yield. Plot grain yield and final plant population density are measured from a larger harvest area (two 30 ft row segments). Final plot dry matter yield is estimated from the grain yield measured at harvest and the harvest index obtained from the 6-plant sample collected at R6 stage. Plant nutrient accumulation in grain, cobs, and vegetative parts is calculated from nutrient concentrations measured in the R6 sample and the estimated final dry matter fractions.
Date: Physiological maturity and final harvest
Locations: Each experimental plot.
Sampling: See Appendix 9.2 for details. Shortly before R6, mark harvest areas with spray paint. Count number of plants, ears, and prolific plants in the harvest area. At R6 stage, collect 6 plants randomly within the harvest area. Separate into ears and stover (stalks, leaves, husks) and determine fresh and dry weights of grain, cobs, and stover. Take sub-samples of each and grind for nutrient analysis. At final harvest, pick all ears from the 2 x 30 ft harvest area (without husks). Weigh and determine moisture content of grain on 6-ear sub-sample. Take subsample of about 100 g grain and determine 100-seed weight.
Analysis: Grain and stover yield, harvest index, final plant density, barren and prolific stalks, 100-seed weight, ears/m2, kernels/m2
N, P, K, S, Ca, Mg, Cl concentrations and uptake in grain and stover
Other plant
measurements
· Record hybrid and dates of planting, emergence, tasseling, physiological maturity (blacklayer), and final harvest. See Appendix 9.2 for definition of growth stages.
· At selected sites, plant samples will also be collected at V6 and VT stages for determination of total fresh and dry matter and N, P, and K uptake. Where possible, this will be accompanied by chlorophyll meter readings (SPAD) and remote sensing. Sampling protocols follow previously recommended procedures (Peterson et al., 1993), i.e., SPAD readings of the upper most expanded leaf at V8 and V10-12 plus a later sample at VT on the ear leaf.
o 2002: do V6 and VT sampling at six sites (sites 1, 4, 5, 6, 8, 9, 11)
o 2002: IKONOS imagery for sites 5, 6, 7, (8)
· End of season stalk nitrate test (optional, selected sites and treatments only).
1. Crop management and field history: Record previous crops and input use, previous manure use, type and depth of tillage, hybrid and its genetic family/basic characteristics, row spacing. If available, obtain previous soil test data and previously used fertilizer amounts for the field. Try to get the field history established for at least the past 5 to 10 years.
2. Climate: Collect all available weather data from the nearest weather station. Data sets needed for crop simulation modeling include rainfall, evapotranspiration, solar radiation, minimum air temperature, maximum air temperature, soil temperature, and relative humidity on a daily basis throughout the whole year. Consider proximity to a weather station in site selection. If available, measure temperature (soil and air) and rainfall at each site using data loggers.
3. Irrigation water: Record the amount of water used and submit 2-3 samples per site (sample after pump has been running for 4 hours) and year for full water analysis. Chemical analysis of nutrient concentrations in irrigation water is important to assess the impact of other sources of external nutrient inputs (pH, EC, NH4-N, NO3-N, P, K, Ca, Mg, Na, SO4-S) on crop response to fertilizer. In the case of N, we also need this information to properly quantify the irrigation N credit.
4. Geographical coordinates: To allow use of the project data in spatial analyses using GIS and remote sensing, geographical coordinates of all farmers’ fields (corners and field center) and of the nutrient trials will be measured during the course of the project using a Trimble XPS Global Positioning System (GPS) with differential correction.
5. Remote sensing: Remote sensing will be done for selected sites that are part of the IFAFS grant. Imagery for those sites: satellite image of bare soil (1 m resolution) and multispectral images at 2-3 growth stages each year (2.5 or 4 m resolution); airborne color IR images at 2-3 growth stages each year. Possibly, airborne remote sensing at different growth stages can also be contracted for some of the other sites.
6. Farmers’ fertilizer practice (FFP): In addition to the experimental plots, collect plant samples for determination of grain yield, total biomass and plant N uptake at physiological maturity from four sampling plots (each 2 x 30 ft harvest area) in the farmers’ field area surrounding the experiment. These data will be used to assess current on-farm levels of yield and N use efficiency in comparison with those achieved in the different experimental treatments. To do so, we will obtain fertilizer application records from the farmers.
7. % residue coverage: measure before and after planting using standard NRCS method (100-ft tape across the field, count residue hits at each foot mark; NebGuide
Chemical analysis of soil and plant samples will be conducted by SPAL. Additional quality control measures will include the use of two anonymous standard samples in all batches of soil and plant samples analyzed. For the purpose of this project, we will produce our own standard soil and plant tissue samples to be used in addition to those used by the laboratory. This will allows us to gradually develop quality control charts for routine analysis of soil and plant materials.
Establish a central archive of soil and plant samples at Lincoln for future reference and cross-checking analysis from the beginning of the experiments. Of each soil sample, store about 200-300 g dry soil in well-labeled, air-tight plastic containers for future reference. Minimum information includes: site, treatment, plot number, sampling depth, and date of sampling. Similarly, store all plant samples in a central archive for future reference. Archived soil samples will also be used for specific methodology studies such as refinement of soil testing methods or testing of new methods.
In the sections below, specific deliverables are described for the major objectives, including the data requirements for them. In many cases, data from past studies can be used jointly with data to be collected in new field research to make maximum use of prior research investments and knowledge accumulated.
During the course of the project, we will use standard templates for automating most operations related to field and laboratory data collection, data management, and statistical analysis. Data from previously conducted fertilizer trials and experiments conducted during 2002 to 2004 will be assembled in a common format so that they are retrievable in a user-friendly manner (Access database).
· Yield response algorithms/curves:
o Corn yield vs. N supply, general
o Corn yield vs. soil test P + fertilizer P, general
o Corn yield vs. soil test K + fertilizer K, general
o Corn yield vs. pH, Zn, S, Fe (boundary line approach)
o Corn yield response to P or K by existing soil test categories (P, K)
o Corn yield response to new soil tests (amino sugar N, NaTPB-K)
·
Soil test
calibration/correlation: use data from all experiments across sites and plots
within each site to analyze relationships between soil test value + fertilizer
amount and yield. Identify and evaluate categories of supply.
·
Explore the use of boundary
lines for relating yield to soil test values that are not treated as
experimental factors in the field studies (pH, S, Zn, Fe). Establish
"response" relationships between pH and yield (profit) that one could
use for modifying lime recommendations accordingly. The upper rim of a
scatter plot of yield vs. pH shows the true yield response to soil pH because
all points below that rim line indicate some other interfering yield-reducing
factor. A regression through the data cloud would therefore be inappropriate.
Similar approaches can be tried for zinc, sulfur, and iron.
· Crop nutrient uptake data to obtain improved estimates of uptake per unit yield and removal with grain per unit yield.
· Empirical models that describe the buildup of soil test P and K as a function of fertilizer use and crop removal. Data from the long-term fertilizer recommendation comparison studies in Nebraska (McCallister et al., 1987) could be used for this.
· In-season N status at key growth stages (V6, VT) in relation to final yield and remotely sensed plant growth and N status (selected sites, treatments).
· Economic analysis of fertilizer cost and yield response for all 10 treatments tested.
· Map/table of climatic yield potential for corn in Nebraska (based on crop simulation modeling using the HYBRID-MAIZE model being developed at UN-L; will be done in 2002).
· Crop nutrient uptake and yield data measured across a wide range of possible field conditions in Nebraska (sites, hybrids, nutrient rates x soil test levels). These data are used to (i) calibrate the envelopes describing the relationship between grain yield of corn and plant uptake of N, P, and K at maturity (boundary lines of maximum nutrient dilution and accumulation) and to (ii) simulate optimal crop nutrient requirements as a function of NPK interactions and yield potential.
·
Empirical models for predicting indigenous nutrient
supply (N, P, K) based on soil tests and other credits and their relationship
with plant nutrient accumulation in a nutrient omission plot. Components included in this are (i) soil supply mineral N,
mineralization of SOM, avail. P, avail. K (all throughout the rooting depth),
(ii) nutrient release from decomposing fresh residue, (iii) nutrient release
from decomposing fresh manure, (iv) nutrient input from irrigation. Some of
these are difficult to measure directly and must be accounted for indirectly
(as credits). Soil and plant data collected from nutrient omission plots
in experiments conducted at multiple sites over a 2-3 year period should
provide a suitable database for this. Examples for such empirical models used
in the original version of the QUEFST model:
INS (kg N/ha) = 1.7 (pH-3) OC
IPS (kg P/ha) = 0.014 [1-0.5(pH-6)2] Ptot + 0.5 Olsen-P
IKS (kg K/ha) = 0.35 (2+exch. K) (55-OC)
· On-farm estimates of fertilizer recovery efficiency for N, P, and K to obtain default values for common AEZ/soil types (based on difference method by measuring crop uptake).
Agroecological zoning and crop yield potential simulation should play a major role in making refinements of fertilizer recommendations. Re-analyzing existing fertilizer response data within a georeferenced context and by adding more layers of information such as thematic soil maps, climate parameters, and estimates of crop yield potential along major climatic gradients should be part of such updating.
· Map/table of AEZ, soils, irrigation coverage, etc. for corn in Nebraska
· Map/table of climatic yield potential for corn in Nebraska (based on crop simulation modeling using the HYBRID-MAIZE model being developed at UN-L; will be done in 2002).
· Revise fertilizer recommendations by geographical regions (AEZ) and differentiated by key crop management practices. Analyze auxiliary geo-referenced data to categorize and explain nutrient responses in past and new fertilizer studies (Climate, soil data, previous crop, depth and type of tillage, irrigation type, hybrid, manure history, fertilizer placement, dates of planting, tasseling and maturity)
o Corn yield vs. N supply, by AEZ, soil types, climatic yield potential, tillage, previous crop
o Corn yield vs. soil test P + fertilizer P, by AEZ, soil type, tillage, previous crop
o Corn yield vs. soil test K + fertilizer K, by AEZ, soil type, tillage, previous crop, hybrid type
·
Explore relationships among soil properties and between
soil properties and climate and whether such relationships can be used to
refine fertilizer recommendations across the state. One
interesting aspect of some of the pH observations that we have made is a pretty
predictable response to the degree of N mineralization that follows a pH
gradient. The mean change in 0-30cm
[NO3-N] between pre-plant and lay-by stage in a replicated, unfertilized plots
= +2.9 (ph<6.0), +3.7 (pH 6-6.5), +4.7(pH 6.5-7), + 3.3 (pH>7.0...(n=213). Although these changes seem relatively
small they represent a rather short period of time (~4-5 wk) and the average
starting value of [NO3-N] was lower @ ph<6.0 by about 3 ppm.
· MS Access database of nutrient response data from field experiments conducted in Nebraska (CD-ROM).
· Fertilizer recommendation software for interactive decision making, including scenarios for NPK management (stand alone and web-based).
· Fertilizer chooser – utility to identify the cheapest combination of commercial fertilizers for a certain NPK recommendation (stand alone and web-based).
· UN-L soil fertility web page as central information source for Nebraska.
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Database of previous studies |
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Conduct field experiments, plant sampling |
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Sample processing, chemical analysis |
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Data analysis |
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Software development |
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Fertilizer recommendations |
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· Overall coordination, budget, reporting A. Dobermann
· Site selection, experimental design, field management PI at each site
· Soil and plant sampling protocols A. Dobermann
· Sample archive C. Wortmann
· Database and software tools A. Dobermann
· Evaluation/revision of current recommendation approaches
o N algorithm R. Ferguson, D. Walters
o Evaluating of soybean credit C. Wortmann
o Soil test calibration/correlation for P J. Blumenthal
o Soil test calibration/correlation for K C. Shapiro
o Long-term strategies for P and K management J. Blumenthal
o Differences by tillage C. Shapiro
o Fine-tuning to AEZ/crop modeling A. Dobermann
o Rules for splitting and timing of N R. Ferguson, D. Walters
o pH – yield relationships C. Wortmann
· Evaluation of alternative recommendation approach A. Dobermann
· New soil test methods D. Tarkalson, D. Walters
· Web page D. Tarkalson
· Site 7 will be funded through the USDA-IFAFS project
· Total available: 300k for three years (100 k/yr). Annual expenses:
o Annual per site expenses for field operations, labor, sampling (5.6k per site, total of 62k/yr for 11 sites)
o Soil and plant analysis (2.8k per year and site, total of 31 k/yr for 11 sites)
Soil analysis: basic soil
fertility+ Zn, 40 plots x 2 depths @ $6.5 550
Soil analysis: S and
micronutrients, 40 plots @ $10 400
Soil analysis: residual
nitrate, 40 plots @ $6 250
Soil analysis: complete soil
analysis, 5 samples (profile) @ $80 400
Plant analysis: NPKSCaMgCl
in grain and stover, 40 plots x 2 @ $14 1100
Water analysis 100
o Computer programming and publications (4k/yr)
o Miscellaneous (meetings, sample storage, 2k/yr)
o Equipment (Barber fertilizer spreader, 1k/yr)
· Detailed cost estimates for soil and plant analysis (assuming 10 treatments x 4 replicates):
· Soil analysis: basic soil fertility+ Zn, 40 plots x 2 depths @ $6.5 550
· Soil analysis: S and micronutrients, 40 plots @ $10 400
· Soil analysis: residual nitrate, 40 plots @ $6 250
· Soil analysis: complete soil analysis, 5 samples (profile) @ $80 400
· Plant analysis: NPKSCaMgCl in grain and stover, 40 plots x 2 @ $14 1100
· Water analysis 100
·
The process of developing and approving new fertilizer recommendations should include close collaboration among all potential users of such recommendations. Although UN-L soil fertility staff will have the overall scientific responsibility for conducting research, we propose to establish an external technical advisory committee (TAC) to represent all stakeholders involved and help guide both the research and the development of improved recommendations. The TAC is expected to (i) provide technical guidance and (ii) stimulate complementary activities. By doing so, it is hoped that both UN-L and private sector recommendations can be improved during the course of this project and that some differences among recommendations can be eliminated to the advantage of all stakeholders. Members of the TAC must have knowledge of soil testing and fertilizer recommendations and interest in a collaborative effort to the benefit of Nebraska.
UN-L faculty (including USDA-ARS) and Extension Educators will serve as resource personnel for the TAC and be involved in the experimental work as well as the process of developing fertilizer recommendations. Once the actual process of revising fertilizer recommendations begins, the TAC should consider inviting representatives of other organizations such as NRCS, the Nebraska Department of Agriculture, and environmental groups. Initially proposed non-UN-L representatives include:
|
Name |
Address |
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Bob Olsen |
Olsen Laboratories, Box 370, McCook, NE 69001 (308) 345-3670 bobolsen@olsenlab.com |
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Ray Ward |
Ward Laboratories, P.O. Box 788, Kearney, NE
68848-0788 (308) 234-2418 rayward@wardlab.com |
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Byron Vaughn |
MDS Harris, P.O. Box 80837, Lincoln, NE 68501 (402) 476-2811 bv@mdsharris.com
byron.vaughn@mdsps.com |
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Brian Shreve Laboratory Manager |
Servi-Tech Laboratories, P. O. Box 169, Hastings, NE
68901 (402) 463-3522 brians@servi-techinc.com |
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Kenneth Pohlman |
Midwest Laboratories, 13611 “B” Street, Omaha, NE
68144 (402) 334-7771, Ext. 310 pohlman@midwestlabs.com |
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Neal Christensen Regional Agronomist |
Agriliance, 5100 Waverly Rd, Lincoln, NE 68514 (402) 464-9641 nbchristensen@agriliance.com |
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Don Johnson 1 |
2924 S 113th St, Omaha NE 68114 (402) 333-2417 gjdj2924113@msn.com |
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Raun Lohry 2 President, Liquid Fertilizer
Division |
Nutra-Flo Company, 1919 Grand
Av., Sioux City, IA 51106-5708 (712) 277-2011 RaunL@Kay-Flo.com |
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Will Eitzman 3 |
Panhandle Co-op
Association, P.O. Box 2188, Scottsbluff, NE 69363 (308) 630-5316 weitzman@panhandlecoop.com |
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Jason Steffen New Technologies Manager |
Central Farmers Co-op, 330 E. Highway 20, O’Neill, NE 68763 (402) 336-4177 jsteffen@centralfarmers.com |
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Mark Hinze Crop Consultant |
PMC, 2995 N. Osage Av., Juniata, NE 68955 (402) 751-2778 mark@agpmc.com |
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Jerry Mulliken Crop Consultant |
JM Crop Consulting, 1687 CR 24, Nickerson, NE 68044 (402) 721-7467 jmulliken@microlnk.com |
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Larry Leibhart Crop Consultant |
Leibhart Crop Consulting, HC 71 Box 46,
Anselmo, NE 68831 (308) 643-2571 leibhalarr@phibred.com |
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Mark Pavlik Crop Consultant |
Pavlik Crop Consulting, 906 State St., Creighton, NE 68729 (402) 658 3874 pavlikcc@bloomnet.com |
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Lyle VonSpreckelsen Farmer |
V6 Farms Inc., RR1 Box 2a, Clay Center,
NE 68933 (402) 762-3205 lylev@prairieinet.net |
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Robert Atkeson Water Resources
Manager |
Upper Elkhorn NRD, 301 N Harrison St.,
O’Neill, NE 68763 (402) 336-3867 ratkeson@linux3.nrc.state.ne.us |
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Rodney DeBuhr Water Dept. Manager |
Upper Big Blue NRD, 105 Lincoln Av., York, NE 68467 (402) 362-6601 rdebuhr@upperbigblue.org |
1 Chairman, Agronomy Committee, Nebraska Agri-Business Association, Inc. 2 Vice Chairman, Agronomy Committee, Nebraska Agri-Business Association, Inc. 3 Officer, Nebraska Agri-Business Association, Inc.
The experimental program described provides the core data needed to accomplish our objectives. However, funds are insufficient to conduct certain specific activities that could potentially be beneficial to the soil testing and fertilizer recommendation community in Nebraska. Specifically:
Private soil testing laboratories
Soil testing laboratories interested in this project may obtain the set of soil samples collected annually to conduct their own basic soil fertility analysis using their methods (see Appendix 9.3). This is necessary in cases where extraction methods and instruments vary much so that a more laboratory-specific calibration of soil tests can be done. Examples include: SOM using Walkley-Black, Mehlich-3 P, NH4-acetate S with ICP determination, Boron using different methods.
Fertilizer industry agronomists, dealers, coops, crop
consultants
To expand the geographical coverage of our database, UN-L together with industry agronomists and crop consultants could develop a minimum experimental protocol for conducting similar experiments at other sites to be conducted by the industry. UN-L would help with developing the protocols required, implementing basic standards of high-quality field research, and analyzing the data in conjunction with our own data. Such experiments should follow similar principals as those described and contain a minimum set of common treatments, but with modifications as needed and possibly addition of other treatments of interest. Minimum measurements required form such experiments include: (i) exact geographical location, (ii) basic soil fertility analysis (0-8” depth), (iii) water amount and chemical analysis, (iv) grain yield, (v) hybrid, planting date, date of physiological maturity, and (vi) basic cropping characteristics (rotation, tillage, manure history, etc.).
Collaboration with other states
We propose to explore opportunities for collaboration with other neighboring states to (a) use comparable experimental approaches and (b) over the longer term possibly develop joint fertilizer recommendations. Contacts have been initialized with Kansas State University (D. Leikam, R. Lammond), but other states will be informed as well.
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corn. Soil Sci. Soc. Am. J. 43:528-533.
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Hergert, G.W., R.B. Ferguson, and C.A. Shapiro. 1995.
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