Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM-CERES-Rice.

Nitrogen (N) deficiency can limit rice productivity, whereas the over- and underapplication of N results in agronomic and economic losses. Process-based crop models are useful tools and could assist in optimizing N management, enhancing the production efficiency and profitability of upland rice production systems. The study evaluated the ability of CSM-CERES-Rice to determine optimal N fertilization rate for different sowing dates of upland rice. Field experimental data from two growing seasons (2018-2019 and 2019-2020) were used to simulate rice responses to four N fertilization rates (N30, N60, N90 and a control-N0) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N90 under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N240 to identify the sowing date's specific economic optimum N fertilization rate (EONFR). Results indicated that CSM-CERES-Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94-0.97 and 11-15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha-1 (N180), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha-1 were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM-CERES-Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

2-Methyl-4-chlorophenoxyacetic acid (MCPA) sorption and desorption as a function of biochar properties and pyrolysis temperature.

2-Methyl-4-chlorophenoxyacetic acid (MCPA) is a highly mobile herbicide that is frequently detected in global potable water sources. One potential mitigation strategy is the sorption on biochar to limit harm to unidentified targets. However, irreversible sorption could restrict bioefficacy thereby compromising its usefulness as a vital crop herbicide. This research evaluated the effect of pyrolysis temperatures (350, 500 and 800°C) on three feedstocks; poultry manure, rice hulls and wood pellets, particularly to examine effects on the magnitude and reversibility of MCPA sorption. Sorption increased with pyrolysis temperature from 350 to 800°C. Sorption and desorption coefficients were strongly corelated with each other (R2 = 0.99; P < .05). Poultry manure and rice hulls pyrolyzed at 800°C exhibited irreversible sorption while for wood pellets at 800°C desorption was concentration dependent. At higher concentrations some desorption was observed (36% at 50 ppm) but was reduced at lower concentrations (1-3% at < 5 ppm). Desorption decreased with increasing pyrolysis temperature. Sorption data were analyzed with Langmuir, Freundlich, Dubinin-Radushkevich and Temkin isotherm models. Freundlich isotherms were better predictors of MCPA sorption (R2 ranging from 0.78 to 0.99). Poultry manure and rice hulls when pyrolyzed at higher temperatures (500 and 800°C) could be used for remediation efforts (such as spills or water filtration), due to the lack of desorption observed. On the other hand, un-pyrolyzed feedstocks or biochars created at 350°C could perform superior for direct field applications to limit indirect losses including runoff and leaching, since these materials also possess the ability to release MCPA subsequently to potentially allow herbicidal action.

Linear Support Vector Machine Classification of Plant Stress From Soybean Aphid (Hemiptera: Aphididae) Using Hyperspectral Reflectance.

Spectral remote sensing has the potential to improve scouting and management of soybean aphid (Aphis glycines Matsumura), which can cause yield losses of over 40% in the North Central Region of the United States. We used linear support vector machines (SVMs) to determine 1) whether hyperspectral samples could be classified into treat/no-treat classes based on the economic threshold (250 aphids per plant) and 2) how many wavelengths or features are needed to generate an accurate model without overfitting the data. A range of aphid infestation levels on soybean was created using caged field plots in 2013, 2014, 2017, and 2018 in Minnesota and in 2017 and 2018 in Iowa. Hyperspectral measurements of soybean canopies in each plot were recorded with a spectroradiometer. SVM training and testing were performed using 15 combinations of normalized canopy reflectance at wavelengths of 720, 750, 780, and 1,010 nm. Pairwise Bonferroni-adjusted t-tests of Cohen's kappa values showed four wavelength combinations were optimal, namely model 1 (780 nm), model 2 (780 and 1,010 nm), model 3 (780, 1,010, and 720 nm), and model 4 (780, 1,010, 720, and 750 nm). Model 2 showed the best overall performance, with an accuracy of 89.4%, a sensitivity of 81.2%, and a specificity of 91.6%. The findings from this experiment provide the first documentation of successful classification of remotely sensed spectral data of soybean aphid-induced stress into threshold-based classes.

Impact of variable rate nitrogen and reduced irrigation management on nitrate leaching for potato.

Nitrogen (N) loss from cropping systems has important environmental implications, including contamination of drinking water with nitrate. A 2-yr study evaluated the effects of six N rate, source, and timing treatments, including a variable rate (VR) N treatment based on the N sufficiency index approach using remote sensing, and two irrigation rate treatments, including conventional and reduced rate, on nitrate leaching, residual soil nitrate, and plant N uptake for potato (Solanum tuberosum L. cv. Russet Burbank) production in 2016 and 2017 on a Hubbard loamy sand. Nitrate leaching losses measured with suction-cup lysimeters varied between 2016 and 2017 with flow-weighted mean nitrate N concentrations of 5.6 and 12.8 mg N L-1 , respectively, and increased from 7.1 to 10.4 mg N L-1 as N rate increased from 45 to 270 kg N ha-1 . Despite reductions in N rate of 22 and 44 kg N ha-1 in 2016 and 2017, respectively, for the VR N treatment, there was no significant difference in nitrate leaching compared with the existing N best management practices (BMPs). Reducing irrigation rate by 15% decreased nitrate leaching load by 17% through a reduction in percolation. Residual soil nitrate N in the top 60 cm across all treatments (7.9 mg N kg-1 ) suggests a risk for nitrate leaching during the nongrowing season, and plant N uptake did not explain yearly variation in nitrate leaching and residual soil nitrate. Although existing N BMPs are effective at controlling N losses, development of alternative practices is needed to further reduce the risk of groundwater contamination.

Predicting nitrate-nitrogen loads in subsurface drainage as a function of fertilizer application rate and timing in southern Minnesota.

Fertilizer management practices that focus on applying N fertilizer at the right rate and time have been proposed as a practical option to reduce NO3 -N losses from subsurface drained agricultural fields. In this study, regression equations were developed to predict NO3 -N losses for a corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] rotation in southern Minnesota, using fertilizer application timing and rate and growing season precipitation as inputs. The equations were developed using the results of the field-scale hydrologic and N simulation model DRAINMOD-NII, first calibrated and validated for three sites in southern Minnesota, and then run with different combinations of N fertilizer application rates and timings. Fertilizer timing treatments included a single application in the fall or spring and a split-spring application (half applied preplant and the remaining applied as sidedress). The predictive regression equations showed that the split fertilizer application timing could reduce regional N loads by 28% compared with spring or fall applications. Greater reductions were predicted when the split timing was combined with lower N fertilizer rates. Utilizing the split application timing and reducing the fertilizer rate by 10 and 30% showed 33 and 41% reductions in N loads, respectively, compared with current fertilizer management practices. Such reductions in fertilizer application rates could be achieved through the use of variable-rate nitrogen (VRN) fertilizer technologies. Results of this modeling study indicate that synchronizing fertilizer application with crop requirements and utilizing VRN technologies could significantly reduce N loads to surface waters in southern Minnesota.

Carbon Dosing Increases Nitrate Removal Rates in Denitrifying Bioreactors at Low-Temperature High-Flow Conditions.

Nitrogen losses from croplands contribute to impairment of water bodies. This laboratory experiment evaluated various C sources for use in a denitrifying bioreactor, a conservation practice designed to reduce N losses. The nitrate removal efficiency of candidate treatments (corn cobs [CC], corn cobs with modified coconut coir [CC+MC], corn cobs with modified coconut coir and modified macadamia shell biochar [CC+MC+MBC], wood chips [WC], wood chips with hardwood biochar [WC+BC], and wood chips with continuous sodium acetate addition [WC+A]) were tested with up-flow direction. Effluent was sampled after a repeated weekly flow regime with hydraulic residence times of 1.5, 8, 12, and 24 h. Column temperatures were 15°C for 14 wk (warm), 5°C for 13 wk (cold), and again 15°C for 7 wk (rewarm). Cumulative nitrate N load reduction was greatest for WC+A (80, 80, and 97% during the warm, cold, and rewarm runs, respectively). Corn cob treatments (CC, CC+MC, and CC+MC+MBC) had the second greatest cumulative load reductions for all three temperature experiments, and WC and WC+BC had the lowest performance under these conditions. The nitrate removal rate was optimum at the 1.5-h hydraulic residence time for the WC+A treatment: 43, 30, and 121 g N m d for the warm, cold, and rewarm runs, respectively. Furthermore, acetate addition greatly improved wood chip performance and could be used to enhance nitrate N removal under the cold and high-flow-rate conditions of springtime drainage for the north-central United States.

Beyond the nutrient strategies: Common ground to accelerate agricultural water quality improvement in the upper Midwest.

Nutrients in drainage waters from the Upper Mississippi River Basin states have been a well-documented contributor to the Gulf of Mexico hypoxic zone for decades, and in response, twelve states have developed strategies to address this issue, with Iowa, Minnesota, and Illinois performing rigorous science assessments which estimated nitrogen and phosphorus reduction effectiveness for numerous agricultural non-point source conservation practices. The practices identified in these strategies were compared to identify areas of consensus and discord on nutrient load reduction potentials. Additionally, each practice was assessed for (1) the suitability to stack or be layered with other practices (stackability), (2) the ability to track implementation within a state or regionally (trackability), and (3) the level of production system change required to implement the practice. Overall, there was general consensus among the state strategies in the nutrient load reduction effectiveness of most practices with the exception of cover crops (10%-31% nitrogen reduction) and bioreactors (13%-43% nitrogen reduction). The most effective water quality-improvement practices (i.e., land-use change practices) required relatively more production system changes to agronomic management and were the most trackable (scores: 5, 1-5 scale), although they were also less stackable with other practices (scores: 1 to 1.8; 1-5 scale) and were the least cost effective on a unit area basis (generally $15 to $964 per ha). The most cost effective practices tended to be highly stackable (e.g., nitrogen management: (-)$49 per ha and stackability of 4.7), which indicated that stacking a variety of practices may be the most cost effective use of conservation dollars. The practices that were most difficult to track had relatively lower nitrogen loss reduction effectiveness, but these practices were less costly to implement and required relatively less production system change to agronomic management, two factors of importance to many producers.

Disease risk in a dynamic environment: the spread of tick-borne pathogens in Minnesota, USA.

As humans and climate change alter the landscape, novel disease risk scenarios emerge. Understanding the complexities of pathogen emergence and subsequent spread as shaped by landscape heterogeneity is crucial to understanding disease emergence, pinpointing high-risk areas, and mitigating emerging disease threats in a dynamic environment. Tick-borne diseases present an important public health concern and incidence of many of these diseases are increasing in the United States. The complex epidemiology of tick-borne diseases includes strong ties with environmental factors that influence host availability, vector abundance, and pathogen transmission. Here, we used 16 years of case data from the Minnesota Department of Health to report spatial and temporal trends in Lyme disease (LD), human anaplasmosis, and babesiosis. We then used a spatial regression framework to evaluate the impact of landscape and climate factors on the spread of LD. Finally, we use the fitted model, and landscape and climate datasets projected under varying climate change scenarios, to predict future changes in tick-borne pathogen risk. Both forested habitat and temperature were important drivers of LD spread in Minnesota. Dramatic changes in future temperature regimes and forest communities predict rising risk of tick-borne disease.

Evaluation of the hooghoudt and kirkham tile drain equations in the soil and water assessment tool to simulate tile flow and nitrate-nitrogen.

Subsurface tile drains in agricultural systems of the midwestern United States are a major contributor of nitrate-N (NO-N) loadings to hypoxic conditions in the Gulf of Mexico. Hydrologic and water quality models, such as the Soil and Water Assessment Tool, are widely used to simulate tile drainage systems. The Hooghoudt and Kirkham tile drain equations in the Soil and Water Assessment Tool have not been rigorously tested for predicting tile flow and the corresponding NO-N losses. In this study, long-term (1983-1996) monitoring plot data from southern Minnesota were used to evaluate the SWAT version 2009 revision 531 (hereafter referred to as SWAT) model for accurately estimating subsurface tile drain flows and associated NO-N losses. A retention parameter adjustment factor was incorporated to account for the effects of tile drainage and slope changes on the computation of surface runoff using the curve number method (hereafter referred to as Revised SWAT). The SWAT and Revised SWAT models were calibrated and validated for tile flow and associated NO-N losses. Results indicated that, on average, Revised SWAT predicted monthly tile flow and associated NO-N losses better than SWAT by 48 and 28%, respectively. For the calibration period, the Revised SWAT model simulated tile flow and NO-N losses within 4 and 1% of the observed data, respectively. For the validation period, it simulated tile flow and NO-N losses within 8 and 2%, respectively, of the observed values. Therefore, the Revised SWAT model is expected to provide more accurate simulation of the effectiveness of tile drainage and NO-N management practices.

TMDL implementation in agricultural landscapes: a communicative and systemic approach.

Increasingly, total maximum daily load (TMDL) limits are being defined for agricultural watersheds. Reductions in non-point source pollution are often needed to meet TMDL limits, and improvements in management of annual crops appear insufficient to achieve the necessary reductions. Increased adoption of perennial crops and other changes in agricultural land use also appear necessary, but face major barriers. We outline a novel strategy that aims to create new economic opportunities for land-owners and other stakeholders and thereby to attract their voluntary participation in land-use change needed to meet TMDLs. Our strategy has two key elements. First, focused efforts are needed to create new economic enterprises that capitalize on the productive potential of multifunctional agriculture (MFA). MFA seeks to produce a wide range of goods and ecosystem services by well-designed deployment of annual and perennial crops across agricultural landscapes and watersheds; new revenue from MFA may substantially finance land-use change needed to meet TMDLs. Second, efforts to capitalize on MFA should use a novel methodology, the Communicative/Systemic Approach (C/SA). C/SA uses an integrative GIS-based spatial modeling framework for systematically assessing tradeoffs and synergies in design and evaluation of multifunctional agricultural landscapes, closely linked to deliberation and design processes by which multiple stakeholders can collaboratively create appropriate and acceptable MFA landscape designs. We anticipate that application of C/SA will strongly accelerate TMDL implementation, by aligning the interests of multiple stakeholders whose active support is needed to change agricultural land use and thereby meet TMDL goals.