Subject: Life Sciences, Other Keywords: conservation tillage; water potential; water potential gradient; water transfer resistance; water use efficiency
Online: 9 August 2019 (04:11:21 CEST)
Water availability is a major constraint for spring wheat production on the western Loess Plateau of China. The impact of tillage practices on water potential, water potential gradient, water transfer resistance, yield, and water use efficiency (WUEg) of spring wheat was monitored on the western Loess Plateau in 2016 and 2017. Six tillage practices were assessed, including conventional tillage with no straw (T), no-till with straw cover (NTS), no-till with no straw (NT), conventional tillage with straw incorporated (TS), conventional tillage with plastic mulch (TP), and no-till with plastic mulch (NTP). No-till with straw cover, TP, and NTP significantly improved soil water potential and root water potential at the seedling stage and leaf water potential at the seedling, tillering, jointing, and flowering stages, compared to T. These treatments also significantly reduced the soil-leaf water potential gradient at the 0-10 cm soil layer at the seedling stage and at the 30-50 cm soil layer at flowering, compared to T. Thus, NTS, TP, and NTP reduced soil-leaf water transfer resistance and enhanced transpiration. Compared to T, the NTS, TP, and NTP treatments significantly increased biomass yield (BY) by 18, 36, and 40%, respectively, and grain yield (GY) by 28, 22, and 24%, respectively, with corresponding increases in WUEg of 24, 26, and 24%, respectively. These results demonstrate that NTS, TP, and NTP improved GY and WUEg of spring wheat by decreasing the soil-leaf water potential gradient and soil-leaf water transfer resistance and enhancing transpiration, and are suitable tillage practices for sustainable intensification of wheat production in semi-arid areas.
ARTICLE | doi:10.20944/preprints202202.0335.v1
Subject: Biology, Agricultural Sciences & Agronomy Keywords: Cereals; Grain protein; Near Infrared Spectroscopy (NIRS)-based sensors; Prediction algorithms; FOSS; Hone Lab
Online: 25 February 2022 (11:21:57 CET)
Achieving global goals on sustainable nutrition, health, and wellbeing will depend on delivering enhanced diets to humankind. This will require, among others, instantaneous access to information on food quality at key points within agri-food systems. Although stationary methods are usually used to quantify grain quality (wet-lab chemistry, benchtop NIR spectrometer); these do not suit many required user-cases, such as stakeholders in decentralized agri-food-chains that are typical for emerging economies. Therefore, we explored new technologies and models that might aid these particular user-cases. For this purpose, we generated the NIR spectra of 328 grain samples from multiple cereals (finger millet, foxtail millet, maize, pearl millet, sorghum) with a standard benchtop NIR Spectrometer (DS2500, FOSS) and a novel mobile NIR-based sensor (HL-EVT5, Hone). We explored a range of classical deterministic and novel machine learning (ML)-driven models to build calibrations out of the NIR spectra. We were able to build relevant calibrations out of both types of spectra. At the same time, ML-based methods enhanced the prediction capacity of calibration models compared to classical deterministic methods. We also documented that the prediction of grain protein content based on NIR spectra generated by a mobile sensor (HL-EVT5, Hone) was highly relevant for quantitative protein predictions (R2 = 0.91, RMSE = 0.97, RPD = 3.48). Thus, the findings of this study lay the foundations on which to expand the utilization of NIR spectroscopy applications for agricultural research and development.