The combined effect of climatic factors and technical advancement on yield of sugarcane by using ARDL approach: evidence from Pakistan.

Sugarcane is one of the most important crops in the world and has a major influence on environmental concerns. This study aims to examine the association between sugarcane crop yield, climate change factors, and technical advancement using time series data for the period of 1989 to 2015 in Pakistan. An autoregressive distributed lag (ARDL) model and descriptive statistics analysis were employed in this study.

Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice.

Non-destructive and rapid estimation of canopy variables is imperative for predicting crop growth and managing nitrogen (N) application. Hyperspectral remote sensing can be used for timely and accurate estimation of canopy physical and chemical properties; however, discrepancies associated with soil and water backgrounds complicate the estimation of crop N status using canopy spectral reflectance (CSR). This study established the quantitative relationships between dynamic canopy nitrogen (CN) status indicators, leaf dry weight (LDW), leaf N concentration (LNC), leaf N accumulation (LNA), and CSR-derived new hyperspectral vegetation indices (HVIs), and to access the plausibility of using these relationships to make in-season estimations of CN variables at the elongation (EL), booting (BT), and heading (HD) stages of rice crop growth.