Simone Graeff

Quantifying nitrogen status of corn (Zea mays L.) in the field by reflectance measurements

Nutrient deficiencies can seriously reduce yield and economic returns to farmers. Tools that can rapidly quantify the nutritional status of plants are needed for efficient fertilizer management. Reflectance measurements have shown to be a useful tool to identify the nutritional status of different plant species. A set of calibration curves relating reflectance ratios to the nitrogen (N), phosphorus (P), magnesium (Mg), and iron (Fe) concentrations in corn leaves was established in greenhouse trials in a previous study. In this paper these calibrations were examined for their ability to identify nutrient deficiencies under field conditions. A 2-year field experiment was conducted to check and define the regions of the spectra that are influenced by leaf N concentration and to set up possible equations for quantifying the leaf N status in the field. The experiment was carried out on a loess derived soil in south-western Germany. Reflectance of corn leaves, from plants grown with six different N fertilization treatments ranging from 0 to 160 N kg ha−1, was determined once a week from the beginning of June until the end of July. Reflectance measurements were performed at the 4th leaf of corn plants with a digital LEICA S1 Pro camera under controlled light conditions. Reflectance was measured in different wavelength ranges in the visible and infrared spectra. Leaf scans were evaluated within the L*a*b*-color system. Total N concentration of corn leaves was determined chemically and correlated with reflectance patterns. Significant correlations between corn N status and leaf reflectance changes were obtained at a nitrogen level of N<3.0%. Reflectance patterns at 510780, 5161300, 5401300 nm were found most suitable to the corn N status in the field regardless of the year or sampling date. The results indicate that the spectral patterns and the defined calibration curves of N deficiency from greenhouse studies could be used in field studies. Thus, reflectance measurements may serve as a rapid, non-destructive approach to discriminate nitrogen deficiency in the field.

Identification of powdery mildew (Erysiphe graminis sp. tritici) and take-all disease (Gaeumannomyces graminis sp. tritici) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements

The ability to identify diseases in an early infection stage and to accurately quantify the severity of infection is crucial in plant disease assessment and management. A greenhouse study was conducted to assess changes in leaf spectral reflectance of wheat plants during infection by powdery mildew and take-all disease to evaluate leaf reflectance measurements as a tool to identify and quantify disease severity and to discriminate between different diseases. Wheat plants were inoculated under controlled conditions in different intensities either with powdery mildew or take-all. Leaf reflectance was measured with a digital imager (Leica S1 Pro, Leica, Germany) under controlled light conditions in various wavelength ranges covering the visible and the near-infrared spectra (380–1300 nm). Leaf scans were evaluated by means of L*a*b*-color system. Visual estimates of disease severity were made for each of the epidemics daily from the onset of visible symptoms to maximum disease severity. Reflectance within the ranges of 490780 nm (r2 = 0.69), 510780nm (r2 = 0.74), 5161300nm (r2 = 0.62) and 5401300 nm (r2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490780nm showed most sensitive response to damage caused by powdery mildew and take-all infestation. The results of this study indicated that disease detection and discrimination by means of reflectance measurements may be realized by the use of specific wavelength ranges. Further studies have to be carried out, to discriminate powdery mildew and take-all infection from other plant stress factors in order to develop suitable decision support systems for site-specific fungicide application.

Use of reflectance measurements for the early detection of N, P, Mg, and Fe deficiencies in Zea mays L.

Mineral deficiencies can seriously reduce crop yield and economic returns to farmers. Reflectance measurements may provide inexpensive and fast estimates of the mineral status of plants. This study was conducted to examine specific changes of leaf reflectance due to nutrient deficiencies. During the 1998 and 1999 growing seasons leaf scans of N-, P-, Mg-, and Fe-deficient corn plants were performed with a digital LEICA S1 PRO camera under controlled light conditions. Leaf scans were evaluated with the L*a*b*-color system. This is a three-dimensional system with parameter a* describing the green/red percentage and parameter b* the blue/yellow percentage of a color. L* represents the lightness of a color. The a* and b* parameters provided good prediction of N, P, Mg, and Fe status of the plants in the wavelength ranges of 380—390 nm, 430—780 nm, 516—780 nm, 516—IR, and 540—600 nm because reflectance changed specifically due to the nutrient deficiency. Analyses of water-soluble and propanol-soluble pigments showed no significant changes in absorbance during latent deficiency. The results indicate that reflectance measurements may provide a powerful tool for the specific detection of latent nutrient deficiencies in corn plants. Reflexionsmessungen zur fruhzeitigen Erkennung von N-, P-, Mg- und Fe-Mangel bei Zea mays L. Mangel an Pflanzennahrstoffen vermindert vielfach den Ertrag und fuhrt zu wirtschaftlichen Einbusen fur den Landwirt. Zur Vermeidung von Ertragsverlusten ist eine spezifische und fruhzeitige Erkennung von Mangelsituationen erforderlich. Reflexionsmessungen konnten eine kostengunstige und schnelle Moglichkeit fur die Bestimmung der Nahrstoffversorgung von Pflanzen sein. Zur Vermeidung von Ertragsverlusten ist allerdings eine sichere und fruhzeitige Erkennung von Mangelsituationen erforderlich. Die vorliegende Studie diente der Untersuchung spezifischer Reflexionsanderungen bei Pflanzen, ausgelost durch einen Nahrstoffmangel. In den Jahren 1998 und 1999 wurden Blatt-Scans von Maispflanzen mit N-, P-, Mg- und Fe-Mangel mit einer digitalen LEICA-S1-PRO-Kamera unter kontrollierten Lichtbedingungen durchgefuhrt. Die Scans wurden im L*a*b*-Farbraum ausgewertet. Es handelt sich um ein dreidimensionales System, wobei der Parameter a* den Grun/Rot-Anteil und der Parameter b* den Blau/Gelb-Anteil einer Farbe beschreibt. L* reprasentiert die Helligkeit einer Farbe. Anhand der a*- und b*-Werte konnten die untersuchten Nahrstoffmangel eindeutig in den Wellenlangenbereichen 380—390 nm, 430—780 nm, 516—780 nm, 516—IR und 540—600 nm identifiziert werden, da sich die Reflexion nahrstoffspezifisch anderte. Die Analyse der wasser- und propanolloslichen Pigmente zeigte jedoch keine signifikanten Unterschiede in der Absorption wahrend eines leichten Nahrstoffmangels im Vergleich zur Kontrolle. Die Ergebnisse der Reflexionsmessungen stellen eine gute Grundlage fur die Fruherkennung von Ernahrungsstorungen dar.

Does soil aging affect the N2O mitigation potential of biochar? A combined microcosm and field study

The application of biochar as a soil amendment to improve soil fertility has been suggested as a tool to reduce soil‐borne CO2 and non‐CO2 greenhouse gas emissions, especially nitrous oxide (N2O). Both laboratory and field trials have demonstrated N2O emission reduction by biochar amendment, but the long‐term effect (>1 year) has been questioned. Here, we present results of a combined microcosm and field study using a powdered beech wood biochar from slow pyrolysis. The field experiment showed that both CO2 and N2O emissions were still effectively reduced by biochar in the third year after application. However, biochar did not influence the biomass yield of sunflower for biogas production (Helianthus annuus L.). Biochar reduced bulk density and increased soil aeration and thus reduced the water‐filled pore space (WFPS) in the field, but was also able to suppress N2O emission in the microcosms experiment conducted at constant WFPS. For both experiments, biochar had limited impact on soil mineral nitrogen speciation, but it reduced the accumulation of nitrite in the microcosms. Extraction of soil DNA and quantification of functional marker genes by quantitative polymerase chain reaction showed that biochar did not alter the abundance of nitrogen‐transforming bacteria and archaea in both field and microcosm experiments. In contradiction to previous experiments, this study demonstrates the long‐term N2O emission suppression potential of a wood biochar and thus highlights its overall climate change mitigation potential. While a detailed understanding of the underlying mechanisms requires further research, we provide evidence for a range of biochar‐induced changes to the soil environment and their change with time that might explain the often observed N2O emission suppression.

Evaluation of Image Analysis to Determine the N-Fertilizer Demand of Broccoli Plants (Brassica oleracea convar. botrytis var. italica)

Numerous models have been developed for calculating optimum decision rules for nitrogen fertilization based on remote sensing techniques. New technologies related to digital image analysis may provide an alternative method to estimate nutrient status faster and more efficiently than current techniques. A series of field studies was conducted to determine the applicability of digital image analysis for nitrogen demand estimates in broccoli plants. Digital images were taken under constant light conditions in various wavelength ranges (380–1300 nm) using a digital imager. Images were processed for the parameters 𝑎∗ and 𝑏∗ in the 𝐿∗𝑎∗𝑏∗ color system. The image analysis showed a close correlation between the nitrogen status of broccoli plants and the parameter 𝑏∗ of the 𝐿∗𝑎∗𝑏∗ color system especially in the wavelength ranges 510780 and 516780 nm. The relationship between nutrient concentration in leaf dry matter and the parameters 𝑏∗ was used to determine the N fertilizer demand within the cultivation period. Estimated N amounts were applied as top dressing four weeks after setting and were 100 kg ha−1 lower than the standard fertilizer rate. Calculated N balances indicated a total uptake of applied N amounts in the image-based N treatments without considerable yield loss. Thus, digital image analysis proved to be an effective means of determining nitrogen status and adjusting fertilizer applications to preserve or enhance crop quality and yield considering sustainability.

A Model Based Ideotyping Approach for Wheat Under Different Environmental Conditions in North China Plain

Before starting a breeding program for a specific crop or variety, it can be helpful to know how traits behave in determining yield under different conditions and environments. Crop growth models can be used to generate valuable information on the relevance of specific traits for an environment of interest. In this paper, the simulation model CMS-Cropsim-CERES-Wheat was used to test the performance of input parameters which describe cultivar differences concerning plant development and grain yield. In so-called ideotyping sequences, the specific cultivar parameters were varied and the model was run with the same management information in four different scenarios. The scenarios consisted of two locations, Wuqiao (37.3°N, 116.3°E) and Quzhou (36.5°N, 115°E) in Hebei Province (North China Plain), and a dry and a wet growing season for each location. The input parameter G1 (corresponding trait: kernel number per spike) followed by G2 (corresponding trait: kernel weight) had the biggest influence on yield over all scenarios. The input parameters P1V (corresponding trait: vernalization requirement) and P1D (corresponding trait: photoperiod response) also played an important role in determining yield. In the dry scenarios a low response in vernalization and photoperiod generated a higher yield compared to a high response. The lower responses caused earliness and the period of late water stress was avoided. The last relevant parameter that affected yield was PHINT (corresponding trait: leaf area of first leaf). The simulation showed that with an increasing PHINT, yield was enhanced over all scenarios. Based on the results obtained in this study, plant breeders could carefully select the relevant traits and integrate them in their breeding program for a specific region.

Crop Models as Decision Support Systems in Crop Production

The current challenges crop production faces in the context of required yield increases while reducing fertilizer, water and pesticide inputs have created an increasing demand for agronomic knowledge and enhanced decision support guidelines, which are difficult to obtain on spatial scales appropriate for use in a multitude of global cropping systems. Nowadays crop models are increasingly being used to improve cropping techniques and cropping systems (Uehera and Tsuji, 1993; Penning de Vries and Teng, 1993; Boote et al., 1996). This trend results from a combination of mechanistic models designed by crop physiologists, soil scientists and meteorologists, and a growing awareness of the inadequacies of field experiments for responding to challenges like climate change. A general management decision to be made underlies the principle that a crop response to a certain input factor can only be expected if there is a physiological requirement and if other essential plant growth factors are in an optimum state. Hence, the challenge for a farmer is to determine how to use information with respect to the management decisions he has to make, in other words he has to find an efficient, relevant and accurate way how to evaluate data for specific management decisions. Crop models enable researchers to speculate on the long-term consequences of changes in agricultural practices and cropping systems on the level of an agro-ecosystem. Finally, models make it possible to identify very rapidly the adaptations required to enable cropping systems to respond to changes in the economic or regulatory context (Rossing et al., 1997). The following chapter gives an overview on the current knowledge and use of crop models and addresses the problems associated with these methods. In a second part the use of crop growth models for decision support in terms of yield variability, fertilizer and irrigation strategies will be discussed in the context of two global case studies, one in China and the other one in Germany. The discussion focuses on the currently available modeling techniques and addresses the necessary future research areas in this context.

Spatial variability and temporal stability of corn (Zea maysL.) grain yields – relevance of grid size

Abstract Corn yields are frequently heterogeneous across space and time. A 5-year field monitoring was conducted to determine spatial variability and temporal stability of corn grain yields within three farmer fields. The objectives of this study were to evaluate the spatial variability and temporal stability of yields. Therefore yield data was analysed at the field scale and for three different grid sizes. Results indicated that the grid size required to capture the spatial variability and temporal stability of yield differed over the fields. In general, smaller grid sizes were able to describe yield variability more precisely, whereas larger grid sizes were able to more accurately describe yield stability. To develop units for site-specific management, grid size should be determined in consideration of temporal yield stability and in consideration of the underlying factor leading to spatial yield variability. If the underlying factor is highly variable within the field, smaller grid sizes are useful. If the underlying factor is less variable within the field larger grid sizes seem to be more suitable for site-specific management.

GIS-based spatial nitrogen management model for maize: short- and long-term marginal net return maximising nitrogen application rates

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. In this work, a nitrogen prescription program has been developed that simulates the consequences of different nitrogen prescriptions using the DSSAT crop growth models. The objective of this paper is to describe a site-specific nitrogen prescription and economic optimizer program developed for computing spatially optimum N rates over long periods of weather and plant population for maize (Zea mays L.) using the CERES-Maize model. The application of the model was demonstrated on a field in Germany and another one in the USA to evaluate the concept across different environmental conditions. The user can determine the short- and the long-term optimal spatial nitrogen prescription based on crop price and nitrogen cost. The program simulated short-term optimum N applications that averaged 9% (McGarvey field, USA) and 48% (Riech field, Germany) lower than the uniform rates actually applied in the fields. The program indicated different site-specific N management options for low and high yielding fields under the assumed prices for maize and N. The implementation of a site-specific plant population management was investigated. A site-specific-optimization of plant population showed a higher profitability in the heterogeneous field in Germany. Hard pan depth, hard pan factor, root distribution factor and the percentage of available soil water across the heterogeneous field were useful indicators in predicting the magnitude of site-specific plant population benefits over uniform rates.

GIS-Based Spatial Nitrogen Management Model for Maize

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.