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Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the "trilateral" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0–2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the "trilateral" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0–2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture. [ABSTRACT FROM AUTHOR]

The aim of this study was to address the problems of water scarcity and low water and fertilizer use efficiency in vineyards in the Guanzhong Plain of Shaanxi, China. In the experiment, drip fertigation technology was used, including three irrigation levels (W3 (100% M, and M as the irrigation quota), W2 (75% M) and W1 (50% M)) and four fertilization levels (F3 (648 kg/hm2), F2 (486 kg/hm2), F1 (324 kg/hm2) and F0 (0 kg/hm2)). Traditional fertilization (CG) and a rainfed (CK) treatments were used as control treatments. Field experiments were conducted in Yangling, Shaanxi, China from 2019 to 2021 (wet years in 2019 and 2020, and dry years in 2021) to determine irrigation and fertilization intervals corresponding to higher crop yields and water and fertilizer utilization efficiency in different precipitation patterns. The results showed that, compared with the CK and CG treatments, the drip irrigation and fertilization treatment increased grape yield and significantly improved water productivity and fertilizer agronomic use efficiency. The highest grape yields were obtained in the F2 treatment under drip fertigation, and the highest grape yields were 16044 kg/hm2 and 12728 kg/hm2 in wet years and dry years obtained at W1F2 and W2F2, respectively. With the same fertilization level (F2), the grape yields of the W1, W2 and W3 treatments in wet years increased by 37.95%, 23.73% and 23.50% compared with those in dry years, respectively. The optimal irrigation and fertilization intervals determined by the response surface method were: the irrigation amount ranged from 8 to 11 mm and 32–38 mm, nitrogen application ranged from 182 to 190 kg/hm2 and 162–164 kg/hm2, phosphorus application ranged from 116 to 131 kg/hm2 and 98–100 kg/hm2, and potassium application ranged from 216 to 225 kg/hm2 and 194–201 kg/hm2 in wet years and dry years, respectively. This provides a guideline for the precision management of irrigation and fertilization of grape under different precipitation year types in Guanzhong Plain of Shaanxi Province. ● Drip fertigation increased grape yield, WUE and FAUE. ● The optimal drip fertigation strategy for highest yield, WUE and FAUE was derived in wet and dry years. ● Response surface methodology was used to determine optimal irrigation and fertilization in wet and dry years. [ABSTRACT FROM AUTHOR]