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Soil testing is the foundation of fertilizer recommendations in the United States. Fertilizer recommendations have primarily been developed by land‐grant universities with limited coordination among programs. The individual state approach to develop fertilizer recommendations has resulted in discrepancies in recommended soil sampling protocols, soil analysis methods, and fertilizer recommendations at similar soil nutrient levels. A national survey was developed to summarize the status of soil testing and fertility work in the United States to inform future collaborative efforts among states and regions and identify opportunities to harmonize recommendation guidelines. Topics included relevant funding, multi‐state collaborations, state soil‐test recommendations and related data, fertilization philosophies, and analytical and soil sampling methods. Responses from 48 states and Puerto Rico showed inconsistencies across state boundaries in every category. The number of faculty full‐time equivalents working in soil fertility now averages 1.3 per state, a 21.5% decrease every 10 years since the 1950s. Land‐grant university soil‐test‐based phosphorus (P) and potassium (K) recommendation philosophies were categorized as Sufficiency (37%), Build and Maintain (19%), hybrid (20%), or multiple philosophies for which recommendations are provided (20%). Respondents in two states did not know the recommendation philosophy (4%). Fertilizer‐P and K recommendations for corn (Zea mays L.) were based on eight different extractants with differences across and within regions. While there have been some successful regional efforts in the past, additional multi‐state collaborative efforts are needed to identify research gaps and develop comprehensive strategies to update soil‐test correlation and calibration data to address modern agronomic, economic, and environmental concerns. Core Ideas: A national survey determined the status of soil fertility recommendations in the United States.This paper focuses on survey results regarding P and K fertilizer recommendations.Soil fertility evaluation methodology, interpretations, and recommendations lack consistency among states.41% of states do not know when their P and K correlations were established or last validated.Multi‐state collaboration and funding for soil fertility research are needed to improve P and K recommendations. [ABSTRACT FROM AUTHOR]

Soil test correlation data are often used to identify a critical soil test value (CSTV), above which crop response to added fertilizer is not expected. Oftentimes, models are used to determine the CSTV from soil test correlation data, yet most commonly used models have inherent assumptions that may not be valid for these data. The arcsine‐log calibration curve (ALCC) was developed in response to the statistical limitations of other commonly used models. A modified ALCC model using standardized major axis regression further improves this model's applicability to soil test correlation data. Here, we describe a Microsoft Excel spreadsheet for calculating CSTV from soil test correlation data using the modified ALCC model. The spreadsheet is available for download providing an accessible and easy‐to‐use tool for those who would like to use this method but who lack the experience with more sophisticated coding programs. The spreadsheet is available for download at http://www.ars.usda.gov/ALCC. Core Ideas: Soil test correlation data are often modeled to identify critical soil test values.Ordinary least squares regression methods are often inadequate for modeling these data.The arcsine‐log calibration curve is an alternative approach.A publicly available spreadsheet increases the accessibility of the arcsine‐log calibration curve method.Attention must be given to influence of extremely high soil test values when using this method. [ABSTRACT FROM AUTHOR]

The Fertilizer Recommendation Support Tool (FRST) will perform correlations between soil nutrient concentrations and crop response to fertilization from user‐selected datasets in the FRST national database. Yield response for the nutrient of interest in a particular site‐year is presented as relative yield (RY), a ratio of unfertilized yield to the maximum attainable yield (A). Several methods exist in the literature for estimating A and calculating RY but the effect of method choice on soil test correlation outcomes is undocumented. We used six published methods to calculate RY from site‐year yield data for five published correlation datasets, and fit a generalized linear plateau (LP) model to each. The critical soil test value (at the LP join point) and RY intercept coefficients were not significantly affected by RY method for any of the datasets, and RY plateau was significantly affected by method for only one dataset. The top options after robust group discussions were the so‐called MAX and FITMAX methods. We selected the MAX method, which defines A as the numerically highest treatment yield mean, as the most appropriate method for FRST because MAX represents maximal yield in responsive sites, is inclusive of trial data having a range of treatment numbers, limits RY to 100% (which allows options for transforming data), and is simpler to implement than FITMAX, which requires a decision tree to calculate RY for diverse trials. Core Ideas: Relative yield needs to be defined for the Fertilizer Recommendation Support Tool.We compared six relative yield calculation methods.Relative yield calculation methods had little effect on estimated critical soil test values.For the tool, we recommend defining relative yield based on the numerical maximum among all treatment yield means. [ABSTRACT FROM AUTHOR]

Soil‐test correlation and calibration data are essential to modern agriculture, and their continued relevance is underscored by the expansion of precision farming and the persistence of sustainable soil management priorities. In support of transparent, science‐based fertilizer recommendations, we seek to establish a core set of required and recommended information for soil‐test P and K correlation and calibration studies, a minimum dataset, building on previous research. The Fertilizer Recommendation Support Tool (FRST) project team and collaborators are developing a national database that will support a soil‐test‐based nutrient management decision aid tool. The FRST team includes over 80 scientists from 37 land‐grant universities, two state universities, one private university, three federal agencies, two private not‐for‐profit organizations, and one state department of agriculture. The minimum dataset committee developed and vetted a robust set of factors fo minimum dataset consideration that includes information on soil sample collection and processing, soil chemical and physical properties, experimental design and statistical analyses, and metadata about the trial, production system, and field management. The minimum dataset provides guidelines for essential information to meet the primary objective of knowledge synthesis, including meta‐analysis and systemic reviews, but permits researchers the flexibility to satisfy local, state, and regional objectives. Ultimately, this consensus‐driven effort seeks to establish a standard that ensures the maximum utility and impact of modern correlation and calibration studies for developing crop nutrition recommendations that improve productivity and profitability for the crop producer, while reducing environmental impacts of nutrient losses. Core Ideas: Data sharing is needed for transparent soil‐test‐based recommendations.A soil‐test correlation and calibration minimum dataset provides a data quality standard.A minimum dataset will facilitate meta‐analysis for nutrient recommendations. [ABSTRACT FROM AUTHOR]

Managing a sustainable dairy farm requires balancing phosphorus (P) imports and exports that enter and leave through the farm gate. Over the long term, P surpluses will elevate soil‐test P concentrations above crop requirements through routine land applications of manure. The objectives of this study were aimed at Virginia dairy farms (a) to determine P mass balances, (b) to define potential guidelines for a sustainable and feasible zone of operation based on P balance and P use efficiency, and (c) to assess risk factors driving P surplus and P use inefficiencies. Data on farm‐gate P imports and exports via feed, manure, crops, fertilizers, bedding, animals, and milk were collected for 58 dairy farms in Virginia. There was no relationship between farm P balance and milk production, indicating that a P surplus was not necessary for good milk productivity. A feasible P balance limit was calculated below which 75% of farms could operate, and this was 18.7 kg P ha−1. Two risk factors were identified for farms having a P balance above this limit: (a) land application of poultry litter and (b) excessive import of P through feed. Combined dairy and beef operations generally had more land and a lower P balance, whereas having combined dairy and poultry did not raise the P balance as long as poultry litter was exported. Dairy farms in Virginia can operate with a sustainable P balance as long as they avoid using excessive poultry litter and pay attention to P imported through purchased feed. [ABSTRACT FROM AUTHOR]