Abstract Sulfur (S) deficiency is a relatively new concern for farmers in industrialized nations due to the decline of atmospheric S deposition in recent decades. Sulfur is mobile in the soil but can be retained in subsoil where it is adsorbed to iron (Fe) and aluminum (Al) oxyhydroxides and aluminosilicate clays, leading to stratification with depth. In this experiment, we evaluated the effectiveness of S fertilizers in a corn ( Zea mays L.)–soybean ( Glycine max L.) rotation, how S concentration varies with depth through time, and how soybean rooting depth interacts with soil profile S distribution. We applied S at a rate of 45 kg ha −1 as gypsum, ammonium sulfate (AMS), poultry litter, or elemental S to corn in the rotation and found that corn yield was similar to an untreated control (10.1 Mg ha −1 mean). We collected 80‐cm soil samples, which revealed increased S in subsoil layers after corn harvest and before soybean planting. During the soybean year of the rotation, we applied the same rate of S using gypsum and AMS to plots that were untreated during the corn year of the rotation. Soybean rooting depth reached the soil profile layers with the highest S concentrations by the R1 growth stage. Soybean yield was unaffected (2.7 Mg ha −1 mean), but grain S and cysteine concentrations increased with S fertilization, whether applied in the corn or soybean year. Results of this study support the development of flexible and economical S fertilization programs spanning multiple years by taking advantage of S storage in the soil profile.

Most public and private phosphorus (P) and potassium (K) fertilizer recommendations are based on soil testing and developed around a critical soil test value (CSTV) to separate responsive and nonresponsive soils. A survey was developed, as part of the Fertilizer Recommendation Support Tool (FRST) project activities, to improve understanding of stakeholder utilization of soil-test-based information for P and K fertilization. The survey consisted of 23 questions and covered topics pertaining to demographics, soil testing goals, the fertilizer decision-making process, fertilizer recommendation philosophy, and FRST use. Respondents were grouped into four categories for analysis: farmers (n = 21), independent crop advisors (n = 84), industry agronomists (n = 104), and public-sector professionals (n = 29). Soil testing was recognized as an important aspect of P and K fertilization decision-making by 91% of respondents; however, less than 8% of respondents in any occupation category reported making P and K fertilization decisions exclusively on soil test information. Yield goal was a key determinant of P and K fertilizer rate decisions for stakeholders, particularly for farmers and industry agronomists. Less than one-half of the farmers, independent crop advisors, and industry agronomist respondents indicated CSTV as being beneficial information to inform rate decisions. The survey results emphasize the importance of strengthening soil-test-based P and K fertilizer recommendations to better reflect stakeholder expectations and align with the key factors influencing on-farm, soil-test-based fertilizer decisions.

Soil test‐based fertilizer recommendations traditionally serve to predict average nutrient needs across fields, but their effectiveness for precision agriculture remains uncertain. Our objectives were to evaluate whether soil phosphorus (P) concentrations predicted corn ( Zea mays ,r L.) yield response to P at the sub‐field level, and to determine if soil test critical levels varied within field boundaries. We conducted research over seven growing seasons at two Kentucky sites collecting spatially dense yield response data from over 150 paired plots per field. Mehlich 3 extractable phosphorus (M3P) soil ranged from 0.8 to 63 mg kg −1 , with 96% of sample points falling below the University of Kentucky's fertilizer cutoff of 30 mg kg −1 M3P for corn. Each plot (10 −2 ha) received 0 or 29.5 kg ha −1 P. While M3P effectively predicted average field‐level response, with yield increases in five of seven site‐years, it failed to predict subfield responses, where only 51% of plots showed positive yield response to P application. Linear plateau models revealed that conventional statistical treatments of soil test correlation data mask important subfield variability. The poor relationship between soil test P and yield response at the subfield scale suggests that variable rate P management requires incorporating additional factors beyond soil P concentration or moving away from such deterministic models toward probabilistic models. Our findings demonstrate that while current soil test recommendations provide accurate field‐scale guidance, they lack the precision required for variable rate application.

Soil fertility and plant nutrition is an applied science that integrates knowledge across all disciplines of soil and plant sciences to effectively and efficiently provide nutrients to plants. Efficient use of nutrients is required not only to maximize agricultural production but also to protect air, soil, and water quality as well as the natural resources involved in providing fertilizers to support agricultural production. This article provides an overview of the essential nutrients, their role in plant growth, behavior in soil, and management in crop production systems.

There is relatively low adoption of winter cover crops across the United States, despite the many ecosystem service benefits they provide, and there has been much debate about corn yield penalties following cereal cover crops such as cereal rye ( Secale cereale L.). This 12 site‐year, coordinated study across a latitudinal gradient in the northeastern United States sought to determine the interactions between cereal rye biomass and fertilizer nitrogen (N) rate and timing on no‐till corn ( Zea mays L.) yield. Total N rates, not the timing of N fertilization, significantly affected corn yields, and higher cereal rye biomass slightly increased corn yields once sufficient N was added. We conclude that if total fertilizer N rates are sufficient, the split between starter N application at planting and sidedress N fertilization does not affect yield in no‐till corn across a range of cereal rye cover crop biomass levels.

Predicting crop nitrogen (N) fertilizer needs is a major challenge in contemporary agriculture. Despite the success of current N recommendation tools, environmental concerns over N pollution from agriculture, and the adoption of improved corn (Zea mays L.) technologies with enhanced N efficiencies highlight the need for more accurate N fertilizer recommendation systems. Here, we aimed to develop a methodology to predict corn N requirements based on delta yield (dY = maximum yield-unfertilized yield). To develop this delta yield-based nitrogen (dY-based N) tool, we selected 486 quadratic-plateau corn yield response to N curves (from 732 N rate trials across northern US) to calculate dY and N fertilizer required to reach the yield plateau (Nx). The economic optimum nitrogen rate (EONR) was calculated using different fertilizer:crop price ratios (PR). The response curve outputs were then partitioned into calibration and validation sets. The calibration set was used to select linear models to predict Nx based on dY, resulting in nine state, agroecosystem region, and irrigation-specific sub-models. These sub-models predicted Nx of the validation set with a mean absolute error (MAE) of 33.0 kg N ha(-1). Predicted values from the site-year quadratic-plateau response fits were used to improve further predictions' outcomes. Predictions of EONR based on dY had a lower MAE than the predictions of Nx, ranging between 19.9 and 25.4 kg N ha(-1) depending on the PR, highlighting the system's predictive power. The exclusion of non-responsive and linear-response trials in our proposed dY-based approach enables future model refinement to improve EONR prediction accuracy across a broader range of yield responses to fertilizer-N rates. The proposed dY-based N system, which integrates both economic and agronomic inputs (including management, environmental effects on soil N supply, and maximum yields), could help to reduce N losses and provide functional benefits for N optimization.

Soil pH and liming recommendations that address the soils, crops, and liming materials have been developed and adopted by land grant universities since the early 20th century. We inventoried land grant institution soil pH and lime requirement (LR) measurement methods for 1980 and 2020 and examined differences in lime rate recommendations for six reference soils using a survey developed by members of the Fertilizer Recommendation Support Tool initiative. Laboratory analysis for six acidic soils with a range of properties was shared with scientists requesting a lime recommendation for each, assuming a 0- to 15-cm soil depth, 6.5 target pH, and lime material having 100% effective calcium carbonate equivalence. Soil pH methods, LR methods, and lime rate recommendations were documented for 48, 41, and 34 states, respectively. The most widely used pH method was a 1:1 soil–water ratio (34 states, 71%). Thirty-one states use one or more buffer solutions to determine LR with the most widely used being the Sikora (10 states), Mehlich (10 states), and Shoemaker, McLean, and Pratt (nine states) buffers. Forty lime rate recommendations from 34 states for each soil were summarized with median rates ranging from 2242 to 9079 kg ha −1 and coefficients of variation ranging from 41% to 73%. The reasons for high LR variability are likely due to different calibrations as no strong trends for LR method or region were observed. Efforts are needed to develop and harmonize lime recommendations to provide accurate and transparent guidance, especially for states sharing common soils and boundaries.

The Fertilizer Recommendation Support Tool (FRST) Project is a collaborative effort involving most land grant institutions, USDA branches, nonprofit organizations, and private industry. The FRST objectives are to develop a soil fertility community of practice, preserve soil test correlation and calibration data in a relational database, and develop a decision tool to provide consistent soil test interpretations. Released in April 2024, the interactive tool acts on an evolving database that contained 1455 P trials, 1316 K trials, and 143 S trials from 44 states and Puerto Rico by March 1, 2025. Decision tool outputs include an interactive county-level map of available data and an estimated critical soil test value. The FRST relational database is a repository for soil-test-based P, K, and S data to support data-driven management recommendations. Continued success of the FRST project and decision tool utility rely on collaboration and support from the soil-test-based nutrient management community.

Crop N decision support tools are typically based on either empirical relationships that lack mechanistic underpinnings or simulation models that are too complex to use on farms with limited input data. We developed an N mineralization model for corn that lies between these endpoints; it includes a mechanistic model structure reflecting microbial and texture controls on N mineralization but requires just a few simple inputs: soil texture soil C and N concentration and cover crop N content and carbon to nitgrogen ratio (C/N). We evaluated a previous version of the model with an independent dataset to determine the accuracy in predictions of unfertilized corn (Zea mays L.) yield across a wider range of soil texture, cover crop, and growing season precipitation conditions. We tested three assumptions used in the original model: (1) soil C/N is equal to 10, (2) yield does not need to be adjusted for growing season precipitation, and (3) sand content controls humification efficiency (epsilon). The best new model used measured values for soil C/N, had a summertime precipitation adjustment, and included both sand and clay content as predictors of epsilon (root mean square error [RMSE] = 1.43 Mg ha-1; r2 = 0.69). In the new model, clay has a stronger influence than sand on epsilon, corresponding to lower predicted mineralization rates on fine-textured soils. The new model had a reasonable validation fit (RMSE = 1.71 Mg ha-1; r2 = 0.56) using an independent dataset. Our results indicate the new model is an improvement over the previous version because it predicts unfertilized corn yield for a wider range of conditions. An improved N mineralization model predicts unfertilized corn yield for a wide variety of conditions. The new model provides realistic estimates of microbial humification efficiency across a range of soil textures. Humification efficiency is affected more by soil clay content than sand content. The updated coefficients account for the influence of precipitation on corn yield. The improved model provides a foundation for site-specific N fertilizer recommendations.

Cadmium (Cd) accumulation in Colombian cacao is a growing concern due to its potential health impacts and EU regulations on Cd content in chocolate products. Furthermore, cacao plays a significant role as an agricultural commodity and a tool for illegal crop replacement, yet our regional understanding of Cd dynamics in cacao cultivation in the north flank of the Sierra Nevada de Santa Marta is still limited. This research provides the first comprehensive investigation of cadmium biogeochemistry in cacao agroecosystems by analyzing the interactions between subsurface soils, topsoil, rock fragments, litter, and cacao leaf Cd concentrations from 30 farms. Results reveal generally low mean total soil Cd concentrations for topsoil and subsurface soils at 0.12 mg kg-1 and 0.05 mg kg(-1), respectively. Leaf and litter Cd concentrations are significantly higher (p < 0.05) than soil Cd, with a mean of 0.42 and 0.4 mg kg(-1), respectively. Our results suggest that age dependent surface-level processes such as the bioaccumulation and biocycling of Cd over time through the leaves, litter, and topsoil, govern Cd in the topsoil, leading to older cultivars and trees exhibiting higher Cd concentrations in leaves, litter, and soils. Subsurface soil Cd is primarily driven by geogenic Cd coming from the weathering of the underlying bedrock with a hypothesized contribution from pedogenic Cd being translocated to deeper soil horizons from the topsoil via clay and oxide illuviation. Our research provides insights into the accumulation of Cd in cacao plants and soils, which can lead to long-term preferential accumulation of cadmium on soil layers and thus increase plant uptake through roots.

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 .

Injecting manure and commercial fertilizer beneath the soil surface is an important nutrient management practice that conserves ammonia-nitrogen (N) but creates distinct bands of N below the soil surface. To date, no widely accepted soil nitrate sampling protocol has been developed to account for the extreme heterogeneity created by injection. To develop sampling recommendations for Pre-Sidedress Nitrate Test (PSNT), we quantified patterns of NO3--N concentrations in soil of corn (Zea mays L.) plots injected with liquid dairy cattle (Bos taurus L.) manure at 76 cm spacing over 2 years. Soil monoliths were collected to allow precise sampling of 30 cm deep by 2.5 cm soil cores from which a mid-season PSNT was determined. Monte Carlo simulation was conducted to simulate the effects of alternative soil sampling protocols on bias and error. Results from the simulation support the following equispaced sampling protocol: five, 30-cm deep soil cores are spaced 15 cm apart and oriented in a line perpendicular to the injected manure bands, collected at four locations in the field, to produce a single composite of 20 samples for NO3- analysis. It is not necessary to know manure band location. As spatially discrete manure application patterns become more prevalent with the expansion of manure injection, we believe this PSNT sampling protocol balances risk of error with practical concerns needed to promote adoption.

Cover crops can be used to provide some of the nitrogen (N) needs of a cash crop to complement mineral fertilizers or manure, but there has yet been limited work to describe corn (Zea mays L.) yield as a function of cover crop quality and N inputs. We investigated the response of corn yield to gradients of both preceding cover crop C:N ratio and poultry litter (PL) application rates in Beltsville, MD during 2012-2014. To achieve different C:N ratios of the cover crops, hairy vetch (Vicia villosa Roth. "Groff") and cereal rye (Secale cereale L. "Aroostook") were seeded in a replacement series of six seeding rate proportions, resulting in shoot C:N ratios of 9.2:1 to 152:1 across years. For each hairy vetch/cereal rye sown proportions, PL was side-dressed at corn V5-V8 stage in subsurface bands (SSB) at four targeted rates: Zero, P-based (67 kg plant available nitrogen [PAN] ha(-1)), N-based (135 kg PAN ha(-1)), and excess N and P (269 kg PAN ha(-1)). We found that corn yield followed a linear-plateau relationship across these two dimensions. Within the linear region, each unit increase in log-scaled cover crop C:N ratio resulted in a yield decrease of 2.56 & PLUSMN; 0.26 Mg ha(-1) at a given rate of SSB PL. To optimize corn yields, we describe a model where each unit increase in log-scaled cover crop C:N ratio required an additional 45.9 & PLUSMN; 6.22 kg PAN ha(-1) from SSB PL. Yields following winter fallow were typically intermediate to the range of yields observed following the gradient of cover crop C:N ratios. We did not find significant differences in corn yield responses when comparing SSB PL to at-planting incorporated or broadcast PL; we also found no significant differences between SSB PL and surface-banded urea ammonium nitrate. Taken together, our approach of modeling yield response across two dimensions can be widely used to guide adaptive N management in subsequent cash crops following winter cover crops, thereby balancing both economic and environmental objectives in cover crop-based cropping systems.

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.

The soiltestcorr R package is an open-source software designed to enable accessible and reproducible computation of correlation analyses between crop yield response to fertilization and soil test values. The package compiles a series of functions for analyzing soil test correlation data: (i) Cate & Nelson data partitioning procedure (graphical and statistical versions), (ii) nonlinear regression analysis (linear-plateau, quadratic-plateau, and Mitscherlich-type exponential models), and (iii) the modified arcsine-log calibration curve. The soiltestcorr enables users to correlate crop response to soil nutrient availability and estimate a critical soil test value and visualize results with ggplot without requiring advanced R programming skills. Finally, a web application that facilitates the use of the package is also offered for users with no background in R programming.

The pre-sidedress soil nitrate test (PSNT) was developed over 30 years ago to determine sidedress nitrogen (N) fertilizer recommendations for corn (Zea mays L.). Since the original PSNT calibrations were developed, changes in production practices such as no-till and cover cropping and increases in corn yields and N use efficiency could affect the accuracy of PSNT recommendations. To update the PSNT recommendations in Pennsylvania and demonstrate the test's efficacy, we compiled a dataset of 32 calibration sites and 13 demonstration sites where the PSNT was conducted, and the economically optimum N rate (EONR) for corn was determined from an N fertilizer yield response curve. We recalibrated the PSNT recommendation algorithm and compared its accuracy to the original calibration. The new calibration resulted in a single long-term manure history factor that interacted with the PSNT result to adjust the sidedress N recommendation (-5.7 kg N ha(-1) [mg NO3-N kg(-1)](-1)). The new calibration also included a term for a mixed species cover crop, which increased the sidedress N recommendation 56.2 kg N ha(-1). Finally, the coefficient that scales the N fertilizer recommendation based on yield goal decreased by 29% from the original calibration to 12.9 kg N Mg-1 grain. The new algorithm for predicting EONR reduced the error of the sidedress N recommendation by one-half compared to the original calibration. The new PSNT calibration will allow users to accurately determine sidedress N recommendations in sites with a long-term manure history and underscores the importance of updating soil fertility recommendation algorithms with modern data.

Cadmium (Cd) is a heavy metal that poses a threat to food safety via the ingestion of food products with Cd. The uptake of Cd by the cacao tree (Theobroma cacao) has gained attention after the European Union set limits for Cd in chocolate products, the main commodity produced from cacao beans. In this study, we analyzed levels of Cd in soils and plant tissues across five cacao farms in the Piura region of Peru to identify the origins of Cd accumulation, and the natural and human factors controlling its concentration. Our results show that Cd levels varied in the order: leaves (1.25 mg kg−1) > beans (0.78 mg kg−1) > soils at 5 cm (0.68 mg kg−1) > soils at 20cm (0.6 mg kg−1). Our findings suggest that the higher concentration of Cd in plant tissues and surface soils can be explained by readily available Cd from fertilizers and a litter layer being absorbed, cycled, and accumulated by the plant. Moreover, even when fertilizers are within regulatory limits, their continuous application, combined with the biocycling of Cd, may lead to high Cd concentrations in beans and leaves. Conversely, farms on alluvial soils and more stable topographic positions display higher soil and plant Cd concentrations. Likewise, farms located at lower altitudes, with higher contributing areas are more likely to receive Cd transported through sediments and water in the river network. Our results also suggest that variations in the underlying geology and soil mineralogy may be a source of potentially Cd bearing sediments. Overall, this study indicates that the high levels of Cd in plants in the study area are the result of a combined mechanism involving plant bioaccumulation and high Cd in fertilizers for the most part, with a minor contribution from potential Cd bearing minerals in sediments of alluvial soils.

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.

Surveys of irrigation water reveal important information about water quality in different geographic regions and can serve as a reference to compare with individual samples submitted to labs. This survey aimed to establish a baseline profile of turfgrass irrigation water quality from nonamended and noneffluent sources in Pennsylvania that can be compared with water quality data from other geographic regions and future surveys in the Mid-Atlantic region, and to improve test report guidelines for samples submitted to labs. The survey was conducted between fall 2007 and spring 2019 and included 190 irrigation water samples. Samples were analyzed at Penn State's Agricultural Analytical Services Laboratory (PSAASL) for pH, alkalinity, hardness, electrical conductivity, total dissolved solids, sodium absorption ratio, bicarbonate, carbonate, residual sodium carbonate, nitrate-N, ammonium-N, and 13 elements. The results showed acceptable irrigation water quality according to published guidelines, with some exceptions, which included high (and potentially problematic) values for sodium, total dissolved solids, and sulfur, but these represented <= 10% of the samples. According to the results, most normal ranges and upper limits for the quality parameters of turfgrass irrigation water should be similar to those currently listed in PSAASL reports. However, the normal ranges for pH, alkalinity, and bicarbonate should be expanded to account for the numerous water samples (> 45%) that exceeded the maximum range listed in test reports. The normal range and upper limit of nitrate-N should be lowered in PSAASL reports to reflect the results and concerns about environmental and health-related effects.

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.