[1] Accurate estimation of spatially distributed chlorophyll content (Chl) in crops is of great importance for regional and global studies of carbon balance and responses to fertilizer (e.g., nitrogen) application. In this paper a recently developed conceptual model was applied for remotely estimating Chl in maize and soybean canopies. We tuned the spectral regions to be included in the model, according to the optical characteristics of the crops studied, and showed that the developed technique allowed accurate estimation of total Chl in both crops, explaining more than 92% of Chl variation. This new technique shows great potential for remotely tracking the physiological status of crops, with contrasting canopy architectures, and their responses to environmental changes. Citation: Gitelson,

[1] Accurate estimation of spatially distributed CO 2 fluxes is of great importance for regional and global studies of carbon balance. We applied a recently developed technique for remote estimation of crop chlorophyll content to assess gross primary production (GPP). The technique is based on reflectance in two spectral channels: the near-infrared and either the green or the red-edge. We have found that in irrigated and rainfed crops (maize and soybean), midday GPP is closely related to total crop chlorophyll content. The technique provided accurate estimations of midday GPP in both crops under rainfed and irrigated conditions with root mean square error of GPP estimation of less than 0.3 mg CO 2 /m 2 s in maize (GPP ranged from 0 to 3.1 mg CO 2 /m 2 s) and less than 0.2 mg CO 2 /m 2 s in soybean (GPP ranged from 0 to 1.8 mg CO 2 /m 2 s). Validation using an independent data set for irrigated and rainfed maize showed robustness of the technique; RMSE of GPP prediction was less than 0.27 mg CO 2 /m 2 s.

SummaryThe objective of this study was to develop a rapid non-destructive technique to estimate total chlorophyll (Chl) content in a maize canopy using Chl content in a single leaf. The approach was (1) to calibrate and validate a reflectance-based nondestructive technique to estimate leaf Chl in maize; (2) to quantify the relative contribution of each leaf Chl to the total Chl in the canopy; and (3) to establish a relationship between leaf Chl content and total Chl in a maize canopy. The Red Edge Chlorophyll Index Clred edge = (RNIRIRred edge)-l based on reflectances, R, in the red edge (720-730nm) and near infrared (770-800nm) was found to be an accurate measure of maize leaf Chl. It was able to predict leaf Chl ranging from 10 to 805 mg Chl m-2 with root mean-square error less than 38 mg Chl mP2. Relationships between Chl content in each maize leaf and total canopy Chl content were established and showed that Chl in the collar leaf before silking or ear leaves explained more than 80% and 87% of the variation in total Chl in a maize canopy, respectively. Thus, non-destructive measurements of both reflectance and area of a single leaf (either collar or ear) can be used to accurately estimate total Chl content in a maize canopy.

Chlorophyll (Chl) content is among the most important crop biophysical characteristics. Chlorophyll can be related to photosynthetic capacity, thus, productivity, developmental stage, and canopy stresses. Th e objective of this study was to quantify and characterize the temporal variation of Chl content in the vertical profi le of maize (Zea mays L.) canopies by means of a refl ectance-based, nondestructive methodology. A recently developed technique that relates leaf refl ectance with leaf pigment content has been used for accurate leaf Chl estimation. Th e technique employs refl ectance in two spectral bands: in the red edge (720-730 nm) and in the near infrared (770-800 nm). More than 2000 maize leaves were measured for refl ectance and total and green area during a growing season. A bell-shaped curve showed a very good fi t for the vertical distribution of Chl content regardless of crop growth stage. Th e parameters and coeffi cients of the bell-shape function were found to be very useful to interpret temporal changes in the vertical profi le of each variable. Comparisons among Chl, leaf area index (LAI) and green LAI showed that Chl content was more sensitive to changes in the physiological status of maize than other biophysical characteristics. Th e quantifi cation of Chl content in canopy should be seen as a useful tool to complement the information on green LAI or LAI. Its applicability will help to improve the understanding of the crop ecophysiology, productivity, the radiation use effi ciency and the interplant competition.

Nitrous oxide (N2O) is an air pollutant of major environmental concern, with agriculture representing 60% of anthropogenic global N2O emissions. Much of the N2O emissions from livestock production systems result from transformation of N deposited to soil within animal excreta. There exists a substantial body of literature on urine patch N2O dynamics, we aimed to identify key controlling factors influencing N2O emissions and to aid understanding of knowledge gaps to improve GHG reporting and prioritize future research. We conducted an extensive literature review and random effect meta‐analysis (using REML) of results to identify key relationships between multiple potential independent factors and global N2O emissions factors (EFs) from urine patches. Mean air temperature, soil pH and ruminant animal species (sheep or cow) were significant factors influencing the EFs reviewed. However, several factors that are known to influence N2O emissions, such as animal diet and urine composition, could not be considered due to the lack of reported data. The review highlighted a widespread tendency for inadequate metadata and uncertainty reporting in the published studies, as well as the limited geographical extent of investigations, which are more often conducted in temperate regions thus far. Therefore, here we give recommendations for factors that are likely to affect the EFs and should be included in all future studies, these include the following: soil pH and texture; experimental set‐up; direct measurement of soil moisture and temperature during the study period; amount and composition of urine applied; animal type and diet; N2O emissions with a measure of uncertainty; data from a control with zero‐N application and meteorological data.

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