Soil Test Phosphorus Research Articles

Impact of in-furrow fertilizers on winter wheat grain yield and mineral concentration

AbstractPlacement of fertilizer in the seed furrow to increase nutrient availability is a common practice in row-crop production. While in-furrow application of fertilizer is widely utilized in the production of winter wheat (Triticum aestivum L.), there is a lack of work evaluating new formulations and nutrient combinations that are available. The objective of this study was to quantify the effects of in-furrow fertilizer products and combinations of products on winter wheat grain yield, nitrogen and mineral concentrations. Trials were conducted across five site-years in central Oklahoma using 11 fertilizer formulations placed in-furrow at the time of planting. In locations that soil test phosphorus (STP) levels or potassium were above sufficiency, the use of in-furrow fertilizers did not improve yield over the control. Inconsistency of response was noted at locations where STP levels were below the critical threshold. While one location showed no response to the addition of P regardless of source, two other locations had significant yield responses from three or more P-containing fertilizers. The addition of both sulphur and zinc resulted in increased yield over the base product at one low STP location. Nutrient concentrations were also influenced in nutrient-limited soils; however, no trends in response were present. Based upon the results of this study, the application of in-furrow fertilizer has the potential to increase winter wheat grain yield and nutrient concentration, when soil nutrients are limiting. As expected the addition of fertilizer when soil test levels are at or above a sufficiency did not increase grain yield.

Influence of soil test phosphorus level and leaching volume on phosphorus leaching

Phosphorus (P) is an essential nutrient in agricultural production, yet its losses contribute to eutrophication in freshwater systems. To mitigate this, there is increasing interest in quantifying soluble P leaching losses and including them in P indices. The objectives of this research were to 1) evaluate water soluble P leaching loss from topsoils (0-15 cm) across a range of initial soil test P (STP) levels; 2) determine the effects of soil type, physiochemical soil properties, and leaching volume on P leaching loss; and 3) determine the predictability of P leaching losses from soil tests. Intact soil cores (3.81 cm in diameter, 0–15 cm deep) were collected from six agricultural fields in Minnesota and leached with deionized water offsite. Additional columns were collected to identify the influence of three leaching volumes on P leaching load. Phosphorus leaching loss was impacted by soil type, initial STP level, and the volume of the leaching event. Leachate P concentration increased with increasing STP level and remained consistent among leaching volumes. Concentrations were not diluted with increased leaching volumes. Consequently, leaching volume was the primary driver of total P load. Several linear and machine learning models were employed to predict P leachate concentration. Phosphorus concentration was best predicted using a Ridge Regression model using two soil tests: the degree of phosphorus sorption (DPSOP) and water extractable P (WEP) (R2 = 0.58, RMSE = 0.06). The model prediction of P leaching loss is promising, although further research on subsoil interactions is needed. This article is protected by copyright. All rights reserved

Challenges in linking soil health to edge-of-field water quality across the Great Lakes basin.

To better understand agricultural nutrient losses, we evaluated relationships between management (e.g., manure and tillage), soil health measurements, and resulting edge-of-field (EOF) surface water quality. This work was conducted before or early into conservation implementation at 14 Great Lakes Restoration Initiative EOF sites spanning Wisconsin, Michigan, Indiana, Ohio, and New York. Analyses of site characteristics (hydroclimate, management, catchment properties) along with 3 yr of soil health measurements (chemical, biological, and physical properties) showed EOF-nutrient export depended on both site and soil properties. A pattern emerged whereby sites not receiving manure and sites with manure defined opposite ends of several gradients for soil and water data. Sites receiving manure had increased microbial activity, organic matter (3.2 vs. 2.7%), and soil test phosphorus (P) (2.8 times more) relative to sites without manure. Suspended sediments (SS), total P (TP), and total nitrogen (TN) in EOF surface runoff varied over three to five orders. Multivariate analysis among sites showed covariant linkages between soil nutrients, soil C, microbial properties, and nutrients in EOF water. There were positive univariate relationships between water-extractable soil P and annual EOF-water concentrations and yields of orthophosphate, TP, TN, and SS (p<.01). Some soil physical properties (e.g., bulk density and infiltration) also covaried among sites but were not consistently related to runoff index or water yield variables. Given the observed among-site variability, we were not able to isolate desirable soil health signals on EOF surface water quality.

Wheat yield prediction by zero sink and equilibrium-type soil phosphorus tests

Diffusive gradients in thin films (DGT) measurements have been shown to outperform other phosphorus (P) tests in soils with strong P sorption, but this has not been confirmed for moderately weathered European soils. We compared the performance of DGT in predicting wheat grain yield in Swedish long-term fertility experiments with those of standard intensity (water-extractable P (P-H 2 O)) and quantity (ammonium lactate-extractable P (P-AL)) tests. A Mitscherlich-type model was used to fit wheat yield response to P application rates (0, 15, 30 or 35, and 45 kg P ha -1 year -1 ) in each individual trial replicate to estimate the maximum yield. For trials with clear plateau-type yield responses and the goodness of fit ( R 2 ) > 0.75, relative yields (RYs) were calculated for each P treatment and plotted against the soil P test results ( n = 143). The goodness of the Mitscherlich-type fits decreased in the following order: DGT-measured P (P-DGT) ( R 2 = 0.35) > P-H 2 O ( R 2 = 0.18) > P-AL ( R 2 = 0.13). When excluding soils with P-AL:P-DGT ≥ 0.1 L g -1 , R 2 was considerably improved to 0.55 for P-AL, 0.46 for P-H 2 O, and 0.65 for P-DGT ( n = 61). At 95% of maximum yield, the upper limit of P deficiency for P-DGT was 44.8 (the soils with P-AL:P-DGT < 0.1 L g -1 ) and 61.9 μg L -1 (all soils), falling within the range reported for other European and Australian soils (6.0–142 μg L -1 ). We show that in the investigated Swedish soils, DGT performed better than the quantity and intensity tests, which is attributed to its ability to capture P diffusion and resupply from the soil solid phase, similar to plant roots in the rhizosphere.

Statistical and machine learning methods for crop yield prediction in the context of precision agriculture

It is of critical importance to understand the relationships between crop yield, soil properties and topographic characteristics for agricultural management. This study’s objective was to compare techniques to quantify the relationship between soil and topographic characteristics for predicting crop yield using high-resolution data and analytical techniques. The study was conducted on a multiple field dataset located in Southwestern Ontario, Canada, where few studies have assessed the impact of applications for precision agriculture and machine learning (ML) to the soil property-yield relationship in this region. The dataset included 145,500 observations of corn and soybean yield, topographic and soil nutrient characteristics. The attributes considered for this study included pH, soil organic matter (OM) content, cation exchange capacity (CEC), soil test phosphorus, zinc (Zn), potassium (K), elevation and topographic wetness index. Multiple linear regression (MLR), artificial neural networks, decision trees and random forests were compared to identify methods able to relate soil properties and crop yields on a subfield scale (2 m). Random forests were the most successful at predicting yield with an R2 value of 0.85 for corn and 0.94 for soybeans. MLR was the least successful with an R2 of 0.40 for corn and 0.45 for soybeans. Cross-validation experiments showed that random forest models in most cases could predict low- and high-yield areas from fields excluded from training datasets, but this was not possible in all cases. Techniques tested the models and identified significant soil and topographic attributes when predicting yield, though the identification was subject to some uncertainty. These results suggest that ML techniques might be used to predict high yield areas of fields without existing yield maps, if those fields have similar relationships of soil properties to yield.

Carbonate accelerated transformation of ferrihydrite in the presence of phosphate

In soils and rhizospheres, iron (oxyhydr)oxides and oxyanions like carbonate and phosphate occur ubiquitously and their interaction have important implications for nutrients and metals cycling. An elevated activity of carbonate in soils and sediments (e.g., pCO2, ∼2%) above current atmospheric CO2 (∼0.04%) is observed. The level of agronomic soil test phosphorus (P) in intensively managed agricultural soils can be up to several hundred mg kg−1. Although it is known that the transformation of iron (oxyhydr)oxides is suppressed by phosphate adsorption, it is unclear how increasing carbonate activities exert the reaction process. Here, the effects of carbonate on the transformation of ferrihydrite were evaluated at pH 7.5 in the presence of phosphate using experimental geochemistry, Fe K-edge X-ray absorption spectroscopy, transmission electron microscopy-energy dispersive X-ray spectroscopy, and Fourier transform infrared spectroscopy. Our results, for the first time, showed that the inhibitory effect of phosphate on ferrihydrite transformation was retarded by carbonate, and it transformed into hematite. Under pCO2 = 408 ppm (0.213 mM carbonate), an increase in [phosphate]o from 0 to 0.5 mM decreased the transformation rate of ferrihydrite from 0.009 to 0.003 d−1. However, the suppression was drastically perturbed when carbonate was added to the phosphate-ferrihydrite system. When 11.42 mM carbonate (pCO2 = 20000 ppmv) was present, the rate increased from 0.009 to 0.033 d−1 without phosphate and from 0.003 to 0.018 d−1 with 0.5 mM of phosphate. Moreover, carbonate promoted the formation of more crystalline rhombic hematite particles, while phosphate modified the morphology of hematite from rhombic to ellipsoidal-like with rough surfaces. The distinctive difference of the influence of carbonate and phosphate on the transformation kinetics, products distribution, and morphologies come from the interaction between carbonate/phosphate and ferrihydrite. Although phosphate formed strong inner-sphere complexes on the ferrihydrite surface, which inhibited the direct contact and dissolution of ferrihydrite, co-adsorbed carbonate still promoted the olation reaction, facilitating the formation of hematite. These results suggest that as an important geochemical process, the increasing activity of carbonate or pCO2 can override the retarding impact of phosphate on the transformation of ferrihydrite and control the occurrence/crystallization of hematite in the subsurface soils under the scenario of climate change.

Thresholds of target phosphorus fertility classes in European fertilizer recommendations in relation to critical soil test phosphorus values derived from the analysis of 55 European long-term field experiments

Phosphorus (P) fertilizer recommendations of individual countries may differ in many aspects, but often the main principle is to reach or maintain a target range of plant-available P in soil. Within this target P fertility class, the soil is expected to supply enough P to the crop, while P fertilization replaces what is exported by the harvested crop. However, the threshold values of the target P fertility classes are based on a multitude of different soil test P (STP) methods and vary by a factor of up to three, even for countries using the same STP method. This study aimed to provide a comparison of the thresholds of target P fertility classes of different European countries and critical soil test P values (Pcrit; STP below which the average relative yield falls below 95% due to P insufficiency) derived from the analysis of data from 55 long-term field experiments in eight European countries. To overcome the issue of diverging STP methods, all values were converted to Olsen-P using empirically based conversion equations from the literature. Converted threshold values varied by a factor of up to five. For the experimental data, we fitted multi-level Mitscherlich-type models to determine Pcrit values of unfertilized soils corresponding to 95% of maximum yield. We found an average Olsen-Pcrit value of 15 mg P kg−1 (adj. R² = 0.37; RMSE = 14.1% relative yield; n = 2368; 55 experiments), which lies far below several country-specific thresholds of target P fertility classes. Crop-specific analyses resulted in higher Olsen-Pcrit values for sugar beet (22 mg P kg−1), potato (19 mg P kg−1) and winter rapeseed (18 mg P kg−1). Among the texture classes (loam, sand, silt and clay), sandy soils exhibited the highest average Olsen-Pcrit value (22 mg P kg−1). We consider a reevaluation of extraordinarily high country-specific thresholds as well as an inclusion of crop type and soil texture (where not already implemented) to be a reasonable measure towards more cost-effective and environment-friendly P fertilization.

Sensitivity and uncertainty analysis for predicted soil test phosphorus using the Annual Phosphorus Loss Estimator model.

In this study we conducted a sensitivity and uncertainty analysis using the Annual P Loss Estimator (APLE) model focusing on model predictions of soil test phosphorus (STP). We calculated and evaluated the sensitivity coefficients of predicted STP and changes in STP using 1- and 10-yr simulations with and without P application. We also compared two methods for estimating prediction uncertainties: first-order variance approximation (FOVA) and Monte Carlo simulation (MCS). Finally, we compared uncertainties in APLE-predicted STP with uncertainties in measured STP collected from multiple sites in Maryland under different manuring and cropping treatments. Results from our sensitivity analysis showed that predicted STP and changes in STP for 1-yr simulations without P inputs were most sensitive to initial STP, whereas model STP predictions were most sensitive to manure and fertilizer application rates when sensitivity analyses included P inputs. For the 10-yr simulations without P application inputs, the range in sensitivity coefficients for crop uptake and precipitation were much greater than for the 1-yr simulations. Prediction uncertainties from FOVA were comparable to those from MCS for model input uncertainties up to 50%. Using FOVA to calculate APLE STP prediction uncertainties using the Maryland data set, the mean measured STP for nearly all site years fell within the 95% confidence intervals of the STP prediction uncertainties. Our results provide users of APLE insight into what model inputs require the most careful measurement when using the model to predict changes in STP under conditions of P drawdown (i.e., no P application) or P buildup.

Comparison of Mehlich-3 and DTPA Soil Tests for Analysis of Micronutrients in Kansas Soils

Mehlich-3 (M3) was designed as a multi-nutrient soil test procedure and has become common at soil testing labs across the U.S. In Kansas, Mehlich-3 is predominately used as a soil test for phosphorus (P) and potassium (K), but recent studies have also investigated the use of M3 for the extraction of base cations and cation exchange capacity estimation. However, data relating M3 to traditional methods for soil micronutrient extraction remain scarce. The objective of this study was to investigate the relationship between M3 and diethylenetriamine pentaacetate (DTPA) extractable copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) across a wide range of Kansas soils. Strong positive correlations were observed between M3 and DTPA for each metal (Fe, r = .91; Zn, r = .98; Cu, r = .92). Correlations between M3 and DTPA were positive but weak for Mn (r = 0.17). Regression analyses suggest these relationships were not one-to-one and were dependent on soil pH. Results from this study show that conversion of M3 to a “DTPA equivalent” is possible but should take soil pH into consideration, especially for Fe and Mn.

Evaluation of Soil Test Phosphorus Extractants and Tissue Analysis for Corn

The objective of this study was to evaluate the relationship of four different soil test phosphorus methods (Mehlich 3, Bray 1, Bray 2, and Haney H3A) for corn production, and determine critical P tissue concentration at different growing stages. The experiment was conducted at 12 locations, and the fertilizer treatments consisted of five phosphorus fertilizer rates applied by broadcast pre-plant. Soil samples were collected at 0- to 6-in. depth, then samples were collected before treatment application by block. Tissue samples were collected at the V6 and R1 growth stages. The relationship between the different soil test phosphorus methods and the R2 varies between 0.24–0.93. Mehlich 3 and Bray 1 have a higher correlation, and Bray 1 and Bray 2 have a lower correlation. Linear plateau determined the critical phosphorus levels for the V6 growth stage was 0.42%, and for the R1 stage was 0.22%. The relationship between the concentration at V6 and R1 was moderately correlated with R2 = 0.62, having a higher phosphorus concentration in the early stage.

SOIL PHOSPHORUS TEST USING A LOW-COST SPECTROPHOTOMETER AND MACHINE LEARNING

Phosphorus concentration is one of the main attributes determined in laboratory analyses of soil samples collected in the field. The objective is to develop a soil phosphorus test using a low-cost spectrophotometer and a machine learning technique. For reflectance measurements, a low-cost system consisting of a Sparkfun AS7625x spectrophotometer and an Arduino Uno is used. Ion exchange resins under standard saturated solutions and modified conditions are used to extract phosphorus ions from the soil samples. Reflectance and phosphorus concentrations determined by the reference method are used in the training and testing of a machine learning. A modification procedure of the ion-exchange resin saturation solution allows the establishment of a strong correlation between the reflectance in 18 spectral bands and P concentration of the soil samples. The obtained model uses five reflectance of the modified resins at wavelengths of 410, 460, 560, 705, and 645 nm to predict the phosphorus concentration. This model presents an R2t accuracy of 0.97 in the training stage with an R2v of 0.96, RMSEv of 9.05, and ratio of prediction to deviation) of 3.81 in the test step.

Comparison of soil phosphorus index systems for grassland in the cross‐border region of Ireland#

AbstractBackgroundThe use of soil phosphorus (P) tests and index systems provides a guide for agronomic nutrient requirements and is frequently also used to estimate risk of P losses to watercourses. Use of soil testing and management based on the results thereof is mandated in some regions. Several P extraction methods are available which evaluate different P pools and are designed for particular soil types. Further to this, index systems categorising specific ranges of plant‐available P, differ. Hence, translation between different tests and index systems is not straightforward. In cross‐border regions, where hydrologic basins encompass more than one political jurisdiction, different tests and rules are implemented in adjacent lands. This can create disparities in land management, confusion as to what legislation applies and obscures the impacts of best management practices at catchment scale.AimsThe aim of this research was to compare the Morgan's and Olsen soil tests used to quantify plant‐available P and the respective index systems, in a border region of the Republic of Ireland (ROI)–Northern Ireland (NI).MethodsOlsen, Morgans and water extractable P (WEP) were evaluated (n = 1,038). Statistical analysis was conducted to derive conversion equations to translate between the statutory test methods and comparison of the respective index categories was performed.ResultsThe conversion equations compared favourably with previous attempts. A stronger relationship was observed between Morgan P and WEP (R2 = 0.60) than between Olsen P and WEP (R2 = 0.45) (including pH and site as interaction factors). The ROI index system was found to indicate lower levels of plant available P in the soil compared to the NI system, for the same soils.ConclusionsThe differences in categorisation of P availability using either index system creates differences in fertiliser recommendations and also perceived aquatic risks even within small cross‐border catchments. This study points to a wider implication for international cross‐border catchments, suggesting that evaluation of the relationships between adjacent national soil index systems is required to achieve harmonised management of shared waterbodies. Neither index system is preferred, but a combination of soil P tests incorporating both agronomic and environmentally oriented analyses may have utility in future decision support tools.

Fuzzy logic based prediction of micronutrients demand for harumanis mango growth cycles

Harumanis is a famous green eating mango cultivar that has been commercially cultivated in Malaysia’s state of Perlis. A variety of nutrients are found in soil, all of which are necessary for plant growth. Micronutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K) are essential for Harumanis mango (Mangifera Indica) to growth. The importance of soil fertility in achieving high plant productivity and quality cannot be overstated. It should be used in a moderate amount and in a balanced manner. Predicting appropriate nutrients and the right timing to satisfy the tree’s demands is critical. The aim of this study is to create for Harumanis mango a fuzzy logic-based system to analyse the results of soil tests for nitrogen (N), phosphorus (P), and potassium (K) in the Harumanis mango orchard. The interpreted data are used to estimate N-P-K nutrient levels and indicate the optimal fertilizer solution and application timing for each Harumanis growth stages. The system utilizes Fuzzy Logic Control (FLC) to predict the nutrients demand for Harumanis mango growth. Results shows the system able to calculate and predict values of required N-P-K fertilizer for optimal growth. Thus, assist farmers in predicting the proper amount of N-P-K to apply to Harumanis mango soil.

Cover Image, Volume 50, Issue 5

On the cover: Water Erosion and Prediction Project rainfall simulator, taken in the FD‐36 watershed supported by field technicians from the USDA‐‐‐‐ARS, Klingerstown, PA. Although rainfall simulation has been used extensively to develop relationships between soil test phosphorus and dissolved phosphorus in runoff, it has major limitations for assessing the risks of legacy P. See D.M. Nash et al., “Estimating dissolved phosphorus losses from legacy sources in pastures: The limits of soil tests and small‐scale rainfall simulators,” https://doi.org/10.1002/jeq2.20265. Photo by Andrew Sharpley. image

A multimethod approach to the study of Classic Maya houselots and land tenure: Preliminary results from the Three Rivers Region, Belize

Results of a recent lidar survey of northwestern Belize revealed a heavily modified anthropogenic landscape populated by dense ancient Maya settlement. Most surprising was the detection of an extensive network of landesque capital in form of linear stone features, with 87.8 linear km identified to date. While likely multifunctional, many of these features appear to delineate residential space and are similar in appearance to contemporary Maya houselots. This paper presents the results of a pilot project designed to collect preliminary data on houselots utilizing multiple research methods. Despite differences in land use and vegetation among the study loci, ground truthing results suggest the lidar data are high fidelity. Excavations produced mixed results, with architectural excavations providing the broadest array of data, including evidence that two of the houselots were occupied during the Late Classic (AD 600 – 850) period. Qualitative soil tests for available phosphorus detected elevated levels in areas that are consistent with middens and kitchen gardens. In addition, microtopographic anomalies were identified that may have represented an important household resource. A synthesis of the available data results in a view of large houselots with broad agricultural resource bases. Finally, settlement density and the ubiquity of demarcated houselots may signal the existence of a restricted-use land tenure system.

Evaluating agronomic soil phosphorus tests for soils amended with struvite

Struvite is an emerging, recycled phosphorus (P) fertilizer of low water solubility (<5%). As a first step towards wide-scale integration of struvite into agricultural systems, distinct interpretation of soil test phosphorus (STP) values for soils amended with struvite may be needed due to the persistence of struvite for months after its application (i.e., residual struvite). However, STP methods were developed for soils amended with highly water-soluble P fertilizers and may not necessarily translate to soils with residual struvite prior to soil testing for P recommendation. We evaluated the potential effects of STP method and edaphic properties (pH, clay content) on STP values for soils with residual struvite. To mimic residual struvite, struvite granules were added to a quartz control and to six soils encompassing a range of pH (4.3, 6.0, 8.1) representative of agricultural soils and with contrasting clay content. The mixtures were then extracted by common STP methods (Mehlich-3, Bray-1, Olsen), Resin, and Haney 3A-2. In the quartz control, dissolution of struvite granules in STP extraction solutions ranged from 59% in Resin to 10% in H3A-2. In soil treatments, apparent dissolution of struvite among STP methods was 19–401% higher for acidic soils with low versus high clay contents. Adsorption experiments confirmed that the disparity in the apparent dissolution of struvite in soils was caused by adsorption of dissolved P on clay minerals. Additionally, for acidic soils with high clay content, scanning electron microscopy coupled with energy dispersive X-ray spectroscopy identified surface deposition of Al and Si on the struvite granule surface during STP extractions, which may have decreased struvite dissolution. Depending on STP method, residual struvite dissolution may overestimate STP concentrations by 20–3900%. Results demonstrate the need to account for the impacts of residual struvite on measured STP values. As a next step, quantifying in situ struvite dissolution rates across soil and cropping conditions is needed to evaluate the extent to which residual struvite may require adjustment of yield-based calibration of STP values.

The influence of lime application on the chemical and physical characteristics of acidic grassland soils with impeded drainage

AbstractSoil acidity and poor nutrient use efficiency are major limiting factors as regards output potential on heavy soils, soils which are dominated by high proportions of clay and organic matter, with impeded drainage, high buffering capacity and located in high rainfall areas. Lime is applied in order to counteract these limiting factors and in turn improve agricultural output and productivity. The current study investigates the effects of two commonly used lime products at three comparable treatment rates, ground lime (7.5, 5 and 2.5 tonne/ha) and granulated lime (7.5, 2.5 and 1.5 tonne/ha), applied across three distinct sites. The ability of each lime product and treatment rate to counteract soil acidity, increase nutrient availability and influence soil physical structure was assessed over time. On average across sites, 1 tonne/ha of each lime product increased soil pH by 0.15 and 0.21 pH units between ground and granulated lime, respectively. Site 3 experienced the greatest increase change in soil pH in comparison to the other two sites, largely due to lower clay content and cation exchange capacity. Granulated lime was 5.7 times more expensive than ground lime in its ability to reduce soil acidity. The high treatment rate showed the greatest reduction in soil acidity, aluminium and iron concentration as a mean across all sites. Morgan's soil test phosphorus concentration increased across all sites, with treatment rates having no effect on the rate of increase. There was evidence of reduced soil compaction and lime application showed no negative implication on soil physical structure.

Plant and soil tests to optimize phosphorus fertilization management of grasslands

Developing more sustainable forage systems requires efficient decision support tools for fertilization management. Soil phosphorus (P) tests have long been used as decision support tools for fertilization management but, more recently, plant nutrition indices using the P concentration of shoot biomass were developed to assess the P nutrition status of grasslands. The objectives of this study were to (i) evaluate the relationship between the phosphorus nutrition index (PNI) and the yield response to P fertilization (ii) analyze relationships between PNI and soil plant-available P (SPAP) indicators, and (iii) evaluate PNI assets for P diagnosis in forage system. Five long-term (≥ 9 years) grassland P fertilization experiments under different soil and climate environments in Canada, Switzerland, France and Romania were used. Three SPAP indicators were tested: CP, the soil solution orthophosphate ions (oPion) concentration (mg P L−1), Olsen P (mg P kg−1), and, a process-based assessment (Qw + Pr) from the sum of oPion in the soil solution (Qw, mg P kg−1) and diffusive oPion with time and CP (Pr, mg P kg−1). PNI was calculated as sward P concentration divided by the critical P concentration.The cumulative effect of P fertilization resulted in a wide range of SPAP values. Overall, CP varied from 0.03 to 3.6 mg P L−1, (Qw + Pr60 min) from 6−52 mg kg−1, and Olsen P from 4−40 mg kg−1. The PNI varied from 48–94% in plots with no applied P, and from 83–121% in P-fertilized plots. A generally positive relationship between relative forage dry matter yield and PNI was established, with a critical PNI value of 92% that distinguishes P-limited and non-P-limited grassland nutrition. Positive relationships between PNI and the three SPAP indicators confirmed that the soil P status influenced the grassland P nutrition status. Critical values on a stock basis for a target PNI value of 92% were similar for Olsen P (12.9 kg P ha−1) and (Qw + Pr60 min) (13.5 kg P ha−1). This study opens perspectives for P diagnosis improvement in forage systems.

Is the Lack of Response of Maize to Fertilization in Soils with Low Bray1-P Related to Labile Organic Phosphorus?

The usual soil test phosphorus (P) neglects the P supply from labile organic P (Po) fractions, which could explain the nonresponse of maize (Zea mays L.) in sites with soil P testing below the critical level. We aim to determine Po and inorganic P (Pi) in NaHCO3 extracts and in the coarse soil fraction (hereinafter, CF) from responsive and nonresponsive sites to P fertilization in maize. We then compare the classification errors of the Cate and Nelson method by comparing the relationship between maize relative yield and the soil Bray1-P concentration vs. the new proposed indices. The study included responsive and nonresponsive sites to P fertilization carried out across the Pampas Region in the center-east of Argentina. Treatments included four P fertilization rates: 0, 12, 24, and 36 kg P ha−1. The experiments were laid out in a randomized complete block design with three replicates. We determined Bray1-P, Pi, and Po in NaHCO3 extracts and in the coarse soil fraction. Sites non-responsive to P fertilization and with Bray1-P concentrations below the critical level showed 70% more Po in the coarse soil fraction (Po-CF) than sites with high crop response and similar Bray1-P level. However, Po-Bic alone did not improve the relationship with maize relative yield. Po-CF and Bray1-P included in a soil integrative P index improved the prediction of crop response to P fertilization and reduced classification errors, which suggests that Po-CF is a source of available P for the crops. The novelty reported in this study was to demonstrate the organic P contribution to relative yield by including it into an integrative soil testing. We find that nonresponsive sites to P fertilization, with low Bray1-P, were correctly classified when including Po-CF in a new soil test P. Improvements in the P fertilization diagnostic prescription tool contribute to an increase in economic profit and reduce environmental impact.

Implications of choosing different interpolation methods: A case study for soil test phosphorus

AbstractMany farmers have their fields grid soil sampled to plan for variable rate P fertilizer application. Grid soil samples are often interpolated to create fertilizer application maps. However, most farmers and other practitioners do not compare interpolation methods. The objective of this study was to evaluate the performance of different grid soil sampling interpolation methods on P fertilizer prescription maps. Grid soil samples were collected from six fields and interpolated via geographically weighted regression (GWR), random forest (RF), and inverse distance weighting (IDW). At four out of six site–years, the root mean square error of soil test P (STP) was 7 to 16% lower for the GWR method compared with the RF method, and GWR identified the low‐STP areas better than RF. Geographically weighted regression may outperform RF and IDW because of its lower error and reduced sensitivity to individual high‐ and low‐STP values. Evaluating multiple interpolation techniques and comparing maps both visually and using global error rates can improve the decisions made by farmers and other practitioners.