EVALUATION OF ALGORITHM THRESHOLDS FOR CROP CANOPY SENSOR-BASED IN-SEASON NITROGEN APPLICATION IN CORN

Nitrogen fertilizer is frequently the most limiting nutrient in corn production. Typically most nitrogen is applied before planting. Since nitrogen can leave the soil system fairly easily, the result can be an inefficient use of nitrogen fertilizer. Previous research has shown increased efficiency with no reduction in yield by applying nitrogen later in the season when the crop is actively growing, with rates regulated spatially through the use of active crop canopy sensors. This study evaluated the potential for N cutoff thresholds using a sufficiency index as the threshold value for areas with poor stand or an unrecoverable N deficiency. In this study the algorithm developed by Solari, et al. (2010) was used. Field scale treatments were imposed on six irrigated fields in south-central and western Nebraska to evaluate performance of the active crop canopy sensor-based in-season N management algorithm with and without predicted permanent yield loss thresholds. The study found no consistent advantage in yield, nitrogen use efficiency, or profit with sensor-based treatments using algorithm thresholds. The uniform, soil-test-based UNL treatment was most often the most profitable treatment. Further research is needed to revise the Solari, et al. (2010) method to account for soil-N supply prior to and following in-season N application. Adviser: Richard Ferguso

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

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

Water effects on optical canopy sensing for late-season site-specific nitrogen management of maize

The interpretation of optical canopy sensor readings for determining optimal rates of late-season site-specific nitrogen application to corn (Zea mays L.) can be complicated by spatially variable water sufficiency, which can also affect canopy size and/or pigmentation. In 2017 and 2018, corn following corn and corn following soybeans were subjected to irrigation×nitrogen fertilizer treatments in west central Nebraska, USA, to induce variable water sufficiency and variable nitrogen sufficiency. The vegetation index-sensor combinations investigated were the normalized difference vegetation index (NDVI), the normalized difference red edge index (NDRE), and the reflectance ratio of near infrared minus red edge over near infrared minus red (DATT) using ACS-430 active optical sensors; NDVI using SRSNDVI passive optical sensors; and red brightness and a proprietary index using commercial aerial visible imagery. Among these combinations, NDRE and DATT were found to be the most suitable for assessing nitrogen sufficiency within irrigation levels. While DATT was the least sensitive to variable water sufficiency, DATT still tended to decrease with decreasing water sufficiency in high nitrogen treatments, whereas the effect of water sufficiency on DATT was inconsistent in low nitrogen treatments. A new method of quantifying nitrogen sufficiency while accounting for water sufficiency was proposed and generally provided more consistent improvement over the mere averaging of water effects as compared with the canopy chlorophyll content index method. Further elucidation and better handling of water-nitrogen interactions and confounding are expected to become increasingly important as the complexity, automation, and adoption of sensor-based irrigation and nitrogen management increase

Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1 ) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops

Assessing factors influencing maize yield response to nitrogen using remote sensing technologies

Nitrogen (N) is a limiting nutrient in maize that is an environmental issue; the result of over or asynchronous application with respect to crop N uptake. Rates are largely determined by a yield goal, which fails to account for spatial and temporal variability in N supply and grain yield. Crop canopy sensors that monitor N status of maize have been validated as a way to increase nitrogen use efficiency (NUE), and maintain yield potential by applying N in-season. Such methods are not immune to the effects of temporal variability that occur beyond the time of application, such as intense rainfall events that are conducive to N loss. To identify potential factors that influence the temporal stability of hybrid respond to N, two different experiments carried out. In the first, blocks represented a range of soil organic matter (OM) and mean relative yield (MRY) values, and received split N application at different timings. Nitrogen, OM, MRY, and timing were evaluated across years for temporal stability and influence on yield. Results showed only MRY was temporally stable; although all factors influenced yield. Sidedress application beyond V14 lost yield. In the second experiment, temporal stability of hybrid response to N (RTN) was evaluated. Hybrids selected represented a broad range of RTN. Hybrid x N interaction was significant across site years, which indicated an inability to classify hybrids based on RTN. A final experiment compared crop canopy sensors from an unmanned aerial vehicle (UAV), to collect more frequent N status of maize, and established best management practices of how to utilize an active crop canopy sensor mounted to a UAV. Results showed that an active crop canopy sensor mounted on a UAV is a suitable platform to replace or augment current methods of acquiring N status of maize canopies. The collective result of experiments showed a lack in temporal stability that exists in terms of N management that is largely influence by local site and seasonal weather. Future research is needed to investigate the interplay of crop canopy reflectance, soil environment, and weather monitoring on a frequent basis to guide N management

Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle

Leaf area index (LAI) and leaf dry matter (LDM) are important indices of crop growth. Real-time, nondestructive monitoring of crop growth is instructive for the diagnosis of crop growth and prediction of grain yield. Unmanned aerial vehicle (UAV)-based remote sensing is widely used in precision agriculture due to its unique advantages in flexibility and resolution. This study was carried out on wheat trials treated with different nitrogen levels and seeding densities in three regions of Jiangsu Province in 2018–2019. Canopy spectral images were collected by the UAV equipped with a multi-spectral camera during key wheat growth stages. To verify the results of the UAV images, the LAI, LDM, and yield data were obtained by destructive sampling. We extracted the wheat canopy reflectance and selected the best vegetation index for monitoring growth and predicting yield. Simple linear regression (LR), multiple linear regression (MLR), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural network (ANN), and random forest (RF) modeling methods were used to construct a model for wheat yield estimation. The results show that the multi-spectral camera mounted on the multi-rotor UAV has a broad application prospect in crop growth index monitoring and yield estimation. The vegetation index combined with the red edge band and the near-infrared band was significantly correlated with LAI and LDM. Machine learning methods (i.e., PLSR, ANN, and RF) performed better for predicting wheat yield. The RF model constructed by normalized difference vegetation index (NDVI) at the jointing stage, heading stage, flowering stage, and filling stage was the optimal wheat yield estimation model in this study, with an R2 of 0.78 and relative root mean square error (RRMSE) of 0.1030. The results provide a theoretical basis for monitoring crop growth with a multi-rotor UAV platform and explore a technical method for improving the precision of yield estimation

Nutrient Management Suggestions for Corn

Fertilizer nutrient requirements for corn are based on expected yield and soil nutrient availability. The preplant nitrogen (N) recommendation equation, with adjustment for fertilizer cost and time of application, is retained from the previous edition of this publication. Suggestions for in-season nitrogen decisions are briefly outlined. The major change is providing a phosphorus (P) recommendation based on yield history with an implied intent to build and maintain soil test P above the critical level, which has not changed

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

Development of Chlorophyll-Meter-Index-Based Dynamic Models for Evaluation of High-Yield Japonica Rice Production in Yangtze River Reaches

Accurate estimation of the nitrogen (N) spatial distribution of rice (Oryza sativa L.) is imperative when it is sought to maintain regional and global carbon balances. We systematically evaluated the normalized differences of the soil and plant analysis development (SPAD) index (the normalized difference SPAD indexes, NDSIs) between the upper (the first and second leaves from the top), and lower (the third and fourth leaves from the top) leaves of Japonica rice. Four multi-location, multi-N rate (0–390 kg ha-1) field experiments were conducted using seven Japonica rice cultivars (9915, 27123, Wuxiangjing14, Wunyunjing19, Wunyunjing24, Liangyou9, and Yongyou8). Growth analyses were performed at different growth stages ranging from tillering (TI) to the ripening period (RP). We measured leaf N concentration (LNC), the N nutrition index (NNI), the NDSI, and rice grain yield at maturity. The relationships among the NDSI, LNC, and NNI at different growth stages showed that the NDSI values of the third and fourth fully expanded leaves more reliably reflected the N nutritional status than those of the first and second fully expanded leaves (LNC: NDSIL3,4, R2 \u3e 0.81; NDSIothers, 0.77 \u3e R2 \u3e 0.06; NNI: NDSIL3,4, R2 \u3e 0.83; NDSIothers, 0.76 \u3e R2 \u3e 0.07; all p \u3c 0.01). Two new diagnostic models based on the NDSIL3,4 (from the tillering to the ripening period) can be used for effective diagnosis of the LNC and NNI, which exhibited reasonable distributions of residuals (LNC: relative root mean square error (RRMSE) = 0.0683; NNI: RRMSE = 0.0688; p \u3c 0.01). The relationship between grain yield, predicted yield, and NDSIL3,4 were established during critical growth stages (from the stem elongation to the heading stages; R2 = 0.53, p \u3c 0.01, RRMSE = 0.106). An NDSIL3,4 high-yield change curve was drawn to describe critical NDSIL3,4 values for a high-yield target (10.28 t ha-1). Furthermore, dynamic-critical curve models based on the NDSIL3,4 allowed a precise description of rice N status, facilitating the timing of fertilization decisions to optimize yields in the intensive rice cropping systems of eastern China