Spain is the main chickpea (Cicer arietinum) producing country in Europe, despite there are few studies on micronutrient application to chickpea. The response of chickpea to the applications of Zn, B and Mo was studied in pot experiments with natural conditions and acidic soils in northwest Spain from 2006 to 2008 following a factorial statistical pattern (5 × 2 × 2) with three replicates. Five concentrations of Zn (0, 1, 2, 4 and 8 mg Zn pot–1), two concentrations of B (0 and 2 mg B pot–1), and two concentrations of Mo (0 and 2 mg Mo pot–1) were added to the pots. Chickpea responded to the Zn, B and Mo applications. There were differences between soils. The mature plants fertilized with Zn, with B and with Mo had a greater total dry matter production. Harvest Index (HI) improved with the Zn application and with the Mo application. The highest HI was obtained with the Zn4× B2 × Mo2 treatment (60.30%) while the smallest HI was obtained with the Zn0 × B0 × Mo0 treatment (47.65%). The Zn, B and Mo applications improved seed yield, mainly due to the number of pods per plant. This was the yield component that had the most influence on, and the most correlation with seed yield. The highest seed yield was obtained from the Zn4 × B2 × Mo2 treatment (4.00 g plant–1) while the lowest was obtained from the Zn0 × B0 × Mo0 treatment (2.31 g plant–1). There was a low interaction between the three micronutrients. The Zn application was more efficient when it was applied with both B and Mo.

Vitis vinifera L. Grapevine water status is critical as it affects fruit quality and yield. We assessed the potential of field hyperspectral data in estimating leaf water content (C w) (expressed as equivalent water thickness) in four commercial vineyards of Vitis vinifera L. reflecting four grape varieties (Mencía, Cabernet Sauvignon, Merlot and Tempranillo). Two regression models were evaluated and compared: ordinary least squares regression (OLSR) and functional linear regression (FLR). OLSR was used to fit C w and vegetation indices, whereas FLR considered reflectance in four spectral ranges centred at the 960, 1190, 1465 and 2035 nm wavelengths. The best parameters for the FLR model were determined using cross-validation. Both models were compared using the coefficient of determination (R 2) and percentage root mean squared error (%RMSE). FLR using continuous stretches of the spectrum as input produced more suitable C w models than the vegetation indices, considering both the fit and degree of adjustment and the interpretation of the model. The best model was obtained using FLR in the range centred at 1465 nm (R 2 ¼ 0.70 and % RMSE ¼ 8.485). The results depended on grape variety but also suggested that leaf C w can be predicted on the basis of spectral signature.

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Visible, near, and shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy, a cost-effective and rapid means of characterizing soils, was used to predict soil sample properties for four vineyards (central and north-western Spain). Sieved and air-dried samples were measured using a portable spectroradiometer (350–2500 nm) and compared for pistol grip (PG) versus contact probe (CP) setups. Raw data processed using standard normal variate (SVN) and detrending transformation (DT) were grouped into four subsets (VIS: 350–700 nm; NIR: 701–1000 nm; SWIR: 1001–2500 nm; and full range: 350–2500 nm) in order to identify the most suitable range for determining soil characteristics. The performance of partial least squares regression (PLSR) models in predicting soil properties from reflectance spectra was evaluated by cross-validation. The four spectral subsets and transformed reflectances for each setup were used as PLSR predictor variables. The best performing PLSR models were obtained for pH, electrical conductivity, and phosphorous (R2 values above 0.92), while models for sand, nitrogen, and potassium showed moderately good performances (R2 values between 0.69 and 0.77). The SWIR subset and SVN + DT processing yielded the best PLSR models for both the PG and CP setups. VIS-NIR-SWIR reflectance spectroscopy shows promise as a technique for characterizing vineyard soils for precision viticulture purposes. Further studies will be carried out to corroborate our findings.