Rapid Prediction of Nutrient Concentration in Citrus Leaves Using Vis-NIR Spectroscopy.

The nutritional diagnosis of crops is carried out through costly foliar ionomic analysis in laboratories. However, spectroscopy is a sensing technique that could replace these destructive analyses for monitoring nutritional status. This work aimed to develop a calibration model to predict the foliar concentrations of macro and micronutrients in citrus plantations based on rapid non-destructive spectral measurements. To this end, 592 'Clementina de Nules' citrus leaves were collected during several months of growth. In these foliar samples, the spectral absorbance (430-1040 nm) was measured using a portable spectrometer, and the foliar ionomics was determined by emission spectrometry (ICP-OES) for macro and micronutrients, and the Kjeldahl method to quantify N. Models based on partial least squares regression (PLS-R) were calibrated to predict the content of macro and micronutrients in the leaves. The determination coefficients obtained in the model test were between 0.31 and 0.69, the highest values being found for P, K, and B (0.60, 0.63, and 0.69, respectively). Furthermore, the important P, K, and B wavelengths were evaluated using the weighted regression coefficients (BW) obtained from the PLS-R model. The results showed that the selected wavelengths were all in the visible region (430-750 nm) related to foliage pigments. The results indicate that this technique is promising for rapid and non-destructive foliar macro and micronutrient prediction.

Determination of persimmon leaf chloride contents using near-infrared spectroscopy (NIRS).

Early diagnosis of specific chloride toxicity in persimmon trees requires the reliable and fast determination of the leaf chloride content, which is usually performed by means of a cumbersome, expensive and time-consuming wet analysis. A methodology has been developed in this study as an alternative to determine chloride in persimmon leaves using near-infrared spectroscopy (NIRS) in combination with multivariate calibration techniques. Based on a training dataset of 134 samples, a predictive model was developed from their NIR spectral data. For modelling, the partial least squares regression (PLSR) method was used. The best model was obtained with the first derivative of the apparent absorbance and using just 10 latent components. In the subsequent external validation carried out with 35 external data this model reached r(2) = 0.93, RMSE = 0.16% and RPD = 3.6, with standard error of 0.026% and bias of -0.05%. From these results, the model based on NIR spectral readings can be used for speeding up the laboratory determination of chloride in persimmon leaves with only a modest loss of precision. The intermolecular interaction between chloride ions and the peptide bonds in leaf proteins through hydrogen bonding, i.e. N-H···Cl, explains the ability for chloride determinations on the basis of NIR spectra.

Advances in validating SALTIRSOIL at plot scale: first results.

SALTIRSOIL (SALTs in IRrigated SOILs) is a model for the medium to long term simulation of soil salinity in irrigated, well-drained lands. Once the algorithms were verified, the objective of our study was to validate SALTIRSOIL under one of several water quality and management scenarios in Mediterranean agriculture. Because drip and surface are the most common irrigation systems in irrigated agriculture in Valencia (Spain), the validation was performed with climate, soil, irrigation water (composition and management) and crop (species and management) information from an experimental plot surface irrigated with well water and planted with watermelon that has been monitored since the late spring of 2007. To carry out the validation, first we performed a global sensitivity analysis (GSA). Second, we compared simulated soil saturation extract composition against measured data. According to the GSA, SALTIRSOIL calculations of soil salinity seem to be most affected by climate (rainfall and evapotranspiration) with 60% of explained soil salinity variance, water salinity with 26% of explained variance, and then irrigation with 4%. According to the closeness of the first comparisons between predictions and measurements, SALTIRSOIL does not seem to be affected by any systematic error, and as a consequence, neither inclusion of new parameters nor calibration of the others already included would be needed at least for surface irrigation. The validation of SALTIRSOIL continues under other water quality and irrigation management scenarios.

Combined use of GIS and environmental indicators for assessment of chemical, physical and biological soil degradation in a Spanish Mediterranean region.

Soil is one of the main non-renewable natural resources in the world. In the Valencian Community (Mediterranean coast of Spain), it is especially important because agriculture and forest biomass exploitation are two of the main economic activities in the region. More than 44% of the total area is under agriculture and 52% is forested. The frequently arid or semi-arid climate with rainfall concentrated in few events, usually in the autumn and spring, scarcity of vegetation cover, and eroded and shallow soils in several areas lead to soil degradation processes. These processes, mainly water erosion and salinization, can be intense in many locations within the Valencian Community. Evaluation of soil degradation on a regional scale is important because degradation is incompatible with sustainable development. Policy makers involved in land use planning require tools to evaluate soil degradation so they can go on to develop measures aimed at protecting and conserving soils. In this study, a methodology to evaluate physical, chemical and biological soil degradation in a GIS-based approach was developed for the Valencian Community on a 1/200,000 scale. The information used in this study was obtained from two different sources: (i) a soil survey with more than 850 soil profiles sampled within the Valencian Community, and (ii) the environmental information implemented in the Geo-scientific map of the Valencian Community digitised on an Arc/Info GIS. Maps of physical, chemical and biological soil degradation in the Valencian Community on a 1/200,000 scale were obtained using the methodology devised. These maps can be used to make a cost-effective evaluation of soil degradation on a regional scale. Around 29% of the area corresponding to the Valencian Community is affected by high to very high physical soil degradation, 36% by high to very high biological degradation, and 6% by high to very high chemical degradation. It is, therefore, necessary to draw up legislation and to establish the policy framework for actions focused on preventing soil degradation and conserving its productive potential.

Linkage of a geographical information system with the gleams model to assess nitrate leaching in agricultural areas.

The GLEAMS (Groundwater Loading Effects of Agricultural Management Systems, Version 2.10) model was linked with a Geographical Information System (GIS) to evaluate the risk of nitrate pollution in an area of 230 km2 near Valencia (Spain). Under Mediterranean conditions, GLEAMS was calibrated and validated using results from field experiments on citrus orchards and vegetables grown in that area. A graphical user interface (GUI) was implemented in the GIS-model system to allow a non-specialist to run the model, and to represent simulation results as a thematic map. In order to execute the GLEAMS model, data must be grouped in five basic layers: four layers correspond to the base maps (soils, climate, land use, NO3 content in irrigation water), and the fifth layer corresponds to agricultural management practices, introduced in the system interacting to the GUI. To illustrate system capabilities. two rotations with crops in the vegetable area (potato/lettuce/onion/cauliflower, and artichoke/artichoke in 2 years rotation each), and orange trees in the citrus area, were simulated to determine the N leached in the study area. Pollution risk maps show that vegetable crops and areas irrigated with groundwater have the highest potential risk of groundwater nitrate pollution. Further analysis identified potato and artichoke (in the first year) to be the crops with the higher risk.