The WEED-PAM® is a chlorophyll fluorescence sensor. It has already been applied in the detection of herbicide resistant Alopecurus mysuroides populations with promising results. Yet more work needs to be done in order to validate the system’s capabilities in different species. In this study, field experiments were conducted at three sites to clarify the capability of this sensor to detect herbicide resistant Apera spica-venti populations. The plants were treated with five different herbicides: three ALS-, one ACCase- and one PS II- inhibitor. Five days after the herbicide treatment, sensor data were gathered. These data were compared with a visual assessment, performed 21 days after the herbicide application. The populations that exhibited a strong resistance to the ALS and PS II inhibitors could be differentiated from the sensitive ones. Yet the Apera spica-venti population with a low resistance level to the ACCase inhibitors cannot be differentiated from the sensitive population.

Nitrogen fertilization of silage maize in Central Italy is typically carried out with two applications at early stages of crop development: 2nd (V2) and 6th (V6) leaf respectively. In such conditions, the crop has not yet fully covered the soil and proximal or remote sensing of the canopy is hindered by the strong soil background signal. There is thus great interest in rapid and inexpensive approaches to N fertilization prescription. Therefore, an indirect method for inferring information on yield potential and soil variability, through a field-based clustering of multi-temporal satellite data, has been developed using archive Landsat images to identify temporally constant patterns. This method is potentially useful for the creation of prescription maps. The usefulness of the method was evaluated during an N fertilisation field trial in Maccarese (Central Italy), in 2016. At the V2 stage, both uniform and variable rate applications were performed and compared. A pseudo-cross variogram and a standardized ordinary co-kriging methodology was used to highlight spatially variable significant differences among the treatments.

There is evidence that well managed winter cereal cover crops can scavenge a goodly amount of post summer cereal harvest residual nitrogen (N), reducing nitrate-N losses to leaching or runoff. The objective of this study was to compare nitrate-N phytoremediation areas derived from five sources of information: site specific, non-site specific, or a combination. The non-site specific source was a single “composite” soil nitrate sample. The site specific sources were: a) a dense soil nitrate-N grid sampling; and b) a N removal map calculated from yield and grain N concentration, both determined at the same grid density as soil nitrate-N. The source combinations were: a) a yield map and a single grain N concentration value taken from published information; and b) a yield map and a single field “composite” grain N concentration value. The results indicated that the published grain N value was inferior to measured grain N values, and that the maize (Zea mays L.) yield map best serves as a stratification tool, delineating similar crop performance areas. Random soil sampling within those areas further optimizes residual nitrate-N recovery management. Site specific technologies can guide establishment of N scavenging cover crops to simultaneously improve resource use efficiency and water quality.

Optimising oilseed rape canopy size through correct management is crucial for maximising yield. Plant growth regulators (PGRs) and nitrogen (N) fertiliser are generally applied at a flat rate, however variable applications may be useful for the optimisation of canopy size. The aim of this paper was to understand the potential for spectral reflectance indices to predict green area index (GAI) and crop N content in winter oilseed rape, with specific focus on the Fritzmeier Isaria Crop Sensor. Three large oilseed rape chessboard experiments were set up in 2015 and 2016 in the UK. The results show good correlations between the Isaria indices and both GAI and crop N content, suggesting that the Isaria may be a useful tool for variably applying PGRs and N fertiliser to oilseed rape.

A range of precision farming technologies are used commercially for variable rate applications of nitrogen (N) for cereals, yet these usually adjust N rates from a pre-set value, rather than predicting economically optimal N requirements on an absolute basis. This paper reports chessboard experiments set up to examine variation in N requirements, and to develop and test systems for its prediction, and to assess its predictability. Results showed very substantial variability in fertiliser N requirements within fields, typically >150 kg ha−1, and large variation in optimal yields, typically >2 t ha−1. Despite this, calculated increases in yield and gross margin with N requirements perfectly matched across fields were surprisingly modest (compared to the uniform average rate). Implications are discussed, including the causes of the large remaining variation in grain yield, after N limitations were removed.

Field vegetables require large amounts of N and precision N management (PNM) may help increase their productivity, quality and profitability while reducing N leaching in the biosphere. Few studies investigated PNM for field vegetables. This may be explained by the great diversity in crops and cultivars which complicate the broad implementation of PNM discoveries. Field vegetables are often grown in histosols, which have unique properties such as quantity of N mineralized and the spatial pattern of organic soil depth. Finally, research gaps exist in the development of decision support systems adapted to field vegetable crops grown in histosols.

Crop growth models including CERES-Maize and CROPGRO-Soybean have been used in the past to evaluate causes of spatial yield variability and to evaluate economic consequences of variable rate prescriptions. However, these modelling techniques have not been widely used because of an absence of user-friendly software. In this work, a nitrogen prescription model to simulate the consequences of different nitrogen prescriptions using the DSSAT crop growth models is developed. The objective is to describe a site-specific nitrogen prescription and economic optimizer program developed for computing optimum spatial nitrogen rates for maize using the CERES-Maize model. The application of the model is demonstrated on two different fields in Germany and the US. The program simulated optimum N applications that averaged 42% (McGarvey field, US) and 39% (Riech field, Germany) lower than the uniform rates actually applied in the fields. The software is written in Python and will ultimately be distributed in the public domain as a plug-in to the QGIS software.

Little is known about the effectiveness of sensor-based variable rate nitrogen (VRN) fertilizer application at reducing nitrate-N losses. A variable rate in-season nitrogen (VRN) treatment was compared to a conventional pre-plant uniform rate nitrogen treatment in eight Minnesota, USA sub-fields. Water samples collected weekly during 2016 were analyzed for nitrate-N. The Drainmod model was able to accurately represent measured tile discharge and nitrate-N concentration data. Modeled nitrate-N losses in tile discharge were 16% lower in VRN than uniform treatments. These results show that in-season VRN fertilizer N management can significantly reduce nitrate-N loads from tile drained maize fields.

This work proposes a methodology that uses remote sensing (RS) images to obtain optimum nitrogen crop uptake (Nuptake) maps, for the all pixels in the image included in the field during the entire growing season. The Nuptake was determined from relationship between critical nitrogen concentration (Nc) and biomass where biomass was estimated by a crop growth model based on the water use efficiency. The paper proposes the use of this methodology in commercial wheat farm. The results are discussed with respect to field measurements of crop biomass and N concentration on different dates and in zones with different nitrogen treatments from 8 commercial wheat farms in Albacete, Spain during 2015 and 2016.

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.

Simple active radiometer sensors, such as RapidScan, enable agronomic decision-making and phenotyping within commercial wheat fields and experiments. The objectives of this study were: 1 - to evaluate the accuracy of quantitative biomass and nitrogen uptake estimation by the RapidScan, and 2 - to evaluate yield loss estimation based on NDVI. The RapidScan sensor was used as an assessment tool in the following studies: (i) over 3 years, 518 wheat samples were monitored during the vegetative growth period for biomass and aboveground nitrogen uptake and (ii) wheat cultivars were tested in an additional 4 field experiments, which were scanned weekly and correlated with grain yield. Results showed that accurate biomass estimation is limited up to about 100 g DM m−2. Grain yield, actual and potential, estimation is highly affected by the emergence date. The results showed that the use of a proximal-sensing technique allows for rapid and accurate crop monitoring and yield estimation, but emphasizing limitations in future use as well.

Intensive agriculture has the objective to increase nutrients use efficiency. Nitrogen (N) is a key nutrient for crops and the estimations of crop N status allow adjusting the fertilization levels to crop requirements, while reducing the environmental costs and optimizing the benefits for farmers. In this work the N status of wheat in a commercial plot has been monitored, varying the N supply taking into account the variability of the soil. The N content in the cover has been monitored simultaneously by sampling at field level and by using vegetation indices based on reflectance in the red-edge band. The results of the field campaign along a crop growth cycle show that the REP, MTCI, AIVI and CCCI calculated from narrow spectral bands show good linear correlations (R2>0.93) with respect to N content (g·m−2). These indices are stable when passing to broad bands as the case of Sentinel 2 with R2>0.9.

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.

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).

A method able to overcome multiple scattering effects in close range hyperspectral imagery of vegetation scenes is presented. It has been developed using canopy and light propagation simulation tools and evaluated on real crop plants in the context of nitrogen content assessment.

This work was to evaluate the differences of soil and plant analysis development (SPAD) and normalized difference vegetation index (NDVI) readings and their relationship with leaf nitrogen accumulation (LNA). The study explored new indices to diagnose nitrogen (N) status. These indices were obtained by multiplying SPAD readings and leaf area index (LAI). Linear regression relationships between Chlorophyll values and N indicators showed the SPAD readings (Chl: LNA=0.0546×Chl-0.479, R2=0.94***, P<0.001). The projected results suggested that Chl values could play an important role for improving N status diagnosis from stem elongation to heading stages in paddy rice.

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.

APOLLO, a newly funded H2020 EU project will develop an agricultural advisory platform for small farmers based on Copernicus Sentinel satellites. It will provide services for tillage scheduling, irrigation scheduling, crop growth monitoring and yield estimation. The aim of this study was to identify the farmers’ requirements of the APOLLO platform. In total 121 farmers were interviewed in Spain, Serbia and Greece. More than 90% of the farmers pointed out that smart agriculture and use of satellite data in agriculture are important. Additionally, more than 80% want to have access to historical data and a flexible subscription policy to the platform according to their needs and use. However, significant differences exist among farmers of these countries in terms of technology awareness and penetration, which should be taken into consideration for developing a successful platform.

Remote sensing images offer a unique monitoring capacity for agriculture fields. Despite significant drawbacks, free historical remote sensing images are likely to provide valuable information to delineate temporally stable zones, especially for a perennial crop like vines. In order to test the potential of these free images for delineating within field soil zones, an experiment was performed on three vine fields located in southern France. On each vine field, the grower was asked to delineate within field zones that present differences in soil characteristics. The first expert zoning (ZP zoning) was only based on the grower’s knowledge. Two weeks after, 12 visible free remote sensing were provided to the grower to refine the first zoning (ZAI zoning). In order to assess the relevancy of both zonings, a soil apparent electrical conductivity (ECa) survey was performed on each field. The Rv zoning index was then computed on ECa data to assess the potential improvement between ZP and ZAI zonings. Results show that for two fields, remote sensing images improved the zoning while for one field both ZP and ZAI expert zonings were similar. This result highlights a potential interest in historical remote sensing images to improve the knowledge that growers have of their fields. It should be noted, however, that this improvement is still limited in the case of viticulture where the small size of the fields with a large number of manual operations is likely to lead to a good knowledge of within field variability.

This work assesses the potential of Sentinel-2A images in precision agriculture for Barley production in a case study. Two workflows are proposed: 1) images were acquired with a relatively simple methodology to follow the crop development; 2) two images around harvest time were downloaded and processed using a more complex and accurate methodology to calculate four vegetation indices (NDVI, WDRVI, GRVI and GNDVI) to be correlated to yield with linear regression models. Yield data were acquired with a yield monitor installed in a combine harvester. Green-based vegetation indices performed slightly better. However, the highest correlation coefficient was 0.48. Better results may be achieved with earlier imagery and other vegetation indices. Sentinel-2 is a promising tool for precision agriculture in large arable crop fields.