There is an increasing need for diagnostic tools that can assess the crop nitrogen (N) nutrition status during the growth cycle. In addition to the leaf chlorophyll (Chl) content, we proposed here ...the use of the leaf content of polyphenolics (Phen) as a potential indicator of crop N status. Because of their absorption features in the visible and in the UV part of the spectrum, both Chl and Phen can be measured by rapid and non-destructive optical methods. Therefore, we used two leaf-clip devices, the Minolta SPAD-502 for Chl, and the Dualex for Phen. The latter is a prototype (patent pending) that measures the UV absorbance of the leaf epidermis, which is related to the leaf Phen content. Dynamics of Phen and Chl were measured on the last fully developed leaves of two winter wheat cultivars subjected to different levels of N availability, from tillering to flowering, in 2001, 2002 and 2003. Both Phen and Chl contents were found to increase along the leaf, starting from the ligula, regardless of the stage of development. Both variables were highly correlated with the N concentration of leaves. The average Chl content of the leaf increased, and the average Phen content decreased, with the increased application of N to the field, irrespective of the growth stage, the cultivar and the year of experiment. Therefore, both Phen and Chl can be considered as probes of the crop N nutrition status. Still, the relationship between Chl and the nitrogen nutrition index (NNI), used as a reference indicator of N deficiency, was influenced by the growth stage, whereas the year of experiment affected the relationship between Phen and the NNI. We also propose the use of the simple Chl/Phen ratio as an indicator of leaf N content at the canopy level, for future application in precision agriculture. This ratio would alleviate, at least partially, the problem of gradients along leaves, and would even accentuate the differences among levels of crop N deficiencies because of the Chl and Phen inverse dependence on the crop N nutrition status.
•LPIS data describe agricultural landscapes in most European countries.•We developed a tool to analyze the French LPIS, called RPG Explorer.•RPG Explorer describes farms, fields and crop proportion ...dynamics.•RPG Explorer also computes crop sequences and models crop rotations.
In the early 2000s a Land Parcel Identification System (LPIS) was set up by each member state of the European Union, to manage agricultural subsidies. These databases describe field geometry and landcover, and provide information on farm characteristics. LPIS data could therefore be used to describe agricultural landscape dynamics, but are seldom put to this purpose by scientists and local stakeholders because it requires the use of GIS software and programming skills. The objective of this paper is thus to present RPG Explorer, a new tool that we developed to analyze agricultural landscape dynamics with LPIS data. RPG Explorer doesn’t require any specific skills in GIS and programming allowing non specialist to deal with complex data.
RPG Explorer includes a first module which computes the changes in crop proportions and farm characteristics (numbers of farm, farm area, farm type). A second module computes crop sequences on each farmer block of LPIS data. We also included a crop rotation model in a third module.
We illustrated the use of RPG Explorer for two example neighboring catchments located in western France, the Vivier catchment (16,000ha) and the Courance catchment (15,000ha). For example, RPG Explorer easily revealed the evolution of crop proportions, such as the increase of temporary grasslands in the Courance catchment (from 7.2% of the Utilized Agricultural Area (UAA) in 2007 to 11.7% in 2013). The number of farms was also shown to vary a lot: from 230 in 2007 to 207 in 2013 for the Vivier catchment, with a subsequent increase of their mean UAA (from 101ha to 119ha). RPG Explorer also showed that more than half of the Vivier catchment UAA (59%) was occupied by only 50 farms in 2013. Concerning crop sequences, the sunflower → winter wheat sequence was the most frequent 2-year sequence in both catchments, but some differences appeared with for example, a higher proportion of winter wheat → winter wheat sequence in the Courance catchment (7.2% against 3.6%). Crop rotation modeling indicated that the rapeseed → winter wheat → sunflower → winter wheat, sunflower → winter wheat and maize monoculture were the three main crop rotations.
Finally, these examples illustrate well the ability for scientist and local stakeholders to easily describe some major agricultural landscape dynamics with RPG Explorer.
In wheat (Triticum aestivum L.), nitrogen remobilization from the vegetative organs of the crop to the grains has been shown to depend on environmental factors and genotype. We performed, for a set ...of 10 winter wheat genotypes, field experiments at six sites over a 2-yr period. By measuring nitrogen uptake at flowering (NUF from 32-284 kg ha(-1)), the amount of remobilized nitrogen (REMN from 24-228 kg ha(-1)) and nitrogen remobilization efficiency (NRE from 0.44-0.92) we were able to determine the effect of genotype and environment on the relationship between REMN and NUF. Environment and genotype had significant effects on nitrogen remobilization and nitrogen remobilization efficiency, which mainly depended on treatment (nitrogen and fungicide) and site. For environments without limiting factor during the grain-filling period, we found that REMN was not dependent on the genotype and could be estimated by a single two-parameter linear relationship (REMN = 4.13 + NUF x 0.76, r2 = 0.97). We analyzed the effect of drought stress before and after flowering, high temperature during these periods, nitrogen availability and disease pressure on REMN by comparing observed and estimated REMN. The effect of the environment on the relationship between nitrogen uptake at flowering and nitrogen remobilization depended on nitrogen uptake during grain-filling period and disease pressure and was also affected by genotype. Disease-resistant genotypes seemed to be able to keep remobilization efficiency stable in conditions of strong disease pressure, whereas nitrogen remobilization efficiency decreased strongly in susceptible genotypes under the same conditions.
Changes of agricultural land use often induce changes in hydrological behavior of watersheds. Hence, effective information regarding runoff responses to future land use scenarios provides useful ...support for decision-making in land use planning and management. The objective of this study is to develop a methodology to assess land use change scenario impacts on runoff at the watershed scale. This objective implies translating qualitative information from scenarios into quantitative input parameters for biophysical models. To do so, qualitative information from scenarios should be quantified and spatialized.
The approach is based on the combination of 2015 local land use change scenarios (SYSPHAMM method) based on local stakeholders expertise, a model of spatio-temporal allocation of crops to fields (LandSFACTS model) and a watershed runoff model (STREAM model). The study was conducted for regions underlain by silty loamy soils scattered across Northern Europe. It was applied on the Saussay watershed in Upper Normandy (France). The approach is illustrated through runoff assessment of one of the land use change scenarios (characterized by the ending of the set-aside obligation and the disappearance of dairy farming). This scenario appeared relevant for local stakeholders.
The methodology presented suggests that assessing local land use scenarios in terms of runoff requires taking into account crop allocation diversity allowed by farmers' decision rules. This requirement accounts for runoff variability at the watershed outlet since crops spatial distribution throughout the watershed, depending on farmers' specific decision rules (i.e. cropping systems), strongly condition runoff phenomenon. Besides, choices regarding scenario implementation (quantification and spatialization) should to be made according to those cropping systems.
Accordingly, taking into account crop allocation diversity due to farmers' cropping systems shows that there is a variability in terms of runoff at the watershed outlet (from 19 478
m
3 to 35 004
m
3 for the winter period and a low-intensity rainfall event for example). This variability can then be explored with local decision makers with the aim of finding solutions reducing runoff risks.
The proposed approach provides a useful source of information for assessing the responses of surface runoff of future land use changes. Such scenarios providing impact assessment on runoff should encourage both local policy makers and local actors to actively discuss the future of land use in Upper Normandy.
► Translate qualitative information from scenarios to quantitative model inputs. ► To be used to compute quantitative scenario evaluation. ► Methodology to assess LU change scenario impacts on runoff at the watershed scale. ► Crop allocation diversity shows runoff variability at the watershed outlet. ► Scenarios impact assessment on runoff encourage local actors to discuss LU future.
In response to environmental threats, numerous indicators have been developed to assess the impact of livestock farming systems on the environment. Some of them, notably those based on management ...practices have been reported to have low accuracy. This paper reports the results of a study aimed at assessing whether accuracy can be increased at a reasonable cost by mixing individual indicators into models. We focused on proxy indicators representing an alternative to the direct impact measurement on two grassland bird species, the lapwing Vanellus vanellus and the redshank Tringa totanus. Models were developed using stepwise selection procedures or Bayesian model averaging (BMA). Sensitivity, specificity, and probability of correctly ranking fields (area under the curve, AUC) were estimated for each individual indicator or model from observational data measured on 252 grazed plots during 2 years. The cost of implementation of each model was computed as a function of the number and types of input variables. Among all management indicators, 50% had an AUC lower than or equal to 0.50 and thus were not better than a random decision. Independently of the statistical procedure, models combining management indicators were always more accurate than individual indicators for lapwings only. In redshanks, models based either on BMA or some selection procedures were non-informative. Higher accuracy could be reached, for both species, with model mixing management and habitat indicators. However, this increase in accuracy was also associated with an increase in model cost. Models derived by BMA were more expensive and slightly less accurate than those derived with selection procedures. Analysing trade-offs between accuracy and cost of indicators opens promising application perspectives as time consuming and expensive indicators are likely to be of low practical utility.
Numerous agro-environmental indicators have been developed to assess the impact of farming systems on biodiversity. They can be combined into logistic models for predicting the presence of species of ...ecological interest. In general, several models are available for a given species and their practical value depends on their accuracy and the cost of measurement of their input variables. This paper aims to assess the accuracy and cost of implementation of a wide range of models predicting the presence of two grassland bird species, the lapwing
Vanellus vanellus and the redshank
Tringa totanus. Some of these models were developed using stepwise selection procedures and the others were developed by Bayesian Model Averaging. Sensitivity, specificity, and probability of correctly ranking fields (AUC) were estimated for each model from observational data. The cost of implementation of each model was computed as a function of the number and types of input variables. Results showed that the presence/absence of lapwings can be predicted more accurately than the presence/absence of redshanks, probably due to the stricter ecological requirements of lapwings. For both species, the highest AUC values were obtained with models combining habitat and management variables. The most costly models were not always the most accurate. Full models and models derived by Bayesian Model Averaging were most costly and less accurate than some of the models derived using selection procedures. When large sets of candidate variables were considered, the models selected using the BIC criterion were less costly and sometimes more accurate than the models selected using the AIC criterion.
To optimize wheat segregation for the various markets, it is necessary to add to genotype segregation, a prediction before harvest of the values of yield and grain protein concentration (GPC) for the ...different fields of the collecting area. Different tools allowing a prediction of crop production exist. Among them, the evaluation of nitrogen concentration by a chlorophyll meter (Soil–Plant Analysis Development (SPAD) readings), classically used to adapt the nitrogen fertilizer application, has been used in few works to foresee grain yield and grain protein concentration. But the relationships between N crop status and SPAD measurements varies among varieties and this genotypic effect has rarely been incorporated in models of forecasting grain quality.
This paper compares several models to forecast yield, nitrogen uptake in grain (NUG) and grain protein concentration from trials carried out in 2001 and 2002 at the INRA experiment station of Grignon (West of Paris). Trials crossed nine varieties by four (2002) or five (2001) nitrogen rates. Input variables of those models are mainly chlorophyll meter measurements (SPAD) on the penultimate leaf at GS65 and on the flag leaf at GS71 Zadoks growth stages and ear number per square meter (NE).
A square root model of yield based on NE
×
SPAD gave the best fit (RMSE
=
0.6
t
ha
−1 for both stages) if considering three different groups of genotypes. Based on the same variable, NE
×
SPAD, a quadratic model for NUG without significant effect of genotypes gave the best fit (RMSE, between 21 and 30
kg
ha
−1 depending of the growth stage). And, for GPC, considering the same three groups of genotypes, the slope of the linear model with the ratio of predicted grain nitrogen concentration to predicted yield, is the same at both stages and very close to the standard value used to calculate protein concentration from nitrogen concentration (5.7), but the predictive quality of the model is more than 10% higher at GS71 (
R
2 of 0.77) than at flowering (
R
2 of 0.64). Finally, the sensibility of the models to delay in the stage of measurement is discussed.
Genotypes x environment interactions (GEI) are more fully analyzed when genotypes and environments are well characterized. The characterization of the environments via direct measurements seems ...somewhat immediate. However, this method generates too many variates that reduce their own significance in the GEI analysis. Several methods can be used to reduce this number, e.g., a crop diagnosis combined with probe genotypes and biological indicators. Genotypes can be characterized by several methods: (1) via direct measurements, (2) using crop modelling, or (3) by comparison to probe genotypes. All these methods will be illustrated in this chapter, mainly for winter wheat (Triticum aestivum L), and their prospects for QTL detection will be discussed.
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