Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 1: Labile Amino‐Nitrogen Concentrations

Moore, David B.; Guillard, Karl; Geng, Xingyuan; Morris, Thomas F.; Brinton, William F.

doi:10.2135/cropsci2018.11.0706

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…This was not unexpected due to the inherent variability associated with SOM mineralization and labile C fraction liberation in soils during and across growing seasons (Barrett et al, 2002). In the companion paper (Moore et al, 2019), binary logistic regression model fits and predictions were better for Solvita Labile Amino‐Nitrogen (SLAN)–N concentrations with the same response variables, further indicating the higher variability associated with a biological test compared with a chemical test. The SLAN test results, however, were compiled across 9 yr as opposed to the 3‐yr evaluations with the SSCB test reported herein.…”

Section: Discussionmentioning

confidence: 96%

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO₂–Burst Carbon Concentrations

et al. 2019

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Current turfgrass fertilizer recommendations do not account for plant‐available soil N mineralized from labile C fractions. The Solvita Soil CO2–Burst (SSCB) test can measure mineralizable C via soil CO2 respiration. This study was conducted across 3 yr (2014–2016) in Connecticut to determine: (i) if SSCB–C concentrations are correlated to responses from predominately Kentucky bluegrass (Poa pratensis L.) and tall fescue [Schedonorus arundinaceus (Schreb.) Dumort.] lawns, and (ii) the probability of turfgrass responses being equal to or greater than responses from common urea rates in relation to SSCB–C concentrations. Randomized complete block design field experiments were set out with 23 rates of organic fertilizer (0–2000 kg N ha−1 yr−1) and four different rates of urea (50, 100, 150, and 200 kg N ha−1 yr−1). Yearly spring soil samples were analyzed for SSCB–C concentrations and correlated with turfgrass responses. Growth and quality responded positively and linearly (P < 0.001) to SSCB–C concentrations, but variability was high and correlations were relatively weak. When spring soil SSCB–C concentrations were ≥91, 113, 166, and 211 mg kg−1, there was a ≥90% probability that overall combined responses across species and variables would be equal to or greater than responses obtained from urea rates of 50, 100, 150, and 200 kg N ha−1 yr−1, respectively. The SSCB test has promise for predicting the probability of soils supporting turfgrass whose performance equals or exceeds benchmark values. This would be helpful in guiding N fertilization, but high variability within the test may limit its predictive ability.

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Section: Discussionmentioning

confidence: 96%

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO₂–Burst Carbon Concentrations

et al. 2019

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“…Therefore, N soil tests are considered unreliable and should not be used to make decisions regarding N fertilizer (Reicher and Throssell 1998). Although some experimental N test methods are undergoing study (Moore et al 2019), none are sufficiently widely used to make N fertilizer recommendations. Therefore, these The three most important factors used when making nutrient application decisions in 2021 were visual observation, weather, and previous product performance (Table 11).…”

Section: Resultsmentioning

confidence: 99%

Nutrient Use and Management Practices on United States Golf Courses

Shaddox

Unruh

Johnson

et al. 2023

hortte

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Nutrient use on United States golf courses increases management costs and has the potential to influence ecosystems. Therefore, it is critical to assess nutrient use and management practices to develop and teach best management practices. The objectives of this survey were to measure nutrient use and management practices on United States golf courses in 2021, and to determine if changes occurred since 2006. A survey was developed and distributed via e-mail to 14,033 United States golf facilities, with 1444 responding. From 2006 to 2021, the total projected nitrogen (N), available phosphorus (P2O5), and soluble potash (K2O) applied declined by 41%, 59%, and 54%, to 54,376, 13,761, and 41,386 tons, respectively. These reductions were attributed to course closures, reduced fertilized acres, reduced application rates, and nutrient use restrictions. The percentage of facilities that did not apply P2O5 increased to 21%, which is likely a result of P2O5 application restrictions. Soil testing was associated with greater application rates of N, P2O5, and K2O. Returning clippings, using precision fertilizer applications, reducing turfgrass acreage, and considering N release from soil organic matter were associated with reduced application rates of P2O5. Golf course superintendents have contributed to nationwide reductions in N, P2O5, and K2O, as evidenced by the reduction in fertilized acres and the reduction in nutrient use rates from 2006 to 2021.

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“…To analyze our soils, we used the Solvita Nexus protocol, a test with a near 30-year commercial history. Samples were processed and analyzed for standard physical and chemical properties 23 , 67 , 68 , and for biological indicators including soil respiration 69 , potentially mineralized N (PMN), and Solvita labile amino nitrogen (SLAN) 70 – 72 . Water soluble organic carbon (WSOC) and water-soluble C:N ratios were as reported by Haney et al (2012) 21 , and water stables aggregates (WSA) as determined by wet sieving according to Jemison et al (2019) 73 .…”

Section: Methodsmentioning

confidence: 99%

Spatial patterns of historical crop yields reveal soil health attributes in US Midwest fields

Fowler,

Basso,

Maureira

et al. 2024

Sci Rep

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Attaining high crop yields and increasing carbon storage in agricultural soils, while avoiding negative environmental impacts on water quality, soil erosion, and biodiversity, requires accurate and precise management of crop inputs and management practices. The long-term analysis of spatial and temporal patterns of crop yields provides insights on how yields vary in a field, with parts of field constantly producing either high yields or low yields and other parts that fluctuate from one year to the next. The concept of yield stability has shown to be informative on how plants translate the effects of environmental conditions (e.g., soil, climate, topography) across the field and over the years in the final yield, and as a valuable layer in developing prescription maps of variable fertilizer rate inputs. Using known relationships between soil health and crop yields, we hypothesize that areas with measured constantly low yield will return low carbon to the soil affecting its heath. On this premises, yield stability zones (YSZ) provide an effective and practical integrative measure of the small-scale variability of soil health on a field relative basis. We tested this hypothesis by measuring various metrics of soil health from commercial farmers’ fields in the north central Midwest of the USA in samples replicated across YSZ, using a soil test suite commonly used by producers and stakeholders active in agricultural carbon credits markets. We found that the use of YSZ allowed us to successfully partition field-relative soil organic carbon (SOC) and soil health metrics into statistically distinct regions. Low and stable (LS) yield zones were statistically lower in normalized SOC when compared to high and stable (HS) and unstable (US) yield zones. The drivers of the yield differences within a field are a series of factors ranging from climate, topography and soil. LS zones occur in areas of compacted soil layers or shallow soils (edge of the field) on steeper slopes. The US zones occurring with high water flow accumulation, were more dependent on topography and rainfall. The differences in the components of the overall soil health score (SHS) between these YSZ increased with sample depth suggesting a deeper topsoil in the US and HS zones, driven by the accumulation of water, nutrients, and carbon downslope. Comparison of the field management provided initial evidence that zero tillage reduces the magnitude of the variance in SOC and soil health metrics between the YSZ.

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Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 1: Labile Amino‐Nitrogen Concentrations

Cited by 6 publications

References 11 publications

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO₂–Burst Carbon Concentrations

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO₂–Burst Carbon Concentrations

Nutrient Use and Management Practices on United States Golf Courses

Spatial patterns of historical crop yields reveal soil health attributes in US Midwest fields

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Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 1: Labile Amino‐Nitrogen Concentrations

Cited by 6 publications

References 11 publications

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO2–Burst Carbon Concentrations

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO2–Burst Carbon Concentrations

Nutrient Use and Management Practices on United States Golf Courses

Spatial patterns of historical crop yields reveal soil health attributes in US Midwest fields

Contact Info

Product

Resources

About

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO₂–Burst Carbon Concentrations

Predicting Cool‐Season Turfgrass Response with Solvita Soil Tests, Part 2: CO₂–Burst Carbon Concentrations