<p>Few contemporary studies have examined nutrient accumulation and partitioning of modern corn (Zea mays L.) hybrids grown under in-season sulfur (S) and potassium (K) nutrient management. A fertilizer x hybrid field experiment was conducted during the 2020 and 2021 growing seasons in West Lafayette, Indiana to (1) determine the efficacy of in-season fertilizer management as a strategy to increase grain yield among different modern hybrids, (2) determine the extent to which nutrient harvest index (NutHI) relates to other plant traits under intensive agronomic management, and (3) determine whether grain yield and within-plant nutrient dynamics respond to in-season S and K management differently depending on hybrid. The three fertilizer treatments (whole plot) were (1) Control, no S or K applied, (2) Sulfur, 22.4 kg S ha-1 broadcast applied as ammonium thiosulfate (ATS) [12-0-0-26S] immediately after planting, and (3) Sulfur_Potassium, S (from treatment 2) plus 108 kg K ha -1 topdress applied via Aspire™ [0-0- 58(K2O)-0.5B] at the V4 growth stage. The experiment consisted of four modern Pioneer® corn hybrids (subplot) that varied in crop relative maturity (CRM) from 105 to 114 days planted at the same density following fall strip tillage in a continuous corn production environment. Aboveground dry matter accumulation (DM) and plant tissue concentrations of N, P, K, S, and B were measured via whole-plant samples at V6, R1, and R6. Soil K levels were above the critical K level prior to K application but soil S was not reliably determined. </p> <p><br></p> <p>Grain yields following Sulfur and Sulfur_Potassium treatments were similar, averaging 14.6 Mg ha-1 , which represented a 20% increase over the 12.2 Mg ha-1 observed in the Control. Grain yield gains following S application were the result of increased S uptake, first detected at V6, which alleviated S-deficiency and improved the N to S ratio (N:S) within the plant. K application did not affect grain yield. The greatest contrast in grain yield among hybrids corresponded to extremes in CRM, from 12.9 Mg ha-1 in 105_day to 14.5 Mg ha-1 in 114_day. Fertilizer x hybrid interactions were rarely observed among measured variables with the notable exception of HI, which was increased in later maturity hybrids (111_day and 114_day) by Sulfur and Sulfur_Potassium relative to Control, but was unaffected by fertilizer treatments in 105_day and 110_day hybrids. Most phenotypic parameters were positively influenced by Sulfur and Sulfur_Potassium relative to Control, including above-ground dry matter accumulation (DM), nutrient accumulation, dry matter harvest index (HI), nutrient harvest index (NutHI), and 13 grain/stover nutrient concentrations at maturity. Compared to Control, total plant DM was increased following Sulfur and Sulfur_Potassium by an average of 14% at V6, 16% at R1, and 20% at R6. Whole-plant biomass totals were similar between Sulfur and Sulfur_Potassium, averaging 693 kg DM ha-1 at V6, 10909 kg DM ha-1 at R1, and 26170 kg DM ha-1 at R6; however, K application increased the proportion of total DM partitioned to stover rather than grain, which reduced HI from 0.58 under Sulfur to 0.56 under Sulfur_Potassium. </p> <p><br></p> <p>Nutrient accumulation (of N, P, K, S, and B) was influenced by in-season S and K application at all growth stages relative to the Control. Sulfur application increased V6 wholeplant S and K concentrations, but lowered P and B concentrations. Applying in-season K boosted V6 N and P concentrations, but temporarily reduced K uptake by 10%. Due to gains from S and K application in both DM and nutrient concentration at maturity, total nutrient accumulation in Sulfur_Potassium increased by up to 25% in N, 28% in P, 59% in K, 77% in S, and 44% in B. Total plant DM at maturity was reduced by 14% in the low-yielding 105_day hybrid relative to other hybrids at maturity, leading to similar reductions in nutrient accumulation despite relatively high nutrient concentrations. There was little hybrid variation in whole-plant P, S, and B uptake but substantial hybrid variation in N (9%) and K (21%) uptake. Partitioning of N, P, and K between stover and grain at maturity was influenced by the increased grain nutrient concentrations following Sulfur_Potassium relative to Sulfur. Grain nutrient concentrations under Sulfur relative to Sulfur_Potassium increased from 1.19% N to 1.24% for N, from 0.32 to 0.36% for P, and from 0.39 to 0.44% for K. When Sulfur_Potassium increased grain P and K concentrations over Sulfur, grain nutrient content (i.e., removal) also increased even when grain DM was similar, boosting removal from 48 kg P ha-1 to 54 kg P ha-1 , and from 58 kg K ha-1 to 65 kg K ha-1 . In contrast, grain S and B concentrations, at 0.08% S and 3.3 ppm B, as well as grain contents, at 12.4 kg S ha-1 and 0.05 kg B ha-1 , were similar under both Sulfur and Sulfur_Potassium. As previously stated, NutHI increased substantially in response to both S treatments relative to the Control. The NHI in the Control was just 0.57 versus 0.65 under S treatments while the P harvest index (PHI) was 0.71 versus 0.84, S harvest index (SHI) was 0.50 versus 0.57, and B harvest index (BHI) was 0.35 versus 0.39. Hybrid differences in NutHI were relatively small and were related to both DM and nutrient concentrations at maturity. </p> <p><br></p> <p>While further research is necessary to accurately assess nutrient accumulation and partitioning trends as nutrient management strategies continue to evolve, this study demonstrated 14 that in-season S applications can effectively increase grain yield under S-limiting conditions. Inseason K application did not increase grain yield over Sulfur alone (presumably due to adequate soil K); however, added K still enhanced both grain nutrient removal and NutHI. Furthermore, the influence of plant nutrient concentrations at maturity in both grain (positive) and stover (negative), on NutHI was strongest under fertilizer treatments and NutHI was less dependent on grain yield, total DM, or HI trends. Under hybrid treatments, the influence of nutrient concentrations on NutHI was dependent on DM parameters. While grain yield, DM, and HI were likely to have been contributing factors in NutHI determination, stover nutrient concentration was the most consistent factor related to NutHI across fertilizer and hybrid treatments. The observed variation in NutHI might imply there is still potential for improvement of this index beyond HI alone. Although fertilizer and hybrid treatment responses in whole-plant nutrient concentration were strong during the vegetative period, they were less indicative of NutHI than those at maturity. The positive impact of Sulfur_Potassium on grain nutrient concentration, in the absence of a grain yield response, highlighted a potential disparity between achievable levels of grain nutrient concentration and yield. While in-season nutrient applications can substantially increase grain yields, our results show that in-season fertilizer can also affect nutrient accumulation and partitioning, which are key factors to be considered when making nutrient management decisions. </p>

<p dir="ltr">Routine testing of nutrient concentrations via plant tissue is an important component of in-season fertilizer management in maize (<i>Zea</i> <i>mays </i>L.) cropping systems. Accuracy of results are critical for nitrogen (N), phosphorous (P), potassium (K), and sulfur (S) management, yet there is little scientific guidance on which leaf to sample during mid- to late-vegetative growth stages. Additionally, the whole-plant status of each macro-nutrient may be best represented by a different leaf position due to mobility differences among nutrients. Mobility of each nutrient and allocation within the plant may also be influenced by environmental factors, management strategies, and genotype selection. Field experiments were conducted in West Lafayette and Windfall, Indiana in 2021 and 2022. The objectives were to (1) evaluate N, P, K, and S concentrations of specific leaf positions and whole plants in response to N fertilizer rate (NR), planting density (PD), and genotype (G) treatments at multiple growth stages, and (2) determine the ability of various leaf positions to predict whole-plant concentrations of N, P, K, and S across multiple NR, PD, and G environments. The West Lafayette study compared three NR treatments applied as urea-ammonium nitrate (UAN, 28-0-0) at the V5 growth stage and included (1) Control, no N applied, (2) 151 kg N ha^-1, and (3) 241 kg N ha^-1. The Windfall study compared two side-dress UAN rates of (1) Control, no N applied, and (2) 224 kg N ha^-1 at two planting densities (sub-plot) of 49,400 plants ha^-1and 89,000 plants ha^-1 with 4 Pioneer^® genotypes (sub-sub-plot) including two historical double-cross hybrids and two modern single-crosses. Tissue sampling included the top-collared leaf and whole-plant at V8, the 8^th leaf, top-collared leaf, and whole-plant at V12, and the 8^th leaf, 12^th leaf, ear-leaf, top-collared leaf and whole-plant at R1. Tissue N concentrations were consistently responsive to NR and PD treatments at all stages, but bottom leaves better reflected NR changes. As a mobile nutrient, N concentrations were highest in the uppermost leaf positions by R1 (ear-leaf and top-leaf), yet regressions between individual leaf and whole-plant N% were highest in the lower leaf positions (8^th and 12^th leaf positions). This suggested that the more likely a specific leaf was to exhibit nutrient deficiency symptoms, the better it would be at predicting whole-plant concentrations of that nutrient. Regressions between individual-leaf and whole-plant N% (modern genotypes only) increased from V8 to R1 and regressions were best with the 12^th leaf position at both V12 and R1. Tissue S concentration responses to NR increased at later growth stages, and top-leaf S was a stronger reflection of whole-plant S than the 8^th leaf. Despite S concentration differences among leaf positions at R1, the strength of regressions between each leaf position and whole-plant S were similar. There was no optimal leaf position to represent whole-plant S. While leaf N and S concentrations were above whole-plant concentrations, leaf P and K concentrations exhibited the opposite dynamic. There was little leaf P response to experimental treatment factors, and although regressions for leaf P versus whole-plant P concentrations were far weaker than for N, S or K, the 8^th leaf position was preferred at V12 and R1 (R² of just 0.27 and 0.36, respectively). Potassium concentration response to NR was weak. However, leaf K% and whole-plant K% were highly related via regression, irrespective of NR, at all three stages. Prediction of whole-plant K was strongest with the 8^th leaf at V12 and the 12^th leaf at R1. In summary, optimum leaf sampling position was shown to vary with individual macronutrients and growth stages in maize. Although more research is essential, these preliminary results indicate that traditional sampling methods involving selection of the top fully-expanded leaf from V8 to silking, and the ear-leaf during post-silking stages, may not be the most reliable indicators of whole-plant nutrient status.</p>

<p dir="ltr"><a href="" target="_blank">Watermelon [<i>Citrullus lanatus </i>(Thunb.) Matsum. & Nakai] is one of the world's top three most consumed fruits.</a> Indiana cultivates approximately 7,000 acres of watermelons every year, ranking 6^th in the nation. More than 70% of this production is concentrated in and around Knox County, making Southern Indiana a key region for watermelon production in the States. Despite its significance, watermelon production faces many challenges, including erratic rainfall patterns exacerbated by climate change. Enhanced irrigation management has emerged as a critical strategy in mitigating negative environmental effects and in optimizing fertilizer applications.</p><p dir="ltr">Currently, Southern Indiana farmers have incorporated different irrigation and fertilization practices into watermelon production, yet the effects on production outcomes remain poorly understood. To bridge this gap in knowledge, this study aims to explore the effects of existing practices on watermelon yield and develop irrigation guidelines for optimal production in the region. The experiment was conducted at Southwest Purdue Agricultural Center, Vincennes, Indiana, in 2022 and 2023. Four treatments were applied: High Irrigation, Low Irrigation, No Irrigation, and Fertigation. Fertigation treatment received the same water application as the High Irrigation treatment. Fertilizers were applied pre-plant in the High, Low, and No irrigation treatments, while frequent fertigation was applied to the Fertigation Treatment. Soil moisture sensors measuring volumetric water content were used for irrigation decisions. In 2022, the irrigation thresholds were set at 15% water depletion at 1-ft depth for High Irrigation and Fertigation treatment, and 2-ft depth for Low Irrigation. In 2023, the irrigation threshold for Low Irrigation was adjusted to 40% water depletion at 1-ft depth.</p><p dir="ltr">While soil moisture levels in the bed at the different depths varied notably among treatments, no significant differences in yield by weight were observed. The minimal impact of irrigation on watermelon yield suggests that rainfall provides sufficient water, preventing yield-reducing stress. However, the Fertigation and High Irrigation treatments yielded more fruit than the Low Irrigation and No Irrigation treatments. The dry periods in both years coincided with the watermelon fruit setting stages that may have contributed to the lower fruit set in the Low Irrigation and No Irrigation treatments. Fertigation showed a higher early yield than the other treatments in 2022. Analysis of soil and tissue nitrogen levels indicated that sole nitrogen application before planting could result in excessive soil nitrogen levels during vegetative growth. This excess nitrogen might delay flowering and harvest. This project offers insights into enhancing irrigation and fertilization practices for watermelon production in southern Indiana.</p>

<p dir="ltr">Excess nitrogen loading to surface waters and groundwater from intensive agriculture threatens human and ecosystem health and economic prosperity within and downstream of the Mississippi River Basin. Restoring wetlands and floodplains reduces nitrogen export, but nitrogen export from the Mississippi River Basin remains elevated. Engineering restored wetlands and floodplains to have higher areal denitrification rates is necessary to advance toward nitrogen reduction goals. Environmental controls of denitrification in restored ecosystems must be further investigated to determine under what conditions denitrification is highest and to link these optimal conditions to restoration approaches. Yet, restoration efforts to reduce nitrogen export may inadvertently increase phosphorus export and greenhouse gas emissions. We evaluated different restoration design approaches and identified environmental controls of denitrification, phosphorus release, and greenhouse gas production to advance knowledge of how floodplain and wetland restorations can be designed and managed to maximize denitrification while also constraining phosphorus release and greenhouse gas production. Comparisons of different restoration design approaches in the Wabash River Basin in Indiana, U.S.A., demonstrated that a hydrologically connected floodplain with row crop agriculture provides limited N treatment. Floodplain restorations that involved structural modifications to enhance hydrologic connectivity supported higher denitrification than restorations that only reestablished native vegetation. Investigations of the plot- and field-scale drivers of denitrification indicated that enhanced hydrologic connectivity and specific native wetland and prairie vegetation types were associated with soil conditions that supported high denitrification potential, mainly sufficient soil moisture and bioavailable organic matter. These same soil conditions were associated with increased risks of phosphorus release and greenhouse gas production. However, artificial flooding experiments showed that preventing prolonged flooding has a strong potential to reduce phosphorus export from floodplains with limited impacts on nitrogen treatment. Microcosm experiments with plant litter and wetland soils indicated that certain wetland vegetation types may reduce greenhouse gas production without sacrificing nitrogen removal capacity based on differences in plant biomass composition.</p>

<p dir="ltr">In the Midwest, tile drainage is used to lower water tables and remove excess water from the soil to improve crop production. This network of underground pipes (i.e., tiles) and expansive agriculture also increases nutrient export, contributing to ecological harm in local lakes and rivers and further downstream in the Gulf of Mexico. Conservation practices that avoid, control, or trap nutrients can mitigate these losses, but studies quantifying their impact at the watershed scale are challenging. This work uses water quality monitoring data collected throughout the Midwest to identify potential nutrient sources and pathways, the hydroclimatic variables influencing them, and the effects of conservation practices. In a study in northeast Indiana, nutrient travel times for total phosphorus, soluble reactive phosphorus, nitrate, and dissolved organic carbon were observed to be faster during winter storm events, likely due to a lack of vegetative processes. Tile drains were the primary contributor to in-stream nitrogen and phosphorus during spring storms but were not a primary contributor for phosphorus in the winter. Data from nitrate sensors across the Midwest were used to quantify the effect of sampling frequency on hysteresis and flushing indices, showing that sampling intervals greater than 8 hours estimates could lead to inaccurate values, and that caution should be used when interpreting outcomes when using longer sampling intervals. Wet antecedent conditions were associated with a dilution pattern of nitrate during storm events, and tile drainage exacerbates this by causing greater leaching during wet periods. A systematic review of water quality monitoring studies at the watershed scale showed the limits using current data, and suggested how providing better statistics could be used to facilitate a more robust meta-analysis to determine effect sizes and sources of heterogeneity among studies. In a monitoring study located in the central Indiana, agricultural conservation practices reduced nitrate concentrations by 27% in an artificially drained watershed. While tile drainage is a critical pathway for nutrients in the Midwest, the combined effect of various conservation practices can improve water quality at the watershed scale.</p>

<p>Field-edge buffers are a promising way to address nonpoint source pollution from agricultural runoff, but concentrated runoff flow often renders standard fixed-width linear buffers ineffective. AgBufferBuilder (ABB) is a tool within ESRI ArcMap Geographic Information Systems software that designs and evaluates targeted, nonlinear buffers based on hydrologic modeling and other field-specific parameters. We tested ABB on n=45 Areas of Interest (AOIs) stratified based on estimated sediment loading across three sub-watersheds within Michigan’s Saginaw Bay watershed to evaluate the effectiveness of ABB relative to existing practices across a wide range of landscape conditions. We modeled tractor movement around ABB buffer designs to assess more realistic versions of the likely final designs. ABB regularly failed to deliver the desired 75% sediment capture rate using default 9 m x 9 m output raster resolution, with Proposed buffers capturing from 0% to 68.49% of sediment within a given AOI (mean=37.56%). Differences in sediment capture between Proposed and Existing buffers (measured as Proposed – Existing) ranged from -48% to 66.81% of sediment (mean=24.70%). Proposed buffers were estimated to capture more sediment than Existing buffers in 37 of 45 AOIs, representing potential for real improvements over Existing buffers across the wider landscape. In 13 of 45 AOIs, ABB buffers modified for tractor movement captured more sediment than Existing buffers using less total buffer area. We conducted a collaborative design process with three Saginaw Bay watershed farmers to assess their willingness to implement ABB designs. Feedback indicated farmers may prefer in-field erosion control practices like cover cropping and grassed waterways over field-edge ABB designs. More farmer input is needed to better assess farmer perspectives on ABB buffers and to identify preferred data-based design alternatives. Engineered drainage systems with raised ditch berms and upslope catch basins piped underground directly into ditches were encountered several times during site visits. ABB only models surface flow and does not recognize drain output flow entering waterways. Modified ABB functionality that models buffers around drain inlets would greatly improve its functionality on drained sites. This may be accomplishable through modification of user-entered AOI margins but requires further investigation. Unfortunately, the existing tool is built for outdated software and is not widely accessible to non-expert users. We suggest that an update of this tool with additional functionality and user accessibility would be a useful addition in the toolbox of conservation professionals in agricultural landscapes.</p>

<p>Current watershed-scale, nonpoint source (NPS) pollution models do not represent the processes and impacts of agricultural best management practices (BMP) on water quality with sufficient detail. To begin addressing this gap, a novel process-based, watershed-scale, water quality model (WEPP-WQ) was developed based on the Water Erosion Prediction Project (WEPP) and the Soil and Water Assessment Tool (SWAT) models. The proposed model was validated at both hillslope and watershed scales for runoff, sediment, and both soluble and particulate forms of nitrogen and phosphorus. WEPP-WQ is now one of only two models which simulates BMP impacts on water quality in ‘high’ detail, and it is the only one not based on USLE sediment predictions. Model validations indicated that particulate nutrient predictions were better than soluble nutrient predictions for both nitrogen and phosphorus. Predictions of uniform conditions outperformed nonuniform conditions, and calibrated model simulations performed better than uncalibrated model simulations. Applications of these kinds of models in real-world, historical simulations are often limited by a lack of field-scale agricultural management inputs. Therefore, a prototype tool was developed to derive management inputs for hydrologic models from remotely sensed imagery at field-scale resolution. At present, only predictions of crop, cover crop, and tillage practice inference are supported and were validated at annual and average annual time intervals based on data availability for the various management endpoints. Extraction model training and validation were substantially limited by relatively small field areas in the observed management dataset. Both of these efforts contribute to computational modeling research and applications pertaining to agricultural systems and their impacts on the environment.</p>