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Phosphorus (P) fertilizer recommendations based on the build and maintain (B&M) or the sufficiency philosophies were compared at six sites across Minnesota. Various levels of soil test P (STP) levels were established over four growing seasons. Applied P and P removed in harvested grain were monitored and used to develop a soil net P balance (Net P). Linear regression of net P with changes in STP was highly significant. Initial STP levels could be maintained at four of the six sites with a negative Net P. At those same sites, a zero Net P would tend to slowly increase STP over time. A sequential soil P fractionation analysis was conducted on soils from the six sites at the initiation and at the end of the 4‐year period. Nine total soil P fractions were extracted that represented both inorganic P (Pi) and organic P (Po) in the labile and non or less labile soil P pools. A positive linear relationship between Net P and changes in Pi fractions was significant. As Net P increased, so did the changes in Pi in the Resin, BiCarb, and NaOH fractions. These three fractions accounted for 66% to >100% of the changes in Net P with Resin and NaOH accounting for the majority of Net P. Changes in Net P that were accounted for in the less labile P pools, Sonic and HCl fractions, variable, and difficult to determine. There was little effect of Net P on Po fractions. Core ideas: Soil net P balance was positively correlated with changes in STP over four growing seasons.Medium STP interpretation class was maintained in most of these soils with a slight negative soil net P balance.In those same soils, a zero‐soil net P balance would tend to raise STP over time.Soil net P balance is positively related to changes in soil P, mostly in the labile and moderately labile fractions. [ABSTRACT FROM AUTHOR]

Tomato is an important field crop, and nutritional imbalances frequently reduce its yield. Diagnosis and Recommendation Integrated System (DRIS), uses ratios for nutrient deficiency diagnosis instead of absolute concentration in plant tests. In this study, local DRIS norms for the field tomatoes were established and the nutrient(s) limiting tomatoes yield were determined. Tomato leaves were analyzed for nutrients, to identify nutritional status using the DRIS approach. One hundred tomatoes fields were selected from Chatter Plain Khyber Pakhtunkhwa and the Sheikupura Punjab Pakistan. The first fully matured leaf was sampled, rinsed, dried and ground for analyzing P, K, Ca, Mg, Cu, Fe, Mn and Zn using an Inductively Coupled Plasma Atomic Emission Spectrophotometer (ICP AES). Plant tissue N and S were measured by the combustion method. The tomatoes yields were recorded at each location. The data were divided into high-yielding (≥3.79 kg/10 plant) and low-yielding (<3.79 kg/10 plant) populations and norms were computed using standard DRIS procedures. High-yielding plant population had a statistically greater mean S and Fe than the low-yielding population. The average balance index, the sum of functions, for S and Fe were -11.04 and -5.17 which reflected deficiency of S and Fe. Plant nutrients norms established may optimize plant nutrition in field tomatoes for high yield. [ABSTRACT FROM AUTHOR]

Field studies conducted over time to collect any type of plant response to a set of treatments are often not treated as repeated measures data. The most used approaches for statistical analyses of this type of longitudinal data are based on separate analyses such as ANOVA, regression, or time contrasts. In many instances, during the review of manuscripts, reviewers have asked researchers to treat year, for example, as a random effect and ignore the interactions between year and other main effects. One drawback of this approach is that the correlation between measurements taken on the same subject over time is ignored. Here, we show that avoiding the covariance between measurements can induce erroneous (e.g., no differences reported when they exist, or differences reported when they actually do not exist) inference of treatment effects. Another issue that has received little attention for statistical inference of multi-year field experiments is the combination of fixed, random, and repeated measurement effects in the same statistical model. This type of analysis requires a more in-depth understanding of modeling error terms and how the statistical software used translates the statistical language of the given command into mathematical computations. Ignoring possible significant interactions among repeated, fixed, and random effects might lead to an erroneous interpretation of the data set. In this manuscript, we use data from two field experiments that were repeated during two and three consecutive years on the same plots to illustrate different modeling strategies and graphical tools with an emphasis on the use of mixed modeling techniques with repeated measures. [ABSTRACT FROM AUTHOR]

Irrigated potato (Solanum tuberosum L.) production requires large inputs of N, and therefore has high potential for N loss including emissions of [N.sub.2]O. Two strategies for reducing N loss include split applications of conventional fertilizers, and single applications of polymer-coated urea (PCU), both of which aim to better match the timing of N availability with plant demand. The objective of this 3-yr study was to compare [N.sub.2]O emissions and potato yields following a conventional split application (CSA) using multiple additions of soluble fertilizers with single preplant applications of two different PCUs (PCU-1 and PCU-2) in a loamy sand in Minnesota. Each treatment received 270 kg of fertilizer N [ha.sup.-1] per season. An unfertilized control treatment was included in 2 of 3 yr. Tuber yields did not vary among fertilizer treatments, but [N.sub.2]O emissions were significantly higher with CSA than PCU-1. During 3 consecutive yr, mean growing season emissions were 1.36, 0.83, and 1.13 kg [N.sub.2]O-N [ha.sup.-1] with CSA, PCU-1, and PCU-2, respectively, compared with emissions of 0.79 and 0.42 kg [N.sub.2]O-N [ha.sup.-1] in the control. The PCU-1 released N more slowly during in situ incubation than PCU-2, although differences in [N.sub.2]O emitted by the two PCUs were not generally significant. Fertilizer-induced emissions were relatively low, ranging from 0.10 to 0.15% of applied N with PCU-1 up to 0.25 to 0.49% with CSA. These results show that N application strategies utilizing PCUs can maintain yields, reduce costs associated with split applications, and also reduce [N.sub.2]O emissions. Abbreviations: AN, ammonium nitrate; BMP, best management practice; CSA, conventional split application; PCU, polymer-coated urea. doi:10.2136/sssaj2009.0126

Potato (Solanum tuberosum L.) production on sandy soils requires added N. Only 40–60% of the applied N is acquired by the crop. Increased N use efficiency (NUE) and its components, N utilization efficiency (NUtE) and N uptake efficiency (NUpE), could reduce fertilizer rates and environmental losses. We compared N efficiency in fresh‐market red potato varieties, in terms of yield and quality traits, and examined potential mechanisms for that efficiency including uptake, utilization, and increased root growth. We grew selections from a red potato breeding population and commercial varieties under two N rates: 101 and 202 kg N ha−1. We compared NUE, NUpE, and NUtE in low and high N. We compared root phenotypes at tuber initiation and yield and skin quality metrics at harvest. Values for NUtE correlated with NUE and yield in low N and NUpE correlated with NUE and yield in high N. Low‐N conditions produced smaller tubers, while high N resulted primarily in medium tubers. Nitrogen did not affect skinning and redness but low N did result in slightly lighter skin color. Total root mass 45 d after planting (DAP) correlated with final yield and NUE but did not correlate with measures of N uptake across treatments. Larger roots correlated with NUpE only in the high‐N treatment. Selection under low N may reveal NUE and expose more stable representations of the genetic components of skin quality phenotypes. While skinning and skin color were more variable among varieties in low N, within variety they exhibited year‐to‐year consistency. [ABSTRACT FROM AUTHOR]

Anaerobic potentially mineralizable nitrogen (PMN) combined with preplant nitrate test (PPNT) or pre‐sidedress nitrate test (PSNT) may improve corn (Zea mays L.) N management. Forty‐nine corn N response studies were conducted across the U.S. Midwest to evaluate the capacity of PPNT and PSNT to predict grain yield, N uptake, and economic optimal N rate (EONR) when adjusted by soil sampling depth, soil texture, temperature, PMN, and initial NH4–N from PMN analysis. Pre‐plant soil samples were obtained for PPNT (0‐ to 30‐, 30‐ to 60‐, 60‐ to 90‐cm depths) and PMN (0‐ to 30‐cm depth) before corn planting and N fertilization. In‐season soil samples were obtained at the V5 corn development stage for PSNT (0‐ to 30‐, 30‐ to 60‐cm depths) at 0 kg N ha−1 at‐planting rate and for PMN when 0 and 180 kg N ha−1 was applied at planting. Grain yield, N uptake, and EONR were best predicted when separating soils by texture or sites by annual growing degree‐days and including PMN and initial NH4–N with either NO3–N test. Using PSNT (mean R2 =.30)‐instead of PPNT (mean R2 =.19)‐based models normally increased predictability of corn agronomic variables by a mean of 11%. Including PMN and initial NH4–N with PPNT or PSNT only marginally improved predictability of grain yield, N uptake, and EONR (R2 increase ≤.33; mean R2 =.35). Therefore, including PMN with PPNT or PSNT is not suggested as a tool to improve N fertilizer management in the U.S. Midwest. [ABSTRACT FROM AUTHOR]

The anaerobic potentially mineralizable N (PMN) test combined with the preplant (PPNT) and presidedress (PSNT) nitrate tests may improve corn (Zea mays L.) N fertilization predictions. Forty‐nine corn N response experiments (mostly corn following soybean [Glycine max (L.) Merr.]) were conducted in the U.S. Midwest from 2014–2016 to evaluate the ability of the PPNT and PSNT to predict corn relative yield (RY) and N fertilizer over‐ and under‐application rates when adjusted by PMN. Before planting and N fertilization, PPNT (0–30, 30–60, and 60–90 cm) and PMN (0–30 cm) samples were obtained. In‐season soil samples were obtained at the V5 development stage for PSNT (0–30, 30–60 cm) in all N rate treatments and PMN (0–30 cm) in only the 0 and 180 kg N ha−1 preplant N treatments. Increasing NO3–N sampling depths beyond 30 cm with or without PMN improved RY predictability marginally (R2 increase up to 0.20) and reduced over‐ and under‐application frequencies up to 14%. Including PMN (preplant only) with PPNT or PSNT improved RY predictability minimally (R2 increase up to 0.10) only for coarse‐ and medium‐textured soils, but N fertilizer over‐ and under‐application frequencies were not substantially reduced (≤12%). These marginal improvements in RY predictability and N fertilizer over‐ and under‐application frequencies, regardless of the variables used (e.g., fertilization, sampling depth, soil texture, and growing degree‐day categories), demonstrate that including PMN with soil NO3–N alone does not improve corn N fertilization need predictions enough to recommend their use. [ABSTRACT FROM AUTHOR]

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received < 183 mm of precipitation or < 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios > 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content > 9.5%, total C < 24.2 g kg−1, soil organic matter (SOM) < 3.9 g kg−1, or C to N ratios < 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management. [ABSTRACT FROM AUTHOR]

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha-1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4--N included with PMNan. Soil was sampled (0-30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha-1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R² ≤ 0.08; RMSE, ≥67 kg N ha-1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4--N as separate explanatory variables improved EONR predictability (11-20%) in fine-textured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations beyond 7 d modestly improved EONR predictability (R² increased ≤0.18, and RMSE was reduced ≤7 kg N ha-1). Alone, PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4--N were relatively low (R² ≤ 0.33; RMSE ≥ 68 kg N ha-1) compared with other tools for N fertilizer recommendations. [ABSTRACT FROM AUTHOR]