Interpreting soil test results. What do all the numbers mean?

ABSTRACTIron (Fe) deficiency chlorosis in crops is common in high-pH calcareous soils. Soil and plant testing is routinely used for diagnosing iron (Fe) deficiency chlorosis in crops, with mixed results. This article presents an overview of the factors that influence soil and plant tissue testing results. It is clear that soil tests for Fe are dominantly influenced by soil pH, bicarbonate, and moisture regime rather soil test result per se. This is because the solubility of Fe is more regulated by soil pH and moisture regime. Plant tissue testing for Fe can complement the results of soil testing for Fe. But at times, especially in calcareous soils, total Fe in plant tissue is not related to Fe deficiency, but metabolically active Fe is better at diagnosing the occurrence of the disorder. A combined use of soil and plant tissue testing seems more helpful in diagnosing Fe deficiency chlorosis disorder in crops.

Conventional soil tests are commonly used to assess single soil characteristics. Thus, many different tests are needed for a full soil fertility/soil quality assessment, which is laborious and expensive. New broad-spectrum soil tests offer the potential to assess many soil characteristics quickly, but often face challenges with calibration, validation, and acceptance in practice. Here, we describe the results of a 20 year research program aimed at overcoming the aforementioned challenges. A three-step approach was applied: (1) selecting and establishing two contrasting rapid broad-spectrum soil tests, (2) relating the results of these new tests to the results of conventional soil tests for a wide variety of soils, and (3) validating the results of the new soil tests through field trials and communicating the results. We selected Near Infrared Spectroscopy (NIRS) and multi-nutrient 0.01 M CaCl2 extraction (1:10 soil to solution ratio; w/v) as broad-spectrum techniques. NIRS was extensively calibrated and validated for the physical, chemical, and biological characteristics of soil. The CaCl2 extraction technique was extensively calibrated and validated for ‘plant available’ nutrients, often in combination with the results of NIRS. The results indicate that the accuracy of NIRS determinations is high for SOM, clay, SOC, ECEC, Ca-CEC, N-total, sand, and inorganic-C (R2 ≥ 0.95) and good for pH, Mg-CEC, and S-total (R2 ≥ 0.90). The combination of the CaCl2 extraction technique and NIRS gave results that related well (R2 > 0.80) to the results of conventional soil tests for P, K, Mg, Na, Mn, Cu, Co, and pH. In conclusion, the three-step approach has revolutionized soil testing in The Netherlands. These two broad-spectrum soil tests have improved soil testing; have contributed to increased insights into the physical, chemical, and biological characteristics of soil; and have thereby led to more sustainable soil management and cropping systems.

Wheat is a major crop grown in the highlands of Ethiopia, especially in the study area, which is a key food source. In 2020, a pre-extension demonstration was carried out in Gechi district of Buno Bedele Zone to show how soil testing can help decide the right amount of phosphorus fertilizer for bread wheat. The study aimed to look at how much wheat is produced and how profitable it is when using phosphorus fertilizer based on soil testing results, and to teach farmers about using fertilizer rates that are specific to their fields. Two types of fertilizer plans were tested: one based on general recommendations that farmers usually follow, and another based on soil testing results. The improved bread wheat variety called Liban was used. The demonstration took place on one FTC and 10 fields owned by farmers, with each treated area being 12 meters by 20 meters. The rows were spaced 20 cm apart and the right amount of seeds and nitrogen fertilizer was used as recommended for the area. A field visit was held for 59 people when the wheat was ready to be harvested. The best yield came from the method that used soil test results, giving more than a 49.54 increase in grain production compared to the general recommendation. Also, the economic analysis showed that, the highest average profit was made with the soil testing method, earning 40,112.7 Ethiopian birr. Therefore, it is important to start scaling up the use of soil test based phosphorus fertilizer recommendations for bread wheat in the study area, and similar agro ecology.

Song, C., Zhang, X., Liu, X. and Chen, Y. 2012. Effect of soil temperature and moisture on soil test P with different extractants. Can. J. Soil Sci. 92: 537-542. Temperature and moisture are important factors affecting adsorption, transformation and the availability of soil phosphorus (P) to plants. The different temperatures and moisture contents at which soil is sampled might affect the results of soil test P (STP). In order to evaluate the effect of the temperature and moisture, as well as the fertilization level, on the results of soil test P, an incubation study involving three soil temperatures (5, 10, and 20°C), and three soil moisture contents (50, 70, 90% of field water-holding capacity) was conducted with Chinese Mollisols collected from four fertilization treatments in a long-term experiment in northeast China. Four soil P test methods, Mehlich 3, Morgan, Olsen and Bray 1 were used to determine STP after a 42-d incubation. The effect of temperature and moisture on STP varied among soil P tests. Averaged across the four fertilization treatments, the temperature had significant impact on STP, while the responses varied among soil P test methods. Mehlich 3, Morgan and Bray 1 STP decreased and Olsen STP increased with increase in temperature. Effect of soil moisture was only significant for Mehlich 3 P and Olsen P. Soil temperature had greater impact on STP than soil moisture content. The responses of the Olsen method to temperature differed from the other three methods tested. The interaction between soil temperature and soil moisture on soil test P was only significant for Mehlich 3 P. Fertilization level does not affect the STP in as a clear pattern as the temperature and moisture varied for all four methods. Consistent soil sampling conditions, especially the soil temperature, appear to be the first step to achieve a reliable STP for any soil P test.

Some problem areas connected with soil testing and with reporting soil test and plant analysis results are explored, A uniform system for reporting soil test results is suggested as a step towards further standardization of soil test methods within and among states. Four categories are suggested as a common terminology for soil test and plant analysis results. These are: deficient, probable response, adequate and excessive.

In an ideal crop production system, all nutrient and limestone needs would be determined by evaluating expected return from each input, without required purchases being limited by overall financial resources. More realistically, resources get allocated by priority need, and decisions related to fertilizer and limestone use are judged against other crop production needs, enterprise requirements, and overall farm business goals. This allocation becomes especially pertinent when cash flow is low and financial resources become inadequate. In this situation, and considering all potential inputs, the focus should be on garnering the greatest return to each input dollar expended. Prioritizing fertilizer and lime use should be to those areas that will produce the greatest profit. Following is information to help guide fertilization and liming decisions when funds are simply not available to pay for all desired inputs -- keeping in mind that the goal is on ensuring adequate crop production by addressing critical crop input needs, while at the same time attempting to minimize negative impacts from potentially less than optimal production. Soil Test Information Decisions regarding fertilization and liming are based on information derived from soil test results. Without this information it is not possible to make informed decisions regarding lime or nutrient applications. When finances are limited, using soil tests is the best approach to ensure most successful use of dollars spent on fertilizers and limestone. If soil testing is a traditional component of crop management, then soil test results, along with past

The availability of fertilizer P in the soil decreases with time after fertilization. Routine soil tests or other estimates of plant‐available P should accurately reflect that change. The objective of this study was to determine the effect of the time fertilizer P is in contact with soil on the ability of commonly used soil tests to extract plant‐available P. Experiments were conducted to determine the relationship between available P as determined by A value and Bray and Kurtz P1 (BK), sodium bicarbonate (SB), and Mehlich no. 2 (ME) P soil tests. The A value was assumed to be the best available method of determining plant‐available P. Samples from an acidic Thurman loamy sand topsoil (sandy, mixed, mesic Udorthentic Haplustoll) and a calcareous Uly silt loam subsoil (fine‐silty, mixed, mesic Typic Haplustoll) were incubated with 0, 10, 20, 40, and 80 mg P kg −1 for 4, 8, and 20 mo at field‐capacity water content. After each incubation period, oat ( Avena sativa L.) was planted and grown in the greenhouse with an additional 0, 10, and 20 mg P kg −1 labeled with 32 P. The A value was determined for each incubation period and was correlated with the soil test results. Solubility products were also determined for each incubation P rate and period. After 4 and 8 mo of incubation, the BK, SB, and ME soil tests did not extract plant‐available soil P very accurately using the A value as a standard. After 4 mo of incubation, the SB soil test underestimated available P by as much as 50% on the Uly soil, while the BK soil test overestimated available P on the Thurman soil by 40%. Although there was no improvement after 8 mo of incubation, all three soil tests accurately extracted available P after 20 mo when fertilizer P and soil P appeared to have reached an equilibrium. Solubility equilibria analysis lacked adequate sensitivity to show different soil P compounds as affected by time of incubation. The results indicate large potential P fertilizer recommendation errors when soil samples are taken and analyzed prior to achieving equilibrium between applied fertilizer P and soil P.

This paper examines the potential for extension providers to identify learning opportunities by intentionally surveying farmers attending soil testing workshops designed to improve soil health and its management. In south‐eastern Australia, regional government agencies have been running soil health workshops since 2014, yet they have rarely surveyed the participants to understand their previous experience or learning needs, and how that may inform their design. The workshop consisted of two sessions, separated by 6 weeks. Farmers at the first Session were told how to undertake soil measurements. At the second Session, they then discussed their soil test results. The workshop participants ( n = 87) at four different localities in the Northern Tablelands of New South Wales were surveyed (68% response rate) at each session. Firstly, to examine their prior knowledge and experience of soil testing, and secondly on how they applied what they had learnt, examine if the soil test results matched their expectations, and their influence in land management decisions. The survey revealed to regional government agencies that the majority of survey respondents (62%) would soil test again. Despite more than half of the respondents infrequently or never having their soil tested, prior to the workshop, 50% indicated that the test results were unexpected. The motivation for those farmers who would soil test again was the specific desire to identify their soil's potential for improved production. The survey provided a way of profiling the workshop audience and obtaining important feedback on how to improve the impact of the workshops for participants.

Proper soil sampling is critical for accurate fertilizer recommendations. Samples collected in extremely wet or dry conditions may compromise the integrity of the sample and influence analytical results. We evaluated the effects of six soil moistures, two sampling depths (0–10 and 0–15 cm), and two soil probes on soil core uniformity and soil test results. Moisture treatments encompassed a range from dry (11.2%–18.5% moisture) to saturated conditions in Captina, Dewitt, Calhoun, and Calloway silt loam soils. Core depth and dry core weight were measured in all soils, and pH and Mehlich‐3 extractable P, K, S, and Zn were assessed for Calhoun and Calloway soils. Soil moisture, probe, or their interaction influenced core depth and weight, while chemical properties were significantly affected only by soil moisture. Sampling very dry or saturated soils compromised the collection of uniform cores mainly for the 0‐ to 15‐cm depth. Soil pH tended to increase with increasing moisture, but the mean values fluctuated only ±0.3 units. Across soils and depths, extractable S consistently decreased by 16%–48% as soil moisture at sampling time increased. Phosphorus was affected by soil moisture for 0–15 cm samples in both soils but showed no clear pattern. Soil moisture at the time of sampling affected soil test K for both soils and sample depths with individual cores varying up to 47 mg kg −1 (i.e., 59–106 mg kg −1 ). Greater soil test P and K variability occurred for very dry and wet conditions, which often prohibit collecting samples to the proper depth and could impact fertilizer rate recommendations.

With wheat yields below normal for two consecutive years (1995 and 1996) in most Oklahoma wheat fields due to abnormal weather conditions and disease pressure, soil nutrients probably had accumulated at a level that would allow application of lesser amounts of fertilizers to produce normal yields the following year. A free wheat soil testing and education program was initiated to promote statewide soil testing for improved fertilizer recommendations and for helping farmers to cut wheat production cost. This was offered to wheat producers from June 15 to August 15, 1996 by Oklahoma Cooperative Extension Services. Three thousand and seventy‐nine surface (0–6 inches) and 2,957 subsurface (6–24 inches) soil samples were sent to Oklahoma State University (OSU) Soil, Water and Forage Analytical Laboratory over a two‐month period. Surface soil samples were analyzed for pH, buffer index (BI) if pH was less than 6.5, nitrate‐nitrogen (NO3‐N), available phosphorus (P) index, and available potassium (K) index. Subsurface soil samples were analyzed for NO3‐N only. Twenty‐four informational meetings, with a total attendance of 980, were conducted at the end of the program to help fanners interpret soil testing results and understand fertilizer recommendations. Topics at the meeting also included fertilizer nutrient management, soil‐plant‐nutrient interactions, economics of liming low pH soils. Soil testing results showed that 39% of the wheat fields had soil pH less than 5.5. About 50% and 84% of the fields did not need any P and K, respectively. Significant amounts of NO3‐N were found in subsoil samples. By combining NO3‐N from the surface and subsurface samples, there were 54% of the fields that had NO3‐N greater than 80 lb A‐1 which is enough nitrogen (N) for a yield goal of 40 bu A‐1. Farmers would save more money on fertilizer cost if the fertilizer programs had been based on recommendations from soil testing reports. This program clearly demonstrated the need for regular soil testing and the importance of taking subsoil samples for estimating residual N.

An improved ability to predict pasture dry matter (DM) yield response to applied phosphorus (P), potassium (K) and sulfur (S) is a crucial step in determining the production and economic benefits of fertiliser inputs and the environmental benefits associated with efficient nutrient use. The adoption and application of soil testing can make substantial improvements to nutrient use efficiency, but soil test interpretation needs to be based on the best available and most relevant experimental data. This paper reports on the development of improved national and regionally specific soil test–pasture yield response functions and critical soil test P, K and S values for near-maximum growth of improved pastures across Australia. A comprehensive dataset of pasture yield responses to fertiliser applications was collated from field experiments conducted in all improved pasture regions of Australia. The Better Fertiliser Decisions for Pastures (BFDP) database contains data from 3032 experiment sites, 21 918 yield response measures and 5548 experiment site years. These data were converted to standard measurement units and compiled within a specifically designed relational database, where the data could be explored and interpreted. Key data included soil and site descriptions, pasture type, fertiliser type and rate, nutrient application rate, DM yield measures and soil test results (i.e. Olsen P, Colwell P, P buffering, Colwell K, Skene K, exchangeable K, CPC S, KCl S). These data were analysed, and quantitative non-linear mixed effects models based upon the Mitscherlich function were developed. Where appropriate, disparate datasets were integrated to derive the most appropriate response relationships for different soil texture and P buffering index classes, as well as interpretation at the regional, state, and national scale. Overall, the fitted models provided a good fit to the large body of data, using readily interpretable coefficients, but were at times limited by patchiness of meta-data and uneven representation of different soil types and regions. The models provided improved predictions of relative pasture yield response to soil nutrient status and can be scaled to absolute yield using a specified maximal yield by the user. Importantly, the response function exhibits diminishing returns, enabling marginal economic analysis and determination of optimum fertiliser application rate to a specific situation. These derived relationships form the basis of national standards for soil test interpretation and fertiliser recommendations for Australian pastures and grazing industries, and are incorporated within the major Australian fertiliser company decision support systems. However, the utility of the national database is limited without a contemporary web-based interface, like that developed for the Better Fertiliser Decisions for Cropping (BFDC) national database. An integrated approach between the BFDP and the BFDC would facilitate the interrogation of the database by advisors and farmers to generate yield response curves relevant to the region and/or pasture system of interest and provides the capacity to accommodate new data in the future.

Song, C., Zhang, X., Liu, X. and Chen, Y. 2012. Effect of soil temperature and moisture on soil test P with different extractants. Can. J. Soil Sci. 92: 537–542. Temperature and moisture are important factors affecting adsorption, transformation and the availability of soil phosphorus (P) to plants. The different temperatures and moisture contents at which soil is sampled might affect the results of soil test P (STP). In order to evaluate the effect of the temperature and moisture, as well as the fertilization level, on the results of soil test P, an incubation study involving three soil temperatures (5, 10, and 20°C), and three soil moisture contents (50, 70, 90% of field water-holding capacity) was conducted with Chinese Mollisols collected from four fertilization treatments in a long-term experiment in northeast China. Four soil P test methods, Mehlich 3, Morgan, Olsen and Bray 1 were used to determine STP after a 42-d incubation. The effect of temperature and moisture on STP varied among soil P tests. Averaged across the four fertilization treatments, the temperature had significant impact on STP, while the responses varied among soil P test methods. Mehlich 3, Morgan and Bray 1 STP decreased and Olsen STP increased with increase in temperature. Effect of soil moisture was only significant for Mehlich 3 P and Olsen P. Soil temperature had greater impact on STP than soil moisture content. The responses of the Olsen method to temperature differed from the other three methods tested. The interaction between soil temperature and soil moisture on soil test P was only significant for Mehlich 3 P. Fertilization level does not affect the STP in as a clear pattern as the temperature and moisture varied for all four methods. Consistent soil sampling conditions, especially the soil temperature, appear to be the first step to achieve a reliable STP for any soil P test.

Many producers in the southern Great Plains choose to plant summer crops immediately following the harvest of winter crops to increase farm revenue. The practice, referred to as double cropping, is often done with limited inputs to reduce economic risk. The study aims to evaluate soil testing as an indicator of a nutrient response in double‐crop (DC) soybean [Glycine max (L.) Merr.] across Oklahoma. Nutrient‐rich strips of phosphorus (P) and potassium (K) were applied at 49 sites across Oklahoma in 2016 and 2017. Soil test values across all sites of extractable P and K ranged from 3 to 96 and 56 to 374 mg kg–1, respectively. At crop maturity, subplots were hand harvested from each strip as well as from the farmer practice strip (FPS) collected outside of the plot area. Over half (58%) of the total comparisons throughout this study were noted as those that could have positive yield response: 44 locations for P and 13 for K. Soil test results and critical values were most accurately able to correctly identify the locations that would not respond to the additional nutrient inputs, as out of 98 total comparisons, only seven unexpectedly yielded a significant response. Predicting response was found to be especially difficult, as only six of the 57 comparisons expected to be responsive yielded a response. Unpredictability of responses could be attributed to low‐yielding environments as well as the complexity of soil nutrient pool and plant availability relationship. This work highlights the challenges in predicting the response to nutrients in a DC system based upon soil test analysis.

The use of variable rate technology has become increasingly popular for applying plant nutrient elements. The most widely used method for determining variable fertilizer rates is presently based on soil testing and yield mapping. Three field studies (Bumeyville 1995, Burneyville 1996, and Ardmore 1996) were initiated in established Midland bermudagrass [Cynodon dacrylon (L) Pers.] pastures to determine the relationship between spectral radiance at specific wavelengths with forage nitrogen (N) removal and biomass, and to determine field variability of soil test parameters. Variable N (applied to 1.5 × 2.4 m subplots within 2.4 × 45.7 m main plots), fixed N and check treatments were evaluated at each location. Spectral radiance readings were taken in the red (671±6 nm), green (570±6 nm), and near infrared (NIR) (780±6 nm) wavelengths. The normalized difference vegetation index (NDVI) was calculated as NIR‐red/NIR+red. Variable N rates were applied based on NDVI. The highest fixed variable N rate was set at 224, 336, and 672 kg N ha‐1 for Burneyville, 1995, 1996, and Ardmore, 1996, respectively. At Bumeyville, soil samples were collected in all variable rate plots (1.5 × 2.4 m) and analyzed for various soil test characteristics. NDVI, red, green, and NIR spectral radiance readings were correlated with bermudagrass forage N removal and yield. Correlation of forage yield and N removal with red, NIR, and NDVI were best with maximum forage production, however, when forage production levels were low correlation decreased dramatically for the red wavelength compared with NIR and NDVI. Forage yield and forage N removal in variable rate treatments increased when compared to the check while being equal to the half‐fixed and fixed rates where higher N rates were applied. Also, variability about the mean in variable rate plots was significantly lower than half‐fixed and fixed rates which supports adjusting N rates based on indirect NDVI measurements. Variable N rate plots reduced fertilizer inputs by 60% and produced the same yield as fixed rate plots, while fixed and half‐fixed rates did not increase N content in the forage over that of the variable rate treatment. Soil sample data collected from small consecutive plots (<4 m2) was extremely variable indicating that intense sampling would be needed if variable fertilizer application were to be based on soil test results.

This study was carried out to verify the applicability of variable rate fertilization (VRF) based on soil testing and diagnosis of rice plant growth for high quality rice production of var. Chucheongbyeo at the farm level. The field trials were conducted at Icheon in Gyeonggi province on a 10 ha farm consisting of 45 experimental fields. For comparative study, 15 field trials were carried out adopting fertilizer management (FPM) practices currently used by farmers. FPM fields were managed by each rice grower using current cultivation methods, but in each VRF field fertilizer application was prescribed using soil test results and the amount of N fertilizer for top-dressing at panicle initiation stage was calculated using rice growth value at that stage. In VRF fields, the total amount of N fertilizer application was less (72 kg ha−1) than that in FPM fields (103 kg ha−1). However, the amount of K2O fertilizer application was more in VRF fields (60 kg ha−1) than that in FPM fields (52 kg ha−1). The amount of P2O5 fertilizer application was similar between the VRF and FPM fields. Plant height was significantly shorter and the number of tillers was significantly more at VRF fields than at the FPM fields. Coefficient of variation (CV) of each growth characteristic measured in VRF was lower than that of FPM fields at panicle initiation stage. There was no difference in culm and panicle length and panicle number between them at the grain filling stage, but CV of panicle numbers per m2 decreased in VRF compared with that of the FPM fields. Rice yield was not different between VRF and FPM fields despite higher brown rice recovery and 1,000-grain weight in VRF fields. Under VRF management, head rice yield increased due to an increase in head rice ratio accompanied by a reduction in brown rice protein content and variation of quality characteristics. These results suggest that VRF application based on soil tests and measurement of rice growth value at panicle initiation stage has the potential for quality control and production of high quality rice through increasing uniformity of growth and reducing the variability in quality among individual fields.