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In-season diagnosis of crop nitrogen (N) status is crucial for precision N management. Critical N dilution curve and N nutrition index (NNI) have been proposed as effective methods for diagnosing N status of different crops. Critical N dilution curves have been developed for Indica rice in the tropical and temperate zones and Japonica rice in the subtropical-temperate zone, but they have not been evaluated for short-season Japonica rice in Northeast China. The objective of this study was to evaluate the previously developed critical N dilution curves for rice in Northeast China, and develop a more suitable critical N dilution curve in this region. A total of 17 N rate experiments were conducted in Jiansanjiang, Heilongjiang province in Northeast China from 2008 to 2013. The results indicated that none of the two previously developed critical N dilution curves was suitable for diagnosing N status of the short season Japonica rice in Northeast China. A new critical N dilution curve was developed and can be described by the equation N c = 27.7W −0.34 (aboveground biomass ≥ 1 Mg DM ha −1 ) or N c = 27.7 g kg −1 DM (aboveground biomass −1 ). This new curve was lower than those previous curves. It was validated using a separate dataset, and it could discriminate non-limiting and limiting N nutritional conditions. More studies are needed to further evaluate it for diagnosing N status of different rice cultivars in Northeast China and develop efficient non-destructive methods to estimate NNI for practical applications.

Ganoderma lucidum is a valuable basidiomycete with numerous pharmacological compounds, which is widely consumed throughout China. We previously found that the polysaccharide content of Ganoderma lucidum fruiting bodies could be significantly improved by 45.63% with treatment of 42 °C heat stress (HS) for 2 h. To further investigate genes involved in HS response and explore the mechanisms of HS regulating the carbohydrate metabolism in Ganoderma lucidum, high-throughput RNA-Seq was conducted to analyse the difference between control and heat–treated mycelia at transcriptome level. We sequenced six cDNA libraries with three from control group (mycelia cultivated at 28 °C) and three from heat-treated group (mycelia subjected to 42 °C for 2 h). A total of 99,899 transcripts were generated using Trinity method and 59,136 unigenes were annotated by seven public databases. Among them, 2790 genes were identified to be differential expressed genes (DEGs) under HS condition, which included 1991 up-regulated and 799 down-regulated. 176 DEGs were then manually classified into five main responsive-related categories according to their putative functions and possible metabolic pathways. These groups include stress resistance-related factors; protein assembly, transportation and degradation; signal transduction; carbohydrate metabolism and energy provision-related process; other related functions, suggesting that a series of metabolic pathways in Ganoderma lucidum are activated by HS and the response mechanism involves a complex molecular network which needs further study. Remarkably, 48 DEGs were found to regulate carbohydrate metabolism, both in carbohydrate hydrolysis for energy provision and polysaccharide synthesis. In summary, this comprehensive transcriptome analysis will provide enlarged resource for further investigation into the molecular mechanisms of basidiomycete under HS condition.

One challenge in precision nitrogen (N) management is the uncertainty in future weather conditions at the time of decision-making. Crop growth models require a full season of weather data to run yield simulation, and the unknown weather data may be forecasted or substituted by historical data. The objectives of this study were to (1) develop a model-based in-season N recommendation strategy for maize (Zea mays L.) using weather data fusion; and (2) evaluate this strategy in comparison with farmers’ N rate and regional optimal N rate in Northeast China. The CERES-Maize model was calibrated using data collected from field experiments conducted in 2015 and 2016, and validated using data from 2017. At two N decision dates - planting stage and V8 stage, the calibrated CERES-Maize model was used to predict grain yield and plant N uptake by fusing current and historical weather data. Using this approach, the model simulated grain yield and plant N uptake well (R2 = 0.85–0.89). Then, in-season economic optimal N rate (EONR) was determined according to responses of simulated marginal return (based on predicted grain yield) to N rate at planting and V8 stages. About 83% of predicted EONR fell within 20% of measured values. Applying the model-based in-season EONR had the potential to increase marginal return by 120–183 $ ha−1 and 0–83 $ ha−1 and N use efficiency by 8–71% and 1–38% without affecting grain yield over farmers’ N rate and regional optimal N rate, respectively. It is concluded that the CERES-Maize model is a valuable tool for simulating yield responses to N under different planting densities, soil types and weather conditions. The model-based in-season N recommendation strategy with weather data fusion can improve maize N use efficiency compared with current farmer practice and regional optimal management practice.

Nitrogen (N) management needs to be significantly improved to address the triple challenge of global food security, environmental pollution and climate change. In addition to being site-specific, dynamic in-season management is needed to respond to temporal variability in soil N supply and crop N demand. Active canopy sensor-based precision N management (CS-PNM) aims to match N supply with crop N demand in both space and time. Studies that systematically compare this strategy with other N management strategies are limited, especially in intensively farmed regions of developing countries. The objective of this study was to compare CS-PNM strategy in terms of agronomic and environmental impacts in comparison with farmer’s N practice, regional optimum N management, modified Green Window-based N Management and soil test-based in-season root zone N management for an intensive winter wheat (Triticum aestivum L.) and summer maize (Zea mays L.) rotation system in North China Plain. A field experiment was conducted from 2008 to 2012 in Quzhou, Hebei Province of China to evaluate these systems. The CS-PNM strategy was consistently better for both crops than the other tested strategies. In comparison with farmer’s practice and regional optimum N management, the CS-PNM strategy reduced N fertilizer applications by 62% and 36%, increased N use efficiencies by 68–123% and 20–61%, decreased apparent total N losses by 81% and 57%, and lowered intensities of total N2O emission, greenhouse gas emission and reactive N losses by 54–68% and 20–42%, respectively. Here we demonstrate that relative to current N management strategies, the CS-PNM strategy has significant potential to improve N use efficiencies and mitigate environmental degradation for sustainable intensification of agriculture in developing countries.

Precision nitrogen (N) management requires rapid and real-time technologies for in-season crop N status diagnosis. The leaf fluorescence sensor Dualex 4 is an effective and promising tool to monitor crop N status. N nutrition index (NNI) is the most widely recognized diagnostic tool for accurate in-season diagnosis of crop N status. However, studies focusing on revealing the relationships between fluorescence sensing indices and NNI and assessing the N status of maize is limited. The objectives of this study were to (1) evaluate the potential of using Dualex 4 indices measured on three differently positioned leaves to estimate NNI across different stages; and (2) determine if the incorporation of environmental (weather) and management information can significantly improve the in-season N status prediction and diagnosis of maize. In 2016 and 2017, a total of four experiments with six N rates and three plant densities were conducted in two fields in Northeast China. Dualex sensor readings – Chlorophyll (Chl) and N balance index (NBI) – were collected from three differently positioned leaves at three growth stages. Some external factors including weather and management conditions were included for in-season N status assessment. The results indicated that the two Dualex indices (Chl and NBI) had strong relationships with NNI at different growth stages, and both stage-specific and across-stage models could estimate NNI based on their values acquired from differently positioned leaves. Nevertheless, the N diagnostic accuracies based on the estimated NNI by the Dualex indices were not satisfactory with Kappa values all lower than 0.40. Likewise, similar results were found in the multiple linear regression (MLR) models only based on the Dualex readings (MLRChl, MLRNBI and MLRChl+NBI). However, when weather and management variables were used together with Dualex sensor measurements in MLR analysis, the prediction of NNI (R2 = 0.81 to 0.85) and the accuracy of maize N status diagnosis (areal agreement = 0.79 and Kappa = 0.52 to 0.55) were significantly improved. More studies are needed to develop strategies combining more environmental and management variables with sensor data to further improve in-season N status diagnosis and N management and/or combine proximal with remote sensing for large-scale crop N nutritional status diagnosis and in-season site-specific N management.

Over-application of nitrogen (N) in rice (Oryza sativa L.) production in China is common, leading to low N use efficiency (NUE) and high environmental risks. The objective of this work was to evaluate the ability of the CERES-Rice crop growth model to simulate N response in the cool climate of Northeast China, with the long term goal of using the model to develop optimum N management recommendations. Nitrogen experiments were conducted from 2011–2015 in Jiansanjiang, Heilongjiang Province in Northeast China. The CERES-Rice model was calibrated for 2014 and 2015 and evaluated for 2011 and 2013 experiments. Overall, the model gave good estimations of yield across N rates for the calibration years (R²=0.89) and evaluation years (R²=0.73). The calibrated model was then run using weather data from 2001–2015 for 20 different N rates to determine the N rate that maximized the long term marginal net return (MNR) for different N prices. The model results indicated that the optimum mean N rate was 120–130 kg N ha–¹, but that the simulated optimum N rate varied each year, ranging from 100 to 200 kg N ha–¹. Results of this study indicated that the CERES-Rice model was able to simulate cool season rice growth and provide estimates of optimum regional N rates that were consistent with field observations for the area.

The objective of this study was to determine how much improvement red edge-based vegetation indices (VIs) obtained with the RapidSCAN sensor would achieve for estimating rice nitrogen (N) nutrition index (NNI) at stem elongation stage (SE) as compared with commonly used normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) in Northeast China. Sixteen plot experiments and seven on-farm experiments were conducted from 2014 to 2016 in Sanjiang Plain, Northeast China. The results indicated that the performance of red edge-based VIs for estimation of rice NNI was better than NDVI and RVI. N sufficiency index calculated with RapidSCAN VIs (NSI_VIs) (R2=0.43–0.59) were more stable and more strongly related to NNI than the corresponding VIs (R2=0.12–0.38).

The objective of this study was to evaluate the performance of a Crop Circle sensor-based precision nitrogen (N) management (PNM) strategy in different winter wheat cropping systems under on-farm conditions in North China Plain (NCP). Four farmer’s fields were selected for on-farm experiments in Laoling County, Shandong Province of NCP in 2015-2016. In each field, the PNM strategy was evaluated in two winter wheat cropping systems: farmer’s conventional management (FCM) and regional optimum crop management (ROCM). In each cropping system, there were two N management strategies: 1) FCM or ROCM; 2) PNM. The results indicated that the PNM strategy significantly increased partial factor productivity (PFP) by 29% in the FCM system, but did not have any significant improvement in the ROCM system. The ROCM system, using either regional optimum N management or PNM, significantly increased both grain yield and PFP than the FCM system.

The objective of this study was to evaluate the potential of using Multiplex 3, a hand-held canopy fluorescence sensor, to determine rice nitrogen (N) status at different growth stages. In 2013, a paddy rice field experiment with five N fertilizer treatments and two varieties was conducted in Northeast China. Field samples and fluorescence data were collected simultaneously at the panicle initiation (PI), stem elongation (SE), and heading (HE) stages. Four N status indicators, leaf N concentration (LNC), plant N concentration (PNC), plant N uptake (PNU) and N nutrition index (NNI), were determined. The preliminary results indicated that different N application rates significantly affected most of the fluorescence variables, especially the simple fluorescence ratios (SFR_G, SFR_R), flavonoid (FLAV), and N balance indices (NBI_G, NBI_R). These variables were highly correlated with N status indicators. More studies are needed to further evaluate the accuracy of rice N status diagnosis using fluorescence sensing at different growth stages.

Lodging is a major constraint to increasing the global productivity of maize (Zea Maize L.). The objectives of this paper are to: i) describe a model for stem and root lodging in maize, ii) calibrate the anchorage strength component of the model, iii) evaluate the model's applicability by assessing its capacity to explain effects of crop husbandry on lodging risk and iv) investigate the potential to further develop the lodging model to predict lodging risk at an early enough growth stage for tactical agronomic action to minimise lodging risk. The study involved a multidisciplinary collaboration between crop scientists, wind engineers and geospatial scientists in the UK and China. Three field experiments with plant population density and nitrogen (N) fertiliser rate treatments were conducted in the UK and China to develop and test the lodging model. Plant characteristics associated with lodging were measured in the experiments after flowering. An existing model of cereal anchorage strength that uses the spread of the root plate as its primary input was demonstrated to be applicable for maize and calibrated for this crop species. The lodging model's predictions of the effects of plant population and N fertiliser on lodging risk were consistent with published observations. The lodging model calculated that increasing the plant population significantly reduced the anchorage and stem failure wind speeds in all experiments, thus increasing the risk of lodging. This effect was primarily due to increased plant population reducing the spread of the root plate and the stem strength. Changes in N fertiliser had a smaller effect on the lodging associated plant characters. A sensitivity analysis showed that stem failure wind speed was influenced most by variation in stem strength and root failure wind speed was influenced most by variation in the spread of the root plate. This study has shown that the leaf area index measured at leaf 4, 6 or 8 stages is a good indicator of a crop's future risk of lodging, which demonstrates the potential to develop the model into a practical tool for predicting lodging risk in time for tactical agronomic decisions to be made during the crop's growing period. © 2020 Elsevier B.V.

Reliable and efficient in-season nitrogen (N) status diagnosis and recommendation methods are crucially important for the success of crop precision N management (PNM). The accuracy of these methods has been found to be influenced by soil properties, weather conditions, and crop management practices. It is important to effectively incorporate these variables to improve in-season N management. Machine learning (ML) methods are promising due to their capability of processing different types of data and modeling both linear and non-linear relationships. The objectives of this study were to (1) determine the potential improvement of in-season prediction of corn N nutrition index (NNI) and grain yield by combining soil, weather and management data with active sensor data using random forest regression (RFR) as compared with Lasso linear regression (LR) using similar data and simple regression (SR) models only using crop sensor data; and (2) to develop a new in-season side-dress N fertilizer recommendation strategy at eighth to ninth leaf stage (V8-V9) of corn developement using the RFR model. Twelve site-year experiments examining corn N rates and planting densities were conducted in Northeast China. The GreenSeeker sensor data and corn NNI were collected at V8-V9 stage, and grain yield was determined at the harvest stage (R6). The soil information was obtained at planting and the weather data was measured throughout the growing season. The results indicated that corn NNI and grain yield were better predicted by combining soil, weather and management information with GreenSeeker sensor data using RFR model (R2 = 0.86 and 0.79) and LR model (R2 = 0.85 and 0.76) as compared with only using GreenSeeker sensor data (R2 = 0.66 and 0.62–63) based on the test dataset. An innovative in-season side-dress N recommendation strategy was developed using the RFR grain yield prediction model to simulate corn grain yield responses to a series of side-dress N rates at V8-V9 stage. Based on these response curves, site-, and year-specific optimum side-dress N rates can be determined. The scenario analysis results indicated that this RFR model-based in-season N recommendation strategy could recommend side-dress N rates similar to those based on measured agronomic optimum N rate (AONR) or economic optimum N rate (EONR), with root mean square error (RMSE) of 17 kg ha−1 and relative error (RE) of 14–15 %. It is concluded that combining soil, weather and management information with crop sensor data using RFR can significantly improve both in-season corn NNI and grain yield prediction and N management, compared with the approach based only on crop sensor data. More studies are needed to further improve and evaluate this approach under diverse on-farm conditions.

The present study was performed to evaluate the effects of microwave (MW) output power and treatment time on moisture content, lipase and lipoxygenase activities as well as colour changes of wheat germ (WG). In addition, the key aroma compounds in different MW-power-treated WG, which is of importance to the flavour of WG products, were also investigated. The obtained results showed that MW treatment maintained the inherent colour of WG and significantly reduced the moisture content (maximum reduction of 95%) and the activities of lipase and lipoxygenase (maximum reduction of 65% and 99%, respectively). In terms of aroma compounds, with the increase of the MW output power, the content of esters, alkanes, alcohols and acids decreased, while the content of heterocyclic compounds, nitrogen-containing compounds, aldehydes and ketones increased, providing more compounds with roasted flavour and less volatiles with grass-like flavour. Therefore, MW treatment was an effective stabilization method for WG utilization.

This paper systematically evaluated a three band active sensor, Crop Circle ACS-470.The GDVI index (R2=0.60) performed best for estimating plant N concentration.The CIG index (R2=0.89) performed best for estimating plant N uptake.The GRDVI and MGSAVI indices (R2=0.78 and 0.77) performed best for estimating NNI. Crop canopy sensor based in-season site-specific nitrogen (N) management is a promising approach to precision N management. GreenSeeker sensor has previously been evaluated in North China Plain (NCP) for improving winter wheat (Triticum aestivum L.) N management. The Crop Circle ACS-470 is an active canopy sensor with three user-configurable wavebands. This study identified important vegetation indices that can be calculated from Crop Circle green, red edge and near infrared (NIR) wavebands for estimating winter wheat N status and evaluated their potential improvements over GreenSeeker normalized difference vegetation index (NDVI) and ratio vegetation index (RVI). Six field experiments involving different N rates and varieties were conducted in the Quzhou Experiment Station of the China Agricultural University from 2009 to 2012. The results indicated that best Crop Circle ACS-470 sensor vegetation indices could explain similar amounts of aboveground biomass variability in comparison with GreenSeeker sensor NDVI, but Crop Circle normalized difference red edge/green optimized soil adjusted vegetation index (NDRE/GOSAVI) and red edge chlorophyll index (CIRE) were more sensitive to aboveground biomass (having lower noise equivalent) than GreenSeeker NDVI before and after biomass reached about 5000kgha-1, respectively. The Crop Circle green difference vegetation index (GDVI) (R2=0.60) and chlorophyll index (CIG) (R2=0.89) explained 53% and 7-11% more variability in plant N concentration and uptake than GreenSeeker indices, respectively. The Crop Circle green re-normalized difference vegetation index (GRDVI) (R2=0.78) and modified green soil adjusted vegetation index (MGSAVI) (R2=0.77) performed consistently better than GreenSeeker NDVI (R2=0.47) and RVI (R2=0.44) for estimating N nutrition index (NNI). We conclude that the three band user configurable Crop Circle ACS-470 sensor can improve the estimation of winter wheat N status as compared with two fixed band GreenSeeker sensor.

Crop monitoring during the growing season is important for regional management decisions and biomass prediction. The objectives of this study were to develop, improve and validate a scale independent biomass model. Field studies were conducted in Huimin County, Shandong Province of China, during the 2006–2007 growing season of winter wheat ( Triticum aestivum L. ). The field design had a multiscale set-up with four levels which differed in their management, such as nitrogen fertilizer inputs and cultivars, to create different biomass conditions: small experimental fields (L1), large experimental fields (L2), small farm fields (L3), and large farm fields (L4). L4, planted with different winter wheat varieties, was managed according to farmers’ practice while L1 through L3 represented controlled field experiments. Multitemporal spectral measurements were taken in the fields, and biomass was sampled for each spectral campaign. In addition, multitemporal Hyperion data were obtained in 2006 and 2007. L1 field data were used to develop biomass models based on the relation between the winter wheat spectra and biomass: several published vegetation indices, including NRI, REP, OSAVI, TCI, and NDVI, were investigated. A new hyperspectral vegetation index, which uses a four-band combination in the NIR and SWIR domains, named GnyLi, was developed. Following the multiscale concept, the data of higher levels (L2 through L4) were used stepwise to validate and improve the models of the lower levels, and to transfer the improved models to the next level. Lastly, the models were transferred and validated at the regional scale using Hyperion images of 2006 and 2007. The results showed that the GnyLi and NRI models, which were based on the NIR and SWIR domains, performed best with R 2 > 0.74. All the other indices explained less than 60% model variability. Using the Hyperion data for regionalization, GnyLi and NRI explained 81–89% of the biomass variability. These results highlighted that GnyLi and NRI can be used together with hyperspectral images for both plot and regional level biomass estimation. Nevertheless, additional studies and analyses are needed to test its replicability in other environmental conditions.

In recent decades, many spectral indices have been proposed to estimate crop nitrogen (N) status parameters. However, most of the indices based on red radiation lose their sensitivity under high aboveground biomass conditions. The objectives of this study were to (i) evaluate red-edge based spectral indices for estimating plant N concentration and uptake of summer maize ( Zea mays L.) and (ii) study the influence of bandwidth and crop growth stage changes on the performance of various vegetation indices. Nitrogen rate experiments for maize were conducted in 2009 and 2010 at Quzhou Experimental Station of China Agricultural University in the North China Plain. The spectral indices were calculated from hyperspectral narrow bands, simulated Crop Circle ACS-470 active crop canopy sensor bands and simulated WorldView-2 satellite broad bands. The results indicated that red edge-based canopy chlorophyll content index (CCCI) performed the best across different bandwidths for estimating summer maize plant N concentration and uptake at the V6 and V7 and V10–V12 stages. The second best index was MERIS terrestrial chlorophyll index (MTCI). The four red edge-based indices, CCCI, MTCI, normalized difference red edge (NDRE) and red edge chlorophyll index (CI red edge ), performed similarly better across bandwidths for estimating plant N uptake ( R 2 = 0.76–0.91) than normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) ( R 2 = 0.54–0.80) at the V10–V12 and V6–V12 stages. More studies are needed to further evaluate these red edge-based vegetation indices using real Crop Circle ACS 470 sensor and satellite remote sensing images for maize as well as other crops under on-farm conditions.

Normalized Difference Vegetation Index and Ratio Vegetation Index obtained with the fixed band GreenSeeker active multispectral canopy sensor (GS-NDVI and GS-RVI) have been commonly used to non-destructively estimate crop growth parameters and support precision crop management, but their performance has been influenced by soil and/or water backgrounds at early crop growth stages and saturation effects at moderate to high biomass conditions. Our objective is to improve estimation of rice ( Oryza sativa L.) aboveground biomass (AGB) with hyperspectral canopy sensing by identifying more optimal measurements using one or more strategies: (a) soil adjusted Vegetation Indices (VIs); (b) optimized narrow band RVI and NDVI; and (c) Optimum Multiple Narrow Band Reflectance (OMNBR) models based on raw reflectance, and its first and second derivatives (FDR and SDR). Six rice nitrogen (N) rate experiments were conducted in Jiansanjiang, Heilongjiang province of Northeast China from 2007 to 2009 to create different biomass conditions. Hyperspectral field data and AGB samples were collected at four growth stages from tillering through heading from both experimental and farmers’ fields. The results indicate that six-band OMNBR models ( R 2 = 0.44–0.73) explained 21–35% more AGB variability relative to the best performing fixed band RVI or NDVI at different growth stages. The FDR-based 6-band OMNBR models explained 4%, 6% and 8% more variability of AGB than raw reflectance-based 6-band OMNBR models at the stem elongation ( R 2 = 0.77), booting ( R 2 = 0.50), and heading stages ( R 2 = 0.57), respectively. The SDR-based 6-band OMNBR models made no further improvements, except for the stem elongation stage. Optimized RVI and NDVI for each growth stage ( R 2 = 0.34–0.69) explained 18–26% more variability in AGB than the best performing fixed band RVI or NDVI. The FDR- and SDR-based optimized VIs made no further improvements. These results were consistent across different sites and years. It is concluded that with suitable band combinations, optimized narrow band RVI or NDVI could significantly improve estimation of rice AGB at different growth stages, without the need of derivative analysis. Six-band OMNBR models can further improve the estimation of AGB over optimized 2-band VIs, with the best performance using SDR at the stem elongation stage and FDR at other growth stages.

a b s t r a c t How to ensure both food security and sustainable development is one of the largest challenges in the 21st century. Over 10% of the world's population is still chronically malnourished today. Our environ- ment has been severely degraded by agricultural systems in many parts of the world. Precious agricultural resources are not efficiently utilized, but regrettably wasted in large quantities. Precision agriculture has the potential to improve crop yield, resource use efficiency and at the same time, protect the environment. However, current precision agricultural research has mainly focused on improving resource use efficien- cies, without much impact on yield. Here we developed a preliminary integrated precision rice (Oryza sativa L.) management (PRM) system by combining site-specific nutrient management with alternate drying and wetting irrigation and optimized transplanting density, with the aim to increase both yield and nitrogen (N) use efficiency (NUE). It was compared with a high efficiency system optimizing nutrient and water management (HEM), a high yield system optimizing transplanting density and water man- agement (HYM) and farmer's practice (FP) in an on-farm plot experiment conducted in 2010 and 2011 in Northeast China. It was further evaluated in three on-farm demonstration experiments conducted in 2010. Our results show that this PRM system increased grain yield by 10% and NUE by 51-97% over FP. The on-farm results show that it increased yield by 16% and N agronomic efficiency by 27%. The HEM sys- tem improved NUE by 46-63% over FP, without significantly affecting yield. The HYM system increased yield by 11% and NUE by 19-89% over FP. Compared with HEM system, the PRM system increased grain yield by 10% and NUE by 1-33%. Compared with HYM system, the PRM system achieved similar yield, but increased NUE by 5-27%. It is concluded that the preliminary integrated PRM system can optimize both grain yield and N use efficiency better than either the high efficiency or the high yield management sys- tem. This study demonstrates the potential of precision crop management to simultaneously contribute to food security and sustainable development.

Crop Circle is an active multispectral canopy sensor developed to support precision crop management. The Crop Circle ACS-470 model is user configurable, with a choice of six wavebands covering blue, green, red, red edge and near infrared spectral regions. The objectives of this study were to determine how well nitrogen (N) status of rice ( Oryza sativa L.) can be estimated with the Crop Circle ACS-470 active sensor using green, red edge and near infrared (NIR) bands at key growth stages and identify important vegetation indices for estimating rice N status indicators. Six field experiments involving different N rates and two varieties were conducted in Sanjiang Plain in Heilongjiang Province, China during 2011 and 2012. Crop sensor data and plant samples were also collected from five farmers’ fields to further evaluate the sensor and selected vegetation indices. The results of the study indicated that among 43 different vegetation indices evaluated, modified chlorophyll absorption reflectance index 1 (MCARI1) had consistent correlations with rice aboveground biomass ( R 2 = 0.79) and plant N uptake ( R 2 = 0.83) across growth stages. Four red edge-based indices, red edge soil adjusted vegetation index (RESAVI), modified RESAVI (MRESAVI), red edge difference vegetation index (REDVI) and red edge re-normalized difference vegetation index (RERDVI), performed equally well for estimating N nutrition index (NNI) across growth stages ( R 2 = 0.76). For rice plant N concentration, the highest R 2 was 0.33, and none of the indices performed satisfactorily with validation using farmers’ field data. We conclude that the Crop Circle ACS-470 active canopy sensor allows users the flexibility to select suitable bands and calculate different vegetation indices and has a great potential for in-season non-destructive estimation of rice biomass, plant N uptake and NNI.

This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status. For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarized StripMap (VV) data were acquired over the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out. The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarized backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation. HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface–stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.

The challenges facing agriculture in China are probably more severe than ever before. We have developed an integrated technology system in which the focus is on achieving both high crop productivity and high resource use efficiency (“double high” technology system) to ensure food security and environmental sustainability. The components comprise (1) significantly increased grain-yield through high-yield crop management, i.e. an optimal cropping system design and management well adapted to climate conditions; (2) greatly increased nutrient-use efficiency through root/rhizosphere management to optimize the nutrient supply intensity and composition in the root zone to maximize root/rhizosphere efficiency; (3) improved soil quality to ensure long-term food security by managing soil organic matter and eliminating soil physical, chemical and biological constrains and (4) enhanced agricultural sustainability through resource and environment management by increasing resource use efficiency, reducing nutrient losses and greenhouse gas emissions and minimizing negative ecological footprints. In our work in major agricultural regions of China, this system has been successfully tested and demonstrated through well-organized farmer associations, enterprises with improved products and government extension networks. The new “double high” concept has the potential to become an effective agricultural development path to ensure food security and improve environmental quality, especially in China and other rapidly developing economies where agricultural intensification must achieve and must be transformed from low-efficiency systems to achieving high yields with high resource use efficiency.

Red light based broadband vegetation indices are widely applied to derive aerial nitrogen (N) status parameters. With the advance of growth stages, however, crop canopy structure and aerial biomass will vary greatly, which negatively influences the relationships between spectral indices and the crop canopy N status. The current research aimed to assess the performance of red edge based vegetation indices, derived from simulated broadband WorldView-2 data, to remotely sense aerial N concentration and uptake in winter wheat (Triticum aestivum L.). Six experiments with different N rates for five German cultivars and four Chinese cultivars of winter wheat were conducted in southeast Germany and in the North China Plain from 2007 to 2010. The results showed that aerial biomass strongly affected the relationships between broadband vegetation indices and aerial N concentration before the heading stage. Normalising by using the planar domain index approach significantly improved the prediction power of red edge dependent broadband vegetation indices in estimating aerial N status. The two-dimensional broadband canopy chlorophyll content index (CCCI) and a newly proposed nitrogen planar domain index (NPDI) involving the WorldView-2 satellite red edge region were found to be more stable and better predictors than traditional red light based broadband vegetation indices in estimating aerial N concentration after the heading stage and in assessing aerial N uptake before the heading stage. The findings from this study may be useful for managing the application of N fertiliser for winter wheat in Zadoks growth stages 30–55 and in indirectly monitoring aerial N content in Zadoks growth stages 59–75 at landscape scales.

Successful nitrogen (N) management requires better synchronization between crop N demand and N supply from all sources throughout crop growing season. An in-season N management strategy based on soil Nmin test had been developed under experimental conditions, and more than half-N fertilizer could be saved without grain yield losses, compared with farmer's N management practices. The objective of this study was to evaluate this in-season N management strategy for winter wheat (Triticum aestivum L.) in different farmers’ fields of North China Plain (NCP). A total of 121 on-farm N-response experiments (check with no N fertilizer, in-season N management based on soil Nmin test, and farmer's practice) were conducted in seven key winter wheat production regions of NCP from 2003 to 2005. The average N rate determined with in-season N management strategy (128 kg N ha−1) was significantly lower than farmer's practice (325 kg N ha−1) without wheat grain yield losses. As a result, in-season N management strategy significantly increased economic gains by $144 ha−1, reduced residual nitrate-N content in the top 90 cm soil layer and N losses by 81 and 118 kg N ha−1, respectively (P