On-farm and on-station, we compared economic and agronomic effects of two approaches to soil fertility – the cation ratio paradigm (CR) and $B!H (Bsufficient level of available nutrients $B!I (B (SLAN). Treatments consisted of soil amendments. Crop leaf tissue nutrients were less responsive to treatments than were the soil test values; grain quality, soil organic matter, and weed biomass were least responsive. Yields averaged slightly greater in CR, which may reflect higher rates of nutrients rather than the types of amendments used. Input costs, however, averaged $10.42 per acre greater in the CR treatment than in the SLAN treatment.
Within sustainable agriculture, two contradictory approaches to soil fertility uneasily coexist – the cation ratio paradigm (CR) and the one referred to as $B!H (Bsufficient level of available nutrients $B!I (B (SLAN). SLAN proponents, and this now includes all of the U.S. land grant universities, concern themselves with whether the soil contains enough of each nutrient in forms that are available to the crop. In contrast, the CR approach looks not at the gross amounts of available nutrients but the proportions in which they are represented on the soil cation exchange. The SLAN and CR approaches can result in very different recommendations for a producer. By way of illustration, consider a Marshall series soil in western Iowa on which an organic farmer planned to raise corn. The table below shows soil test levels for several nutrients, a CR-based recommendation for this field, a SLAN-based recommendation, and costs associated with each. [Table here in hard copy] There is little communication between the two approaches because they use different terms and conceptualize fertility differently. It is the farmer who is forced to integrate these two information streams and make the financial judgements required in farm management. Sustainable agriculture must deal constructively with this schism if it is to grow in credibility and relevance. Literature Review To many people, there may seem only a fine distinction between the CR and the SLAN approaches to soil fertility. In fact, these acronyms represent very different models of soil-crop relations. Many crop nutrients in their plant-available forms are positively charged (cations). This includes potassium, calcium, magnesium, and some of the micronutrients. These cations are attracted to negatively charged clay and organic matter, which constitute a major available reserve of those nutrients. The ability of a soil to retain positively charged species in forms exchangeable with the soil solution is quantified as soil $B!H (Bcation exchange capacity $B!I (B (CEC) (Tisdale et al., 1985). In general, high CEC is a desirable quality in a soil, since it means a greater total reserve of cation nutrients is available to plants. The SLAN and CR schools regard this information in very different ways, however. The SLAN approach draws from work begun in the 19th and early 20th Centuries, with the concepts of limiting factors (Von Liebig), a crop fertilizer response asymptotic to a theoretical maximum (Mitscherlich), and a nutrient sufficiency level (e.g. Macy) (Macy, 1936; Tisdale, et al., 1985). SLAN proponents, and this now includes all of the U.S. land grant universities, therefore concern themselves with whether the soil contains enough of each nutrient in forms that are available to the crop (Rehm, 1994; Voss et al., 1996). In contrast, the CR approach looks not at the gross amounts of available nutrients but the proportions in which they are represented on the soil cation exchange. Beginning in the 1940s with research by Bear and associates in New Jersey (Bear, et al., 1945) and continuing through writings by Albrecht (1975), Skow and Walters (1995) and others, the concept has developed of an apparent golden mean of nutrients in a $B!H (Bbalanced soil. $B!I (B The ideal proportion of nutrients on the cation exchange is believed to be 65-85% calcium, 6-12% magnesium, and 2-5% potassium (Graham, 1959). The cation ratio adherents are not impressed by the argument that a particular soil cation ratio can result in wildly different amounts of available nutrients in soils of different cation exchange capacity, nor that the amendments based on CR may have their greatest nutrient effects through altering soil pH. CR proponents, including several commercial testing labs, respond that the ratio approach is efficacious, and that, conversely, the real benefit of liming is often from the addition of calcium (Albrecht, 1975). Moreover, this conceptual model resonates with many practitioners in the sustainable agriculture community who are drawn to the ideal of farming in concert with the natural order. Literature Cited in This Report Albrecht, W. A. 1975. (C. Walters, Jr. ed.) The Albrecht papers. Acres USA, Kansas City, Mo. Bear, F.E., A.L. Prince, and J.L. Malcolm. 1945. The potassium needs of New Jersey soils. New Jersey Agric. Exp. Stn. Bull. 721. Cambardella, C. A., and Elliott, E. T. 1993. Methods for physical separation and characterization of soil organic matter fractions. Geoderma. 56:449-457. Macy, P., 1936. The quantitative mineral nutrient requirements of plants. Plant Physiol. 11:749-764. Rehm, G. 1994. Soil cation ratios for crop production. North Central Regional Ext. Pub. 533. Minnesota Ext. Serv. Rice, C. W., Moorman, T.B., and Beare, M. 1996. Role of microbial biomass carbon and nitrogen in soil quality. In: J.W. Doran and A.J. Jones (ed.) Methods for Assessing Soil Quality. Soil Sci. Soc. Am. Spec. Publ. No. 49, SSSA, Inc., Madison, WI. Rzewnicki, P.E., R. Thompson, G.W. Lesoing, R.W. Elmore, C.A. Francis, A.M. Parkhurst, and R.S. Moomaw. 1988. On-farm experiment designs and implications for locating research sites. Am. J. of Alternative Agric. 3(4):168-173. Shapiro, C.A., W.L. Kranz, and A.M. Parkhurst. 1989. Comparison of harvest techniques for corn field demonstrations. Am. J. of Alternative Agric. 4(2):59-64. Skow, D. and C. Walters, Jr. 1995. Mainline farming for century 21. Acres USA, Kansas City, Mo. Tisdale, S.L., W.L. Nelson, and J.D. Beaton. 1985. Soil fertility and fertilizers. 4th ed. Macmillan Publishing, New York. Voss, R.D., A.P. Mallarino, and R. Killorn. 1996. General guide for crop nutrient recommendations in Iowa. Iowa State Univ. Coop. Ext. Serv. Bull. Pm-1688. Yoder, R. E. 1936. A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. J. Am. Soc. Agron. 28:337-351.
1. Initiate a process with producers to compare the economic and agronomic consequences of two philosophies of soil fertility, the sufficiency level of available nutrients (SLAN) approach and the cation ratio (CR) approach.
2. Implement a series of side-by-side, on-farm and on-station comparisons of the two soil fertility management strategies, with both regimens accurately and credibly represented.
A) Methodology: Research sites consisted of two ISU experiment stations and eight farms per year. Over the course of the three-year study, supported by successive SARE grants, a total of eleven farms participated at some time, four organic, one transitional-to-organic, and six fields on which synthetic production inputs were used in a sustainable manner. Project collaborators included the two primary sustainable agriculture organizations in Iowa: Practical Farmers of Iowa (PFI) and the Organic Crop Improvement Association (OCIA). Based on soil testing and the two approaches to fertility, two sets of recommendations were generated for each site, with materials applied accordingly in replicated plots, six replications per site. Where possible, observations centered on corn and soybean, since these are the $B!H (Bcommon denominator $B!I (B crops in Iowa. Experimental units on farms were the long, narrow strips used in more than 650 PFI on-farm research trials. Strip length varied from farm to farm. Investigation has shown that these long, narrow experimental units do not present a problem for statistical power (Rzewnicki et al., 1988; Shapiro, et al., 1989). An impediment to this kind of research has been the difficulty of applying materials like limestone and manure in narrow strips. The project grant enabled the use of equipment for this purpose (customized Vicon pendulum spreader, 3-point hitch-mounted). Yields, stands, and grain moisture were obtained by the farmer cooperators together with project staff. Yields were measured with weigh wagons and/or drive-on portable scales. Project staff also recorded weed biomass in the two systems (grasses and broadleafed weeds separately); many in the CR school regard weeds as diagnostic of soil cation balance. The project focused on selected crop and soil parameters chosen to reflect impacts of the two systems on grain quality, soil quality, and soil biological activity. Grain testing, handled by a commercial laboratory, measured parameters that may reflect crop nutrient status: feed quality, including crude protein, total digestible nutrients, energy components, and minerals. POM, the particulate organic matter fraction, is a major part of the $B!H (Bactive $B!I (B organic matter pool, which is intimately involved in cycling of carbon and nitrogen within the soil. It is measured by physically separating the organic matter on a series of sieves after dispersing the soil. The amount of carbon in each physical fraction was measured by dry combustion (Dumas method, Cambardella and Elliot, 1993) by the National Soil Tilth Laboratory collaborator. Microbial biomass is the living component of soil organic matter. Many models of organic matter formation include microbial biomass as a precursor to the more stable fractions of organic matter. It is measured using fumigation and extraction techniques (fumigate the soil sample with chloroform and extract with K2SO4). The amount of soluble carbon is then measured analytically (Rice et al., 1996). Aggregate stability, an indicator of soil tilth and resistance to erosion, is measured using wet sieving techniques. Soil is placed on a nest of sieves and oscillated in a tank of water for a fixed amount of time. The amount of soil aggregates remaining on each sieve size is determined by collecting the fraction and drying (modification of Yoder, 1936). Data can be expressed as mean weight diameter or as a distribution. The Tilth Laboratory carried out these organic matter-related measurements. The table that follows lists analyses that were performed and by which laboratory. [Table here in hard copy] B) Evaluation: Crop production and profitability were monitored for each site and overall, as were crop quality and soil physical, chemical, and biological indicators. Farm field days and farmer-scientist roundtables took place at PFI general meetings to discuss findings and their impact and to assess the project process itself. Study results were presented at the national meetings of the American Society of Agronomy in Denver, CO, November, 2003.
Amendments and Input Costs Tables 1-3 in the Appendix show the types of inputs used for each treatment at all locations during the three years of study. The types and rates of amendments varied from site to site and year to year, since recommendations were based on annual soil tests and the interpretation philosophy of each treatment. The tables also show the costs, minus delivery, for each treatment. Total fertilizer costs shown are those for lime, calcium, potassium, and zinc, but not for manure or nitrogen, which may have been applied to the whole field. Some phosphorus fertilizer of either organically approved or synthetic origin was applied, but we tried to keep P application rates the same across the two treatments, and therefore these applications are not included in the overall treatment costs. Input cost was the dominant difference between the two treatments. As Figure 1 (Appendix) shows, the average difference in input cost between the treatments averaged $10.42 per acre; with the CR approach requiring $9.61 more in fertilizer and $0.81 more in lime. These figures are based on local prices for limestone, although in parts of Iowa calcitic limestone is not locally available. Producers in those areas can expect to pay approximately $0.15 per ton per mile additional to import this lime. Results for Individual Variables In the following discussion of individual variables, the criterion for statistical significance is set at $B&A (B=0.11, or 89% confidence. Traditional confidence levels of 95% or 99% may have passed over relationships that were submerged in the $B!H (Bbackground noise $B!I (B associated with these on-farm trials. On the other hand an even more liberal standard would have resulted in numerous statistically $B!H (Breal $B!I (B differences that would only make interpretation more difficult, increasing the risk of declaring significant differences when none really existed (type 2 error). Moreover, even at $B&A (B=0.11, many relationships were significant for only one year, calling their reality into question. Soil Mineral Analysis Tables 4 and 5 in the appendix show selected soil mineral analyses for 2001 and over the whole period of the study, respectively. Because treatment effects may not have evidenced themselves immediately, data from the last year of the project are shown separately. Eighteen parameters were analyzed, and the tables show a scatter pattern of treatment responses. Cells are highlighted in yellow if the corresponding nutrient was part of the treatment regimen on that farm at some time during the study. If the alpha level for a treatment difference was less than 0.11, the probability of a greater F-statistic appears in the tables in green, and the means of the two treatments are shown in red and blue, red being used for the larger of the two numbers and blue for the smaller. The same protocol also applies to the tables that follow. When the data are examined across all sites, nine of the 11 farms showed a significant treatment difference for zinc, a frequently used nutrient in the CR treatments. Six farms showed treatment differences in sulfur, a component of both gypsum (calcium sulfate) and the zinc and potassium sulfates that were applied (Table 5, Appendix). Other parameters related to treatments are calcium, magnesium, potassium, and acidity. A number of farms showed significant treatment differences for three nutrients, but the differences occurred in some cases where nutrient was not applied to either treatment. A number of other differences appeared for nutrients other than those applied; of these, four farms showed manganese differences, two farms showed iron differences, two farms showed treatment differences in copper, and one farm showed a phosphorus difference (Table 5, Appendix). Weed Biomass Weed biomass was collected because it has been asserted that soil calcium/magnesium ratios affect certain weeds, perhaps through soil structure effects. The Appendix provides broadleaf and grassy weed biomass, with Table 6 showing 2001 results for each farm and Table 7 showing results combined over years. Data generally appear only if the difference between treatments was significant at close to the 11 percent level of significance. In 2001, three of eight farms exhibited what might be considered significant differences in grassy weed biomass, with two being greater for the SLAN treatment and one for the CR plots. This last farm (# 11) also showed a significant difference in grassy weed biomass when the data were analyzed across the years. Farm #8 also showed a significant difference over years in grass biomass, but in the other direction, i.e. SLAN>CR . Farm #1, which only participated in year one, had significantly greater broadleaf weed biomass in the SLAN treatment, as did Farm #7. Overall, though, grassy weed and broadleaf weed biomass were similar for the CR and SLAN treatments. Grain Quality Analysis Grain samples were analyzed for 15 parameters related to feed quality (Tables 8 and 9, Appendix) as proxy for grain quality. In general, grain quality was fairly uniform across the treatments, but there were farm-to-farm variations. Taken over all three years, grain from Farm #2, for example, showed significantly higher crude fiber, acid detergent fiber, and crude protein and lower crude fat in the CR treatment. Farm #9, in contrast, showed greater neutral detergent fiber, crude fat, and net energy for lactation in the CR treatment than the SLAN. There was no consistent pattern to these differences, as illustrated in the summary statistics in Tables 8 and 9. Moreover, few of the farms provided significant treatment differences in the parameters related to applied amendments. Crop Leaf Tissue Analysis Tables 10 and 11 in the Appendix provide overall and by-farm values for 12 leaf nutrients for 2001 and for the whole study, respectively. It should be noted that the overall data by farm are averages of two or three different crops in the rotation. In 2001, three of eight farms did show elevated leaf potassium in the CR treatments; in two of the three this is consistent with fertilizer rates applied, and in the third (#6) a high rate of lime in the CR may have increased potassium availability. Leaf potassium was a significant overall treatment effect in 2001 (Pr>F=0.0907), but not for the study as a whole. In three of nine farms overall and in three of eight in 2001, leaf magnesium was significantly lower in CR treatment crops; however leaf Mg was higher on one farm in 2001. Two of eight farms in 2001 and two of nine in the study overall showed elevated leaf iron in the CR treatment; however, in the same number of cases SLAN yielded higher leaf iron levels. The apparent statistical significance is in part a function of the uniformity of soil test iron readings; the average levels of leaf tissue iron in the two treatments were only 1.2 percent apart. Leaf tissue nitrogen was significantly greater in the CR treatment in 2001 (Pr>F=0.0741) but not for the study as a whole. Even though zinc was a frequently applied nutrient in the CR treatment, zinc levels were significantly higher in the CR treatment on only one farm in the overall analysis and on no farms in 2001. Particulate Organic Matter Although organic matter was not directly involved in the treatments, several parameters related to the organic fraction of the soil were measured. Particulate organic matter (POM) is a component of organic matter that is related to soil aggregation (tilth) and the potential for mineralization of nutrients into plant-available forms. Tables 12 and 13 in the Appendix show, respectively, POM-related parameters for 2001 and the study overall. There were not many treatment differences that could be considered statistically significant. Water pH was different on two farms. Soil aggregate mass, an indication of aggregate stability and thus soil structure, was greater in SLAN than CR on Farm #3 in 2001 and in the overall analysis on Farm #9, but the overall statistics for aggregate mass show no treatment effect. This is not surprising since on any given farm, crop rotations and yields were the same for the two treatments. Grain Yield Table 14 provides grain yields by farm for each year of the study. There were three site-years in which the CR treatment had higher yields (Pr>F≤0.11), and one in which the reverse was true. Farm #6 had greater yields in the CR two years out of three. This was the farm in which soil potassium and phosphorus levels were higher in the CR treatment, although levels were in the ISU $B!H (Boptimum $B!I (B range throughout the field. Also, the pH measured in 2001 was approximately 6.1 in SLAN and 6.4 in the CR experimental units, a small but statistically significant difference. Change in Soil Test Parameters Figures 2-7 in the Appendix present changes in several soil test parameters over the course of the study. The figures are based on averages across farms and thus show only general trends. Each graph separates the five farms that participated all three years ( $B!H (BCR 3 $B!I (B and $B!H (BSLAN 3 $B!I (B) and the three farms that joined the study in 2000 ( $B!H (BCR 2 $B!I (B and $B!H (BSLAN 2 $B!I (B). For each group, initial soil samples were taken prior to the first crop, either the previous fall or in the early spring. A single, bulked sample was taken for the field overall, and this sample established the initial readings for both treatments. Soil pH remained stable on the farms that participated in the study the entire three years (Fig. 2, Appendix). In the three farm fields that joined for the second year of the experiment, soil pH declined in the final year of the study to approximately 6. This is a level that may or may not affect soybean yields, depending on the pH of the subsoil. Differences in pH were a function of farm more than of treatment. Soil test zinc was clearly affected by treatment (Fig. 3, Appendix). From initial levels of 2.0 ppm (three-year fields) and 1.3 ppm (two-year fields), the CR levels climbed to 2.5 ppm after three years and to 2.2 ppm in the two-year fields, while Zn levels remained flat in the SLAN. This reflects the frequent application of Zinc sulfate in the CR treatment of most farms. No zinc was applied in the SLAN field strips. Soil test potassium in parts-per-million remained fairly constant on the five farms that participated in the study all three years (Fig 4, Appendix). Both treatments in the three farms that joined the study in 2000 exhibited somewhat lower soil test potassium prior to that cropping year, but levels increased subsequently. Most samples tested in the ISU $B!H (Boptimum $B!I (B range or greater throughout the study. At the final sampling, in 2001, CR field strips overall showed significantly greater soil test potassium than SLAN strips (Pr>F=0.0138; Table 4, Appendix). The CR soil fertility paradigm focuses on the relative base saturation of K, Ca, Mg, and H. Like absolute potassium levels, percent potassium on the exchange sites was stable for the three-year farms and increased from a low level on the two-year farms (Fig. 5, Appendix). In 2001, percent K was significantly greater in CR strips than SLAN strips (P>F=0.0065). During the experiment, average K saturation levels changed from 2.8 percent to 3.2 percent in CR strips of the three-year farms, while corresponding strips on the two-year farms went from 1.7 to 2.6 percent. At those rates of change, 8-17 years would be required to increase cation exchange potassium to the 5 percent saturation sometimes cited as the ideal according to the cation ratio approach to fertility (Bear et al., 1945). Percent calcium on the soil cation exchange was stable in the 64-66% range on the three-year farms, with a gradual increase in the CR field strips. No treatment difference was evident on the two-year farms, but percent calcium declined from 69 to about 60 percent over the period. Since the two treatments were similar in this regard, the suggestion is that pH and field conditions at the final sampling may have contributed to the low numbers. The ideal proportion for calcium has been set, variously, at 65% (Bear et al., 1945) and 65-85% (Graham, 1959). From initial levels around 25.5% (three-year fields) and 24% (two-year fields), magnesium ion saturation declined to 22-23 percent at the end of the study in both treatments. The ideal proportion for magnesium was set at 10% by Bear et al. (1945). By that standard, and at that rate of change, an additional 11 or 12 years of CR treatment would be required to meet the 10% target. However, magnesium saturation decreased steadily in both treatments during this study, probably for the same reasons that reduced calcium levels. Averaged over all farms, end-of-project magnesium levels were nearly identical in the two treatments. A longer period of observation would have been required to distinguish a treatment effect from the overall effects of liming and sampling conditions. In 2001, the two-year farms declined from the previous year approximately one percent in saturation of magnesium and eight percent in saturation of calcium. These declines were accompanied by an increase in hydrogen on the cation exchange of 8-9 percent (Fig. 8, Appendix), consistent with a reduction in soil pH in both treatments. Multivariate Analysis The pattern of treatment responses was inconsistent except for those parameters directly related to inputs applied (e.g. soil test Zn). In an effort to discern response patterns, the data were submitted to principal components analysis. The principal components approach seeks the best combination of parameters to account for observed variability. The weightings on the parameters define a vector in multidimensional space. Subsequently a second combination of the parameters is sought to define a vector orthogonal (perpendicular) to the first and best accounting for the remaining variability. A series of these eigenvectors may be generated, each accounting for a diminishing portion of the observed variability. Ideally, the first few eigenvectors will identify most of the important relationships among the data elements. A feature of principal components analysis is its intolerance of missing data. If a farm-year was missing observations, it was necessary to either withdraw those variables from the entire multivariate analysis or to eliminate that farm-year from the analysis. For this reason, the parameters were divided into two groups. The $B!H (Bblue $B!I (B parameters were present in the greatest number of farm-years. The $B!H (Bgreen $B!I (B parameters comprised a more comprehensive set of the variables measured (including $B!H (Bblue $B!I (B variables), but the data from fewer of the farm-years included all these parameters. Tables 15 and 16 in the Appendix show the first eight principal components for the blue variables and the green variables, respectively. Tables 1 and 2 below summarize the most heavily loaded parameters for the first eight eigenvectors in the blue and the green sets of variables, respectively. Among the $B!H (Bblue $B!I (B variables, the most strongly weighted variable in principal components 1 through 8 is, respectively: soil test calcium; soil pH; grain crude protein; particulate organic matter carbon; soil test sulfur; POM carbon; magnesium saturation of the soil cation exchange; and grain net energy for lactation. In Table 2 of the $B!H (Bgreen $B!I (B parameters, the most strongly weighted indicator in principal components 1 through 8 is, respectively: soil test calcium; grain crude protein; leaf iron content; soil aggregate mass; calcium saturation of the exchange; magnesium saturation of the exchange; grain dry matter content; and soil test sulfur. [Tables here in hard copy] Tables 15 and 16 in the Appendix also show the Eigenvalue for each principal component and the proportion of total variability explained by the Eigenvector. In addition, the table provides results of paired-comparison t-tests for treatment differences in each of the eight principal components. For the $B!H (Bblue $B!I (B group of parameters (Table 15), significant treatment differences in the Eigenvalue occurred for principal component 5 (with soil test sulfur being the dominant variable) and for principal component 7 (with percent magnesium saturation on the soil cation exchange being dominant). These two variables are related to the treatments. Sulfur was applied to both treatments on some farms as potassium sulfate, but it was applied at greater rates as calcium sulfate (gypsum) as part of the CR treatment on several of the farms (Tables 1-3, Appendix). Magnesium application, even as a component of lime, was avoided in the CR treatments of all farms as part of the readjustment of soil cation ratios. Similarly with the green variables shown in Table 16, principal component 8 provided a significant difference in Eigenvalues between the two treatments. The most heavily loaded parameter was once again soil test sulfur. It is true that the principal components yielding significant t-tests represent only a minor amount of the total observed variability. However, they are at least logically related to the two treatments, and probably more so than the other principal components. The first principal component of both the $B!H (Bblue $B!I (B and the $B!H (Bgreen $B!I (B parameters is most heavily weighted on soil test calcium. That could be considered a treatment effect except that soil test magnesium is also positively weighted in both these principal components; in principal component 7 of the blue variables, calcium and magnesium are oppositely weighted, consistent with a treatment effect. CR in this study attempted to reduce magnesium by increasing calcium saturation. Other Eigenvectors in both groupings seem to relate to general characteristics of soil testing, leaf analysis, grain quality, and organic matter. Consistent with this, the plots of principal component 1 versus principal component 2 (Figures 9 and 10, Appendix) show that values cluster according to farm much more than – and by year as much as – treatment. Discussion Individual Parameters The consistent treatment effects observed in this study were those directly related to the soil amendments used. Thus calcium, magnesium, sulfur, potassium and zinc were the elements associated with overall treatment differences in soil tests and/or leaf tissue tests. There were farms that did not show significant treatment differences for these nutrients, and there were farms that showed significant treatment differences for nutrients that were not included in any application. Grain analysis identified no significant treatment effects overall, although individual parameters on individual farms were significantly associated with treatment. Similarly, while there were instances of significant treatment differences in grass or broadleaf weed biomass, overall weed levels were similar both in the study overall and in 2001, the final year of the project. Neither did the soil organic matter analyses show significant treatment differences for the study as a whole or for 2001. Soil water pH (1:1 soil:water), analyzed for the study as a whole, was the only variable showing significant treatment effects. The CR approach to fertility seeks to bring cation nutrients into conformity with an ideal set of ratios. For the Midwestern soils that were part of this study, the CR treatment was designed to increase potassium, calcium, and zinc and to decrease magnesium on the soil cation exchange. Zinc was the nutrient most responsive to applications, although soil test results were not expressed as percent zinc on the cation exchange. Potassium increased for both treatments, both in absolute terms and as a percent of cation saturation, and CR was significantly higher in potassium saturation than SLAN by the end of the study. Based on the observed rate of change, 8-17 years would be required to increase cation exchange potassium to the 5 percent saturation sometimes cited as the ideal according to the cation ratio approach to fertility (Bear et al., 1945). A more lenient target of 2-5 percent was suggested by Graham (1959). By that standard, the farms that remained in the study for three years were already adequate in potassium when they began the project, and the three farms that joined the project in 2001 had adequate potassium by the end of the project. The ideal proportion for magnesium has been stated as 10% (Bear et al., 1945) or 10-20% (Graham, 1959). By the first standard, at least an additional 11-12 years of treatment would be required to meet the target in the CR treatment. Actually, since magnesium levels fell similarly in both treatments for other reasons, there is little evidence of a treatment effect on magnesium saturation during the period of the study. By the standard of Graham, magnesium saturation was already approaching acceptable levels at the end of the project; however, as mentioned, most of that change was pH related. Yields Considering statistical significance to be $B&A (B<0.11 (89% confidence), there were three instances of greater yields in the CR treatment and one of greater yield in the SLAN treatment. In none of the years, nor for the study as a whole, were there significant treatment effects on crop yield. Overall, there were small, nonsignificant, yield differences for each of the crops. These will be considered in the summary of economics. Assuming for the sake of discussion that these small yield differences were actually treatment effects and not merely random, the question as to the cause arises. One way of regarding these results is as a vindication of the cation ratio approach to soil fertility. An alternative explanation is based on the fact that the CR often applied fertilizer in cases where the SLAN approach recommended none. The SLAN recommendations were based on Iowa State University calibrations. ISU soil test categories, for example, represent $B!H (Ba decreasing probability of an economic yield response to applied nutrients. $B!I (B (ISU Extension bulletin PM-1688). $B!H (BEconomic yield $B!I (B recognizes that there may be a crop response to amendments beyond economically optimum levels – just not a profitable response. In terms of crop yields, this study does not vindicate one treatment or the other, since each paradigm accounts for the yield observations. Moreover, we do not have sufficient information to distinguish the factors that made CR yields greater on Farm #6 two of three years. Treatment strips remained the same on each farm for the duration of the experiment, so the persistent effect on yields could either be due to treatment or perhaps variations in the field. (Variability due to replications, however, was low.) Zinc, a frequent CR amendment, was never applied on Farm #6. The first yield difference appeared in the first cropping year, by which time the chief difference in treatments had been 800 lbs per acre of dolomitic (magnesium-containing) limestone versus 2,000 lbs of calcitic limestone. By the end of the 1999 cropping season, however, the field strips differed in magnesium and calcium percent saturation by only a percent or two. As Figures 9 and 10 (Appendix) show, Farm #6 scored lower than other farms in the first principal component, but this principal component bears no obvious relationship to the treatments of this study. Patterns Observed Multivariate analysis was employed to detect patterns in the data. Most of the patterns found in soil, crops, and weeds relate to farm differences, not to differences between the two treatments. These farm-to-farm differences likely resulted from both physiographic characteristics such as soils and from the cumulative effects of farm management. Some treatment-related patterns in the data did emerge, but these related to nutrients applied rather than to secondary characteristics such as other nutrients, soil organic matter, grain quality, or weeds. Treatment-related associations in the data were overshadowed in magnitude by farm-related differences.
The sustainable agriculture movement is discovering new relationships and mechanisms relating to pest management, systems functioning, weed dynamics, and more. Soil is an area of great interest to practitioners pursuing sustainability. A number of models, or paradigms, have been proposed to describe the nutrient-supplying functions of soil. Each paradigm has its proponents, and industries have grown up to supply materials and testing services consistent with each model. Farmers are frequently $B!H (Bcaught in the middle, $B!I (B not knowing which approach will best serve them in their pursuit of profitability and sustainability. Given the cogency of this question for sustainable agriculture, it is perhaps surprising that this study is only the second SARE project to address the issue at all, and the first in field crops. This project was designed to generate reliable comparative data regarding the $B!H (Bsufficiency $B!I (B approach to soil fertility developed and recommended by university research, and the $B!H (Bratio $B!I (B approach used by a number of crop consultants and testing laboratories. The project was also intended to develop a dialog on the topic involving farmers, scientists, and information providers on both sides of the question. In accomplishing these two objectives, the project has helped sustainable producers to supply their crops with needed nutrients in a more cost-effective manner and has increased their capacity to make effective management decisions for their farms. Major Observations • Added input cost in the CR (cation ratio) was the most striking and consistent difference between the treatments – a $10.42 per acre annual difference, on average. • Input costs were only partially offset by small yield increases in CR; these could have been due to the CR paradigm itself or merely to the application of nutrients beyond economically optimum levels. • In soil and leaf tissue testing, the parameters most affected by treatment were those directly related to nutrients applied. Few other soil or leaf parameters, nor the parameters measured in grain quality, organic matter, and weeds showed treatment effects for the study as a whole. Individual parameters in particular farms and years showed treatment effects, but these results were not consistent. • The strongest patterns to emerge from these data reflect farm differences more than the experimental treatments. • Producers considering a change in soil fertility-related practices are advised to gather information from a wide range of sources, monitor costs closely, use test strips, and where possible proceed one nutrient at a time. This approach should help to maximize their control.
In this study, input cost was consistently greater in the CR treatment than the SLAN, averaging $10.42 more per acre per year. Table 17 in the Appendix weighs against this cost the annual yield margins and commodity prices. The yield advantage before input costs comes to $2.74, $3.77, and $2.77 per acre per year for corn, soybean, and small grains, respectively. Assuming organic grain prices might double this advantage for corn and for small grains and triple or quadruple it for organic soybean. Based on those assumptions, soybean would be the only crop in the rotation for which the yield difference would carry a higher value than the increased input cost. Furthermore, when averaged over the three crops the additional amendments would not be economical even with the price premiums received on an organic farm. The CR treatment changed some soil characteristics in the direction of cation ratio norms. Presumably if and when those norms were reached, input costs in the CR treatment would decline. Based on the averages generated in this study, that time might arrive in 8-17 years in the case of potassium and probably in a much longer period in the case of magnesium. However, these timelines depend on the goal. Graham (1959) provided ranges rather than fixed targets for cation nutrient saturation. According to those targets, these farms are within or close to the desired ranges. This more relaxed approach would reduce input costs; however, it could also allow potassium levels to fall below that which the SLAN would consider yield limiting. CR recommendations in this study were made by Mr. Keith Cuvelier, a consultant who uses the CR approach in his business. Another consultant might well bring a different variation of CR, and different soils would raise other issues. In this study we observed greater CR input costs that outweighed a possible yield advantage. A producer in a different location, working with a different information provider, might face a different cost-benefit balance. Ideally, that producer would run test strips as were done in this study. However, one motivation for this project was precisely the difficulty that farmers have in applying liming and other high-volume amendments in side-by-side test strips. At the very least, we recommend to producers who are considering a change in their fertilization practices to considering a range of information sources. There is nothing in either the CR or SLAN approaches that precludes a step-by-step approach, so individual nutrients of amendments can indeed be compared in side-by-side strips, and preferably more than a single pair of strips. In this way a farmer can move ahead in a logical way that provides ongoing opportunities for observation and producer control.
We did not anticipate an immediate change in farmer practices as a result of the research or the 13 field days carried out as part of this study. We do look forward to increased discussion of these soil fertility issues in the producer community as more written information becomes available in bulletin and Web formats. We are offering presentations on the project to district board representatives of PFI as they plan winter community meetings.
Educational & Outreach Activities
Five of the participating farms hosted field days in 1999, with four doing so in 2000 and in 2001. Total attendance at these events was more than 450. Follow-up information was provided at the annual winter conference of Practical Farmers of Iowa, and in the organization $B!G (Bs quarterly newsletter, The Practical Farmer. A report ultimately will appear on the organization $B!G (Bs Website, http//www.practicalfarmers.org, and in 2004 a bulletin will be included in the ISU Extension Farm Talk series, which features on-farm research and documentation. A poster acknowledging SARE support for the study was presented at the annual meetings of the American Society of Agronomy in Denver, in November, 2003.
Areas needing additional study
This project was not designed to calibrate crop response and the two approaches to soil testing. Those studies have taken place in controlled settings for the SLAN approach and have not been an area of active inquiry in the CR school of thought. This project instead aimed to reveal what short-to-medium term outcomes could be expected from the two approaches, at least 1) on fertile Iowa soils with a history of ample use of amendments, and 2) as the two philosophies were implemented here by us. The answer appears to be that 1) there will not be significant differences in grain quality, weeds, or organic matter, and 2) that crops can be grown either way, although CR is at a net economic disadvantage.
The long-term result of the CR approach will depend in part on whether amendments can bring soil parameters to the prescribed levels or ranges. In the short term, we found evidence that this would take place in a reasonable time frame for potassium and zinc, but little evidence that three years of amendments had changed the calcium-to-magnesium ratio. Longer-term studies than this are needed to shed light on the question. These studies would also be necessary to detect any treatment effects on soil, crops, or weeds that our project missed, and to distinguish between location-specific soil characteristics and those that are the long-term result of management.