Variable-rate nitrogen fertilization of winter wheat under high spatial resolution

Variable-rate application (VRA) addresses in-field variation in soil nitrogen (N) availability and crop response, and as such is a tool for more effective site-specific management. This study assessed the performance of a VRA system for on-the-go delivery of granular fertilizer in 7-m wide and 200-m long strips of a 2.4-ha wheat field. A randomized complete block design consisted of three treatment strips (a preplant uniform application of 100 kg N/ha, a preplant + in-season uniform farmer rate of 212 kg N/ha and a preplant + in-season VRA) within four blocks. The VRA prototype consisted of Crop Circle ACS-430 active canopy sensors, a GeoScout X data logger that processed the geospatial data to convey a real-time N rate signal (1 Hz) to a Gandy Orbit Air 66FSC spreader through a Raven SCS 660 controller. Crop monitoring included analysis of in-season soil and plant samples, water balance and grain yield. VRA delivered an economic optimum N rate using 72% less in-season N or 38% less total N (131 kg N/ha) than that applied by the farmer (212 kg N/ha). The reduction of total N inputs came about without any yield losses and translated to 58% N-use efficiency in comparison to 44% of the farmer practice and 52% of the preplant control. VRA also provided a much higher revenue over fertilizer costs, €68/ha and €118/ha higher than the preplant control and the farmer practice, respectively. The return of VRA per unit of N was equal to that of the large preplant application due to low leaching losses. Overall, the high-resolution VRA was superior in terms of environmental benefits and profitability with the least uncertainty to the farmer.     

Impact of Residual Soil Nitrate on In-Season Nitrogen Applications to Irrigated Corn Based on Remotely Sensed Assessments of Crop Nitrogen Status

Spatial and temporal variability of soil nitrogen (N) supply together with temporal variability of plant N demand make conventional N management difficult. This study was conducted to determine the impact of residual soil nitrate-N (NO₃-N) on ground-based remote sensing management of in-season N fertilizer applications for commercial center-pivot irrigated corn (Zea mays L.) in northeast Colorado. Wedge-shaped areas were established to facilitate fertigation with the center pivot in two areas of the field that had significantly different amounts of residual soil NO₃-N in the soil profile. One in-season fertigation (48 kg N ha⁻¹) was required in the Bijou loamy sand soil with high residual NO₃-N versus three in-season fertigations totaling 102 kg N ha⁻¹ in the Valentine fine sand soil with low residual NO₃-N. The farmer applied five fertigations to the field between the wedges for a total in-season N application of 214 kg N ha⁻¹. Nitrogen input was reduced by 78% and 52%, respectively, in these two areas compared to the farmer’s traditional practice without any reductions in corn yield. The ground-based remote sensing management of in-season applied N increased N use efficiency and significantly reduced residual soil NO₃-N (0–1.5 m depth) in the loamy sand soil area. Applying fertilizer N as needed by the crop and where needed in a field may reduce N inputs compared to traditional farmer accepted practices and improve in-season N management.     

Development and validation of fuzzy logic inference to determine optimum rates of N for corn on the basis of field and crop features

A fuzzy inference system (FIS) was developed to generate recommendations for spatially variable applications of N fertilizer. Key soil and plant properties were identified based on experiments with rates ranging from 0 to 250 kg N ha⁻¹ conducted over three seasons (2005, 2006 and 2007) on fields with contrasting apparent soil electrical conductivity (EC_a), elevation (ELE) and slope (SLP) features. Mid-season growth was assessed from remotely sensed imagery at 1-m² resolution. Optimization of N rate by the FIS was defined against maximum corn growth in the weeks following in-season N application. The best mid-season growth was in areas of low EC_a, high ELE and low SLP. Under favourable soil conditions, maximum mid-season growth was obtained with low in-season N. Responses to N fertilizer application were better where soil conditions were naturally unfavourable to growth. The N sufficiency index (NSI) was used to judge plant N status just prior to in-season N application. Expert knowledge was formalized as a set of rules involving EC_a, ELE, SLP and NSI levels to deliver economically optimal N rates (EONRs). The resulting FIS was tested on an independent set of data (2008). A simulation revealed that using the FIS would have led to an average N saving of 41 kg N ha⁻¹ compared to the recommended uniform rate of 170 kg N ha⁻¹, without a loss of yield. The FIS therefore appears to be useful for incorporating expert knowledge into spatially variable N recommendations.     

Leaf nitrogen estimation in potato based on spectral data and on simulated bands of the VENμS satellite

Relationships between leaf spectral reflectance at 400–900 nm and nitrogen levels in potato petioles and leaves were studied. Five nitrogen (N) fertilizer treatments were applied to build up levels of nitrogen variation in potato fields in Israel in spring 2006 and 2007. Reflectance of leaves was measured in the field over a spectral range of 400–900 nm. The leaves were sampled and analyzed for petiole NO₃–N and leaf percentage N (leaf-%N). Prediction models of leaf nitrogen content were developed based on an optical index named transformed chlorophyll absorption reflectance index (TCARI) and on partial least squares regression (PLSR). Prediction models were also developed based on simulated bands of the future VENμS satellite (Vegetation and Environment monitoring on a New Micro-Satellite). Leaf spectral reflectance correlated better with leaf-%N than with petiole NO₃–N. The TCARI provided strong correlations with leaf-%N, but only at the tuber-bulking stage. The PLSR analysis resulted in a stronger correlation than TCARI with leaf-%N. An R ² of 0.95 (p < 0.01) and overall accuracy of 80.5% (Kappa = 74%) were determined for both vegetative and tuber-bulking periods. The simulated VENμS bands gave a similar correlation with leaf-%N to that of the spectrometer spectra. The satellite has significant potential for spatial analysis of nitrogen levels with inexpensive images that cover large areas every 2 days.     

Use of hyperspectral data to assess the effects of different nitrogen applications on a potato crop

This study was conducted to explore whether hyperspectral data could be used to discriminate between the effects of different rates of nitrogen application to a potato crop. The field experiment was carried out in the Central Potato Research Station, Jalandhar, on seven plots with different nitrogen (N) treatments. Spectral reflectance was measured using a 512-channel spectroradiometer with a range of 395–1075 nm on two different dates during crop growth. An optimum number of bands were selected from this range based on band–band r ², principal component analysis and discriminant analysis. The four bands that could discriminate between the rates of N applied were 560, 650, 730, and 760 nm. An ANOVA analysis of several narrow-band indices calculated from the reflectance values showed the indices that were able to differentiate best between the different rates of N application. These were reflectance ratio at the red edge (R_740/720) and the structure insensitive pigment index (SIPI). To estimate leaf N, reflectance ratios were determined for each band combination and were evaluated for their correlation with the leaf N content. A regression model for N estimation was obtained using the reflectance ratio indices at 750 and 710 nm wavelengths (F-ratio = 32 and r ² = 0.551, P < 0.000).     

Evaluation of mid-season sensor based nitrogen fertilizer recommendations for winter wheat using different estimates of yield potential

Optical sensors, coupled with mathematical algorithms, have proven effective at determining more accurate mid-season nitrogen (N) fertilizer recommendations in winter wheat. One parameter required in making these recommendations is in-season grain yield potential at the time of sensing. Four algorithms, with different methods for determining grain yield potential, were evaluated for effectiveness to predict final grain yield and the agronomic optimum N rate (AONR) at 34 site-years. The current N fertilizer optimization algorithm (CNFOA) outperformed the other three algorithms at predicting yield potential with no added N and yield potential with added N (R² = 0.46 and 0.25, respectively). However, no differences were observed in the amount of variability accounted for among all four algorithms in regards to predicting the AONR. Differences were observed in that the CNFOA and proposed N fertilizer optimization algorithm (PNFOA), under predicted the AONR at approximately 75 % of the site-years; whereas, the generalized algorithm (GA) and modified generalized algorithm (MGA) recommended N rates under the AONR at about 50 % of the site-years. The PNFOA was able to determine N rate recommendations within 20 kg N ha^?1 of the AONR for half of the site-years; whereas, the other three algorithms were only able recommend within 20 kg N ha^?1 of the AONR for about 40 % of the site-years. Lastly, all four algorithms reported more accurate N rate recommendations compared to non-sensor based methodologies and can more precisely account for the year to year variability in grain yields due to environment.     

Evidence of dependence between crop vigor and yield

A recent paper in Precision Agriculture concluded that algorithms to calculate in-season fertilizer nitrogen (N) recommendations need to include yield and fertilizer response considerations because grain yield and yield response index are independent of each other. The authors used maximum and zero N yields from selected long-term wheat and maize studies to support their conclusion. Yields from plots receiving intermediate N rates in the maize study indicated a probable dependence between grain yield and yield response index, which is contrary to the authors’ conclusions. Data from a more recent, long-term irrigated maize study on a similar soil type were used to illustrate that grain yield and yield response index are definitely dependent on each other and further that the in-season sensor-based sufficiency index is highly correlated with relative yield. The implication is that a yield component, as such, does not necessarily need to be included in development of an in-season N recommendation algorithm.     

南安市马铃薯测土配方施肥指标的研究

2006~2008年在福建省南安市进行了马铃薯田间施肥"3414"多点试验,结果表明:施用N、P、K肥仍是提高马铃薯产量和增加收益的重要途径。不同施肥处理的马铃薯增产增收效果表现为NPKNKP,单位肥料增产效果和收益表现为NPK,投产比表现为NPK。土壤肥力高低对马铃薯施肥效果有较大的影响。南安市马铃薯田间土壤碱解氮、有效磷、速效钾含量低级的指标分别为100、19.6、36 mg/kg,中级的指标分别为65~120、14.8~52.6、36~133 mg/kg,高级的指标分别为120、52.6、133 mg/kg。马铃薯的推荐施肥量随土壤碱解氮、有效磷、速效钾含量的升高而降低,马铃薯获得最高产量的全市平均施N量为(195.33±22.67)kg/hm2,N∶P2O5∶K2O为1∶0.36∶1.19;最佳经济施肥量为:N(192.60±20.26)kg/hm2、P2O5(66.06±13.23)kg/hm2、K2O(210.13±57.28)kg/hm2。     

Developing a new Crop Circle active canopy sensor-based precision nitrogen management strategy for winter wheat in North China Plain

Active canopy sensor (ACS)—based precision nitrogen (N) management (PNM) is a promising strategy to improve crop N use efficiency (NUE). The GreenSeeker (GS) sensor with two fixed bands has been applied to improve winter wheat (Triticum aestivum L.) N management in North China Plain (NCP). The Crop Circle (CC) ACS-470 active sensor is user configurable with three wavebands. The objective of this study was to develop a CC ACS-470 sensor-based PNM strategy for winter wheat in NCP and compare it with GS sensor-based N management strategy, soil N_min test-based in-season N management strategy and conventional farmer’s practice. Four site-years of field N rate experiments were conducted from 2009 to 2013 to identify optimum CC vegetation indices for estimating early season winter wheat plant N uptake (PNU) and grain yield in Quzhou Experiment Station of China Agricultural University located in Hebei province of NCP. Another nine on-farm experiments were conducted at three different villages in Quzhou County in 2012/2013 to evaluate the performance of the developed N management strategy. The results indicated that the CC ACS-470 sensor could significantly improve estimation of early season PNU (R² = 0.78) and grain yield (R² = 0.62) of winter wheat over GS sensor (R² = 0.60 and 0.33, respectively). All three in-season N management strategies achieved similar grain yield as compared with farmer’s practice. The three PNM strategies all significantly reduced N application rates and increased N partial factor productivity (PFP) by an average of 61–67 %. It is concluded that the CC sensor can improve estimation of early season winter wheat PNU and grain yield as compared to the GS sensor, but the PNM strategies based on these two sensors perform equally well for improving winter wheat NUE in NCP. More studies are needed to further develop and evaluate these active sensor-based PNM strategies under more diverse on-farm conditions.     

Use of a virtual-reference concept to interpret active crop canopy sensor data

Active crop canopy sensors make possible in-season fertilizer nitrogen (N) applications by using the crop as a bio-indicator of vigor and N status. However, sensor calibration is difficult early in the growing season when crops are rapidly growing. Studies were conducted in the United States and Mexico to evaluate procedures to determine the vegetation index of adequately fertilized plants in producer fields without establishing a nitrogen-rich reference area. The virtual-reference concept uses a histogram to characterize and display the sensor data from which the vegetation index of adequately fertilized plants can be identified. Corn in Mexico at the five-leaf growth stage was used to evaluate opportunities for variable rate N fertilizer application using conventional tractor-based equipment. A field in Nebraska, USA at the twelve-leaf growth stage was used to compare data interpretation strategies using: (1) the conventional virtual reference concept where the vegetation index of adequately fertilized plants was determined before N application was initiated; and (2) a drive-and-apply approach (no prior canopy sensor information for the field before initiating fertilizer application) where the fertilizer flow-rate control system continuously updates a histogram and automatically calculates the vegetation index of adequately fertilized plants. The 95-percentile value from a vegetation-index histogram was used to determine the vegetation index of adequately fertilized plants. This value was used to calculate a sufficiency index value for other plants in the fields. The vegetation index of reference plants analyzed using an N-rich approach was 3–5 % lower than derived using the virtual-reference concept.     

基于“3414”试验模型的沿海砂质土壤马铃薯氮、磷、钾效应研究

2009年在福建省平潭县具有代表性的土壤类型上进行了马铃薯"3414"试验,结果表明:施用氮肥的效应最好,增产达101.5%;磷肥增产16.7%;钾肥增产16.1%。采用二次多项式逐步回归的方法对试验产量结果进行肥料效应回归模型模拟,得出的3点试验田肥料效应回归模型全部为氮、磷、钾三元二次多项式回归模型,经显著性检验达到极显著水平,由此提出了马铃薯的最佳施肥量。     

Sensing and control system for spot-application of granular fertilizer in wild blueberry field

An automated sensing and control system (hardware and software) was developed for real-time spot-application of granular fertilizer in mowed wild blueberry fields. The custom hardware system was incorporated into a commercial pneumatic granular fertilizer spreader. Custom software for the sensing and control system was developed by combining color co-occurrence matrix based texture analysis and g-ratio algorithms in C++ to acquire and process images in real-time to differentiate mowed wild blueberry plants from bare spots and weeds. The performance accuracy of the spot-applicable fertilizer spreader was evaluated both in laboratory simulation and real-time field tests. Simulation results reported that the accuracy of the developed system was 94.9 %. Real-time field tests reported that the system produced acceptable results at ground speeds of 1.6 and 3.2 km h^?1 for the spot-application of fertilizer at target areas (in plant areas only) within the field. Results also indicated that the ground speed of 4.8 km h^?1 was unacceptable, which could be due to blurred images at high speed and surface unevenness of the wild blueberry field. Spot-application of fertilizer using the modified fertilizer spreader could save fertilizer for the wild blueberry producers.     

中药渣肥配施多种类肥及菌剂对马铃薯生长、产量及品质的影响

为了减少化肥用量,增加马铃薯产量,促进中药渣的科学利用,本文在榆林设置了5个处理的田间试验,调查了马铃薯的生长指标,测定了马铃薯产量和品质。结果表明：化肥基肥减量10%配施中药渣肥、复合微生物肥及菌剂处理（N5）显著增加马铃薯地下茎及匍匐茎的长度、薯块数、商品果产量、块茎还原糖和淀粉含量,显著降低了马铃薯次果产量,相较对照,N5处理马铃薯匍匐茎、薯块数和商品果产量分别增加223.28%、99.40%和49.83%,次果产量降低55.47%。综上,N5有利于马铃薯匍匐茎的生长、增加块茎数目、促进次果向商品果转变,是实现中药渣肥料化利用、化肥减施及马铃薯产量与品质改善的较优施肥技术方案。     

Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat

When utilizing optical sensors to make in-season agronomic recommendations in winter wheat, one parameter often required is the in-season grain yield potential at the time of sensing. Current estimates use an estimate of biomass, such as normalized difference vegetation index (NDVI), and growing degree days (GDDs) from planting to NDVI data collection. The objective of this study was to incorporate soil moisture data to improve the ability to predict final grain yield in-season. Crop NDVI, GDDs that were adjusted based upon if there was adequate water for crop growth, and the amount of soil profile (0–0.80 m) water were incorporated into a multiple linear regression model to predict final grain yield. Twenty-two site-years of N fertility trials with in-season grain yield predictions for growth stages ranging from Feekes 3 to 10 were utilized to calibrate the model. Three models were developed: one for all soil types, one for loamy soil textured sites, and one for coarse soil textured sites. The models were validated with 11 independent site-years of NDVI and weather data. The results indicated there was no added benefit to having separate models based upon soil types. Typically, the models that included soil moisture, more accurately predicted final grain yield. Across all site years and growth stages, yield prediction estimates that included soil moisture had an R² = 0.49, while the current model without a soil moisture adjustment had an R² = 0.40.     

Effects of fertilizer timing and variable rate N on nitrate–N losses from a tile drained corn-soybean rotation simulated using DRAINMOD-NII

Nitrogen (N) from farm fields is a source of pollution to fresh and marine waters. Modifying N fertilizer application rate and timing to consider the spatial and temporal variability in plant N requirements could reduce N losses from farmlands, resulting in improvements to surface water quality. In this study, the field-scale hydrologic and N simulation model DRAINMOD-NII was used to predict nitrate–N losses from fields planted in a corn-soybean rotation at Waseca, Minnesota, USA, over a 15-year period (2003–2017) for two fertilizer application treatments. The N fertilizer treatments simulated included a single uniform fertilizer application in the spring before planting and a variable rate N practice (VRN) where fertilizer was applied as a split pre-plant, side-dress application, based on in-season monitoring of plant N requirements to determine fertilizer rate. Measured discharge (2003–2008) and nitrate–N concentrations in subsurface drainage (2003–2008 and 2016–2017) at the site were used to calibrate discharge and nitrate–N losses in model simulations and validate model performance for uniform vs VRN fertilizer management. Measured nitrate–N concentrations in weekly samples were 13% lower for fields utilizing VRN versus a single spring application in 2016, and 18% lower in 2017. Model predictions of nitrate concentrations based on daily predictions of discharge accurately matched observed data for these years, predicting reductions of 23% and 19% for the years 2016 and 2017, respectively. The results of model simulation for the 15-year period indicated that changing the timing of fertilizer application from a single application to a VRN application could reduce annual N loads lost in drainage by 40%.

     

施氮量对马铃薯磷钾利用和土壤磷钾含量的影响

为了提高肥料利用效率,采用田间试验方法,在基施等量磷肥(P2O5150 kg/hm2)和钾肥(K2O 360 kg/hm2)条件下,研究施氮量(0、80、160、240 kg/hm2)对马铃薯(Solanum tuberosum L.)费乌瑞它磷钾利用和土壤磷钾含量变化的影响。结果表明,马铃薯干物质、全磷及全钾积累总量随施氮量的增加而递增,但施氮量达160 kg/hm2以上时再增施氮肥增加不显著;增加施氮量,磷肥和钾肥的肥料效率略呈上升趋势,但磷肥和钾肥的收获指数、经济效率及生理效率略呈下降趋势。马铃薯收获后,种植地土壤尤其是0~30 cm土层的速效磷和速效钾含量显著增加,但各土层速效磷和速效钾含量随施氮量的增加呈递减趋势。施氮量还影响干物质、全磷及全钾在马铃薯各器官的分配。因此,本研究条件下,N、P2O5、K2O施用量可在160、150、360 kg/hm2基础上适当减量。     

Active remote sensing and grain yield in irrigated maize

Advances in agricultural technology have led to the development of active remote sensing equipment that can potentially optimize N fertilizer inputs. The objective of this study was to evaluate a hand-held active remote sensing instrument to estimate yield potential in irrigated maize. This study was done over two consecutive years on two irrigated maize fields in eastern Colorado. At the six- to eight-leaf crop growth stage, the GreenSeeker? active remote sensing unit was used to measure red and NIR reflectance of the crop canopy. Soil samples were taken before side-dressing from the plots at the time of sensing to determine nitrate concentration. Normalized difference vegetation index (NDVI) was calculated from the reflectance data and then divided by the number of days from planting to sensing, where growing degrees were greater than zero. An NDVI-ratio was calculated as the ratio of the reflectance of an area of interest to that of an N-rich portion of the field. Regression analysis was used to model grain yield. Grain yields ranged from 5 to 24 Mg ha^?1. The coefficient of determination ranged from 0.10 to 0.76. The data for both fields in year 1 were modeled and cross-validated using data from both fields for year 2. The coefficient of determination of the best fitting model for year 1 was 0.54. The NDVI-ratio had a significant relationship with observed grain yield (r ² = 0.65). This study shows that the GreenSeeker? active sensor has the potential to estimate grain yield in irrigated maize; however, improvements need to be made.     

Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R ² = 0.80?0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R ² = 0.74 for four selected species, R ² = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.     

不同有机养分替代化肥对小麦产量、氮肥利用率及土壤肥力的影响

为探究有机肥种类和替代比例对小麦产量、氮肥利用率及土壤肥力的影响,通过田间小区试验,设置不施肥（CK）,常规施肥（CN）,优化减量施肥（ON）,优化减量配施15%(ONL-15%)、30%(ONL-30%)和50%(ONL-50%)沼液氮,优化减量配施30%堆肥氮（ONC-30%）,优化减量配施30%商品有机肥氮（ONS-30%）共8个处理。结果表明：与CN处理相比,ON、ONL-15%和ONL-30%处理均显著增加小麦籽粒产量和氮肥利用率,其中ONL-15%处理小麦产量最高,较CN和ON处理分别增产16.18%和14.08%,该处理下氮肥利用率也由CN处理的18.41%增至36.46%,表明优化减量配施15%沼液氮肥为减氮配施沼液最佳比例。在30%替代比例下,不同种类有机肥配施对小麦增产效果存在差异,与ON处理相比,沼液配施增加小麦产量和氮肥利用率,而堆肥和商品有机肥配施显著降低小麦产量。堆肥配施可以促进土壤全氮、水解性氮和有效磷的积累,商品有机肥配施对缓解土壤酸化、增加土壤有机质含量具有较好效果。有机无机配施处理减少32.60%～65.91%硝态氮在耕层土壤的残留,增加土壤有效氮含...