Crop Nitrogen and Phosphorus Utilization following Application of Slurry from Swine Fed Traditional or Low Phytate Corn Diets

Field application of swine (Sus scrofa) slurry provides essential nutrients for crop production. The N to P ratio for slurry is lower than needed by most crops resulting in P accumulation when applied at N rates required for crop growth. Low phytate corn (Zea mays L.) (LPC) contains similar amounts of total P but less phytate P than traditional corn (TC) resulting in improved P bioavailability and reduced P excretion by monogastric animals. While manure from swine-fed LPC diets has a higher N to P ratio than that from TC diets, field studies comparing crop utilization of nutrients from LPC manure have not been conducted. A field study was conducted to compare N and P utilization by no-tillage rainfed sorghum [Sorghum bicolor (L.) Moench.] receiving three annual surface applications of nutrients (inorganic fertilizer, LPC slurry, and TC slurry) and by irrigated corn receiving one incorporated application of nutrients. Sorghum grain and total dry matter N utilization exhibited a year by treatment interaction but total dry matter N utilization was similar for both manure types in all years (61.2 ± 2.6% for TC and 53.8 ± 2.6% for LPC). Grain P utilization was similar for inorganic fertilizer and manure but differed among years (44.4 ± 7.0% in 1999, 25.1 ± 1.4% in 2000, and 57.0 ± 2.2% in 2001). Corn grain N and P utilization did not diff er among nutrient sources in the year of application (50.7 ± 2.4% for N and 40.4 ± 3.0 for P) and increased little in the year following application (62.2 ± 3.0% for N and 50.2 ± 4.5% for P). Crop N and P utilization from LPC manure and TC manure was similar and nutrient guidelines developed for TC swine slurry should also apply for LPC slurry

Using 360-Degree Video for Immersive Learner Engagement

A 360-degree video is a powerful tool that can bring learners into environments that would otherwise be inaccessible. These videos are simultaneously recorded in all directions, allowing the viewer to control viewing direction. Viewers can experience these videos on a computer, smartphone, or tablet or with a virtual reality headset. Camera and software equipment needed to produce 360-degree videos is affordable, allowing Extension educators to produce their own videos. This article addresses the practical aspects of producing 360-degree-video content that can be shared online or in a classroom setting

Cover Crops and Corn Residue Removal: Impacts on Soil Hydraulic Properties and Their Relationships with Carbon

Large-scale crop residue removal may negatively affect soil water dynamics. Integrating cover crop (CC) with crop residue management can be a strategy to offset potential adverse effects of residue removal. We studied: (i) the impact of corn (Zea mays L.) residue removal (56%) with and without the use of winter rye (Secale cereale L.) CC on soil hydraulic properties, (ii) whether CC would ameliorate residue removal effects on hydraulic properties, and (iii) relationships of hydraulic properties with soil organic C (SOC) and other properties under irrigated no-till continuous corn on a silt loam in south central Nebraska after 5 and 6 yr of management. Cover crops did not affect soil hydraulic properties. However, residue removal reduced cumulative water infiltration by about 45% in one year. Across years, residue removal reduced plant available water (PAW) by 32% and mean weight diameter of water-stable aggregates (MWD) by 23% for the upper 5-cm soil depth. Under no CC, residue removal reduced SOC concentration by 25% in the 0- to 5-cm and by 11% in the 5- to 10-cm depths. Under residue removal, CC increased SOC concentration by 18% in the 0- to 5-cm and by 8% in the 5 to 10-cm depths. Cover crop did not completely offset the residue removal-induced decrease in SOC concentration in the upper 5-cm depth. Plant available water decreased as SOC concentration and MWD decreased. After 6 yr, corn residue removal adversely affected soil hydraulic properties and SOC concentration, but CC was unable to fully offset such adverse impacts

Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

Ground-based active sensors have been used in the past with success in detecting nitrogen (N) variability within maize production systems. The use of unmanned aerial vehicles (UAVs) presents an opportunity to evaluate N variability with unique advantages compared to ground-based systems. The objectives of this study were to: determine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize, if UAV’s were a suitable platform, is the UAV and active sensor platform a suitable substitute for current handheld methods, and is there a height effect that may be confounding measurements of N status over crop canopies? In a 2013 study comparing aerial and ground-based sensor platforms, there was no difference in the ability of aerial and ground-based active sensors to detect N rate effects on a maize crop canopy. In a 2014 study, an active sensor mounted on a UAV was able to detect differences in crop canopy N status similarly to a handheld active sensor. The UAV/active sensor system (AerialActive) platform used in this study detected N rate differences in crop canopy N status within a range of 0.5–1.5 m above a relatively uniform turfgrass canopy. The height effect for an active sensor above a crop canopy is sensor- and crop-specific, which needs to be taken into account when implementing such a system. Unmanned aerial vehicles equipped with active crop canopy sensors provide potential for automated data collection to quantify crop stress in addition to passive sensors currently in use

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = (1 + e-15.2829x(RAGDDi-0.1944))-1 - (1 + e-11.6517x(RAGDDi-1.0267))-1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status

Nutrients in the nexus

Synthetic nitrogen (N) fertilizer has enabled modern agriculture to greatly improve human nutrition during the twentieth century, but it has also created unintended human health and environmental pollution challenges for the twentyfirst century. Averaged globally, about half of the fertilizer-N applied to farms is removed with the crops, while the other half remains in the soil or is lost from farmers’ fields, resulting in water and air pollution. As human population continues to grow and food security improves in the developing world, the dual development goals of producing more nutritious food with low pollution will require both technological and socioeconomic innovations in agriculture. Two case studies presented here, one in sub-Saharan Africa and the other in Midwestern United States, demonstrate how management of nutrients, water, and energy is inextricably linked in both small-scale and large-scale food production, and that science-based solutions to improve the efficiency of nutrient use can optimize food production while minimizing pollution. To achieve the needed large increases in nutrient use efficiency, however, technological developments must be accompanied by policies that recognize the complex economic and social factors affecting farmer decision-making and national policy priorities. Farmers need access to affordable nutrient supplies and support information, and the costs of improving efficiencies and avoiding pollution may need to be shared by society through innovative policies. Success will require interdisciplinary partnerships across public and private sectors, including farmers, private sector crop advisors, commodity supply chains, government agencies, university research and extension, and consumers

Nutrients in the nexus

Synthetic nitrogen (N) fertilizer has enabled modern agriculture to greatly improve human nutrition during the twentieth century, but it has also created unintended human health and environmental pollution challenges for the twentyfirst century. Averaged globally, about half of the fertilizer-N applied to farms is removed with the crops, while the other half remains in the soil or is lost from farmers’ fields, resulting in water and air pollution. As human population continues to grow and food security improves in the developing world, the dual development goals of producing more nutritious food with low pollution will require both technological and socioeconomic innovations in agriculture. Two case studies presented here, one in sub-Saharan Africa and the other in Midwestern United States, demonstrate how management of nutrients, water, and energy is inextricably linked in both small-scale and large-scale food production, and that science-based solutions to improve the efficiency of nutrient use can optimize food production while minimizing pollution. To achieve the needed large increases in nutrient use efficiency, however, technological developments must be accompanied by policies that recognize the complex economic and social factors affecting farmer decision-making and national policy priorities. Farmers need access to affordable nutrient supplies and support information, and the costs of improving efficiencies and avoiding pollution may need to be shared by society through innovative policies. Success will require interdisciplinary partnerships across public and private sectors, including farmers, private sector crop advisors, commodity supply chains, government agencies, university research and extension, and consumers

A Novel Waveform to Extract Exercise Gas Exchange Response Dynamics: The Chirp Waveform

Characterizing exercise gas exchange response dynamics reveals important information about physiological control processes and cardiopulmonary dysfunction. However, current methods for extracting exercise response dynamics typically use multiple step-wise transitions, limiting applicability of this technique. PURPOSE: We designed a new protocol (chirp waveform) to extract exercise gas exchange response dynamics in a single visit. We tested the hypothesis that gas exchange response dynamics extracted from chirp forcing would be similar to those extracted from step-wise transitions. METHODS: Thirty-one participants (14 young healthy, 7 older healthy, and 10 patients with chronic obstructive pulmonary disease) visited the laboratory on three occasions. On visit 1, participants performed a ramp incremental test to determine the gas exchange threshold (GET). On visits 2-3, participants performed either a chirp or step-wise protocol in a randomized order. Chirp forcing consisted of sinusoidal fluctuations in work rate with constant amplitude and progressive shortening of sine periods. Square protocol consisted of 3 square-wave transitions each of 6 min duration. Work rate amplitude (from 20 W to ~95% of the individual’s GET) and exercise duration (30 min) were the same in both protocols. The input-output relationship was characterized using a first-order linear transfer function containing a system gain (K) and time constant (τ) [G(s)= K/(τ×s+1)]. Parameter identification was performed in Matlab using the Matlab System Identification toolbox. Agreement between measures was established using Bland-Altman analysis and Rothery’s Concordance Coefficient (RCC). RESULTS: No systematic bias (mean difference of chirp minus square-wave; Δmean) and good reliability was found for V̇O2 K [Δmean: 0.25(1.03) mL/min/W, p=0.179; RCC: 0.773, p=0.004], V̇O2 τ [Δmean: 0.30(7.08) s, p=0.815; RCC: 0.837, p2 K [Δmean: -0.19(1.57) mL/min/W, p=0.512; RCC: 0.827, pp=0.009] and good reliability (RCC: 0.794, p2 τ. CONCLUSION: The chirp waveform allows extraction of gas exchange response dynamics similar to those obtained from standard methods, thus overcoming the need for multiple tests

ESTIMATING MOOSE ABUNDANCE AND TRENDS IN NORTHEASTERN WASHINGTON STATE: INDEX COUNTS, SIGHTABILITY MODELS, AND REDUCING UNCERTAINTY

The state of Washington was historically considered to be unoccupied by moose (Alces alces) with initial colonization in the 1920s primarily in the northeastern quarter of the state. All evidence indicates a steadily increasing population since, with moose and moose hunting now firmly established. Given the expectation that Washington's moose population will face increasing challenges in the coming decades, our monitoring objective is to move from index-counts to valid estimates of abundance. We documented environmental covariates as an adjunct to simple counts from annual helicopter-based surveys in 2002–2012, and examined the performance of existing moose sightability models on these data. While acknowledging our inability to compare modeled estimates with actual abundance, we reasoned that if existing models converged on similar results, this would suggest that moose sightability is a sufficiently general phenomenon that the cost of developing a specific local model might not be justified. However, despite using similar covariates, the sightability models applied to our data produced widely disparate abundances and estimates with poor precision. Specifically, where coniferous forest cover renders expected detection probability low, sightability models tend to behave erratically. We also used covariate data bearing on sampling variation to refine our estimate of population trend. Multiple regression analyses revised the linear rate of increase associated with the raw counts of the instantaneous rate of growth, r = 0.084 (SE = 0.019) to an adjusted estimate of r = 0.077 (SE = 0.075). While incapable of transforming an index into a population estimate, accounting for variables likely to affect raw counts may be useful to refine estimates of trend. The use of an approach that avoids the autocorrelation inherent in a simple regression of counts on time better reflects true uncertainty

Sprinting after having sprinted: Prior high-intensity stochastic cycling impairs the winning strike for gold

Bunch riding in closed circuit cycling courses and some track cycling events are often typified by highly variable power output and a maximal sprint to the finish. How criterium style race demands affect final sprint performance however, is unclear. We studied the effects of 1 h variable power cycling on a subsequent maximal 30 s sprint in the laboratory. Nine well-trained male cyclists/triathletes (O2peak 4.9 ± 0.4 Lmin -1 ; mean ± SD) performed two 1 h cycling trials in a randomized order with either a constant (CON) or variable (VAR) power output matched for mean power output. The VAR protocol comprised intervals of varying intensities (40-135% of maximal aerobic power) and durations (10 to 90 s). A 30 s maximal sprint was performed before and immediately after each 1 h cycling trial. When compared with CON, there was a greater reduction in peak (-5.1 ± 6.1%; mean ± 90% confidence limits) and mean (-5.9 ± 5.2%) power output during the 30 s sprint after the 1 h VAR cycle. Variable power cycling, commonly encountered during criterium and triathlon races can impair an optimal final sprint, potentially compromising race performance. Athletes, coaches, and staff should evaluate training (to improve repeat sprint-ability) and race-day strategies (minimize power variability) to optimize the final sprint