Soil water deficit and recovery under different vegetation types on the Chinese Loess Plateau

Characterizing soil water content (SWC) dynamics is a prerequisite for conducting sustainable vegetation restoration on the Chinese Loess Plateau. However, quantifying the variations of the SWC in the deep soil layers remains a challenge because of the different driving factors and the complexity of surface processes. In this study, SWC in 0–10 m of artificial forestlands (AF), apple orchard (AO), native forestland (NF), farmland (maize; FL), and native grassland (NG) were monitored during 2019–2020. The deficit size (DS) and recovery index (RI) were used to explore the effects of vegetation types on SWC. The results showed that the SWCs of forestlands were significantly lower than the SWC of native grassland (12.32%) and tree species significantly affected the SWC. The monthly DS values in forestlands were negative, while those of FL were positive. The DS value in 0-10 m and predictive values below 10 m were negative of forestlands. Thus, tree planting may have consumed soil water at a depth of > 10 m. During the investigation period, soil water was restored in 0–1 m with the positive RI values. In addition, artificial forestlands showed good performance in deep soil water recovery. Canopy density was the controlling factor for soil water restoration. Our results demonstrated that the current afforestation mode used more soil water but was conducive to deep soil water conservation. Therefore, reasonable adjustments should be made according to the local soil and water resources for future vegetation selection and management.

Mapping Grasslands in Mixed Grassland Ecoregion of Saskatchewan Using Big Remote Sensing Data and Machine Learning

Accurate spatial distribution information of native, mixed, and tame grasslands is essential for maintaining ecosystem health in the Prairie. This research aimed to use the latest monitoring technology to assess the remaining grasslands in Saskatchewan’s mixed grassland ecoregion (MGE). The classification approach was based on 78 raster-based variables derived from big remote sensing data of multispectral optical space-borne sensors such as MODIS and Sentinel-2, and synthetic aperture radar (SAR) space-borne sensors such as Sentinel-1. Principal component analysis (PCA) was used as a data dimensionality reduction technique to mitigate big data load and improve processing time. Random Forest (RF) was used in the classification process and incorporated the selected variables from 78 satellite-based layers and 2385 reference training points. Within the MGE, the overall accuracy of the classification was 90.2%. Native grassland had 98.20% of user’s accuracy and 88.40% producer’s accuracy, tame grassland had 81.4% user’s accuracy and 93.8% producer’s accuracy, whereas mixed grassland class had very low user’s accuracy (45.8%) and producer’s accuracy 82.83%. Approximately 3.46 million hectares (40.2%) of the MGE area are grasslands (33.9% native, 4% mixed, and 2.3% tame). This study establishes a novel analytical framework for reliable grassland mapping using big data, identifies future challenges, and provides valuable information for Saskatchewan and North America decision-makers.

Herbage mass and allowance and animal genotype affect daily herbage intake, productivity, and efficiency of beef cows grazing native subtropical grassland

The beef sector in Campos grasslands must increase animal productivity without external inputs while reducing environmental impact. The objective of this study was to estimate herbage intake [g/metabolic body weight (MBW)/d] of straightbred (Hereford/Angus) and crossbred (F1 of Hereford x Angus) beef cows grazing subtropical native grassland with High and Low herbage allowance (HA, 5 vs 3 kg DM/kg BW) during gestation and lactation and its relationship with biological efficiency of cow-calf productivity. Herbage intake (estimated via n-alkanes C32:C33 ratio) was measured during early (Ge1, -163 d prior calving) and mid to late [Gm1 (-83) and Gm2, (-90 d prior calving)] gestation and lactation (L0, L1 and L2, 60, 47 and 31d following calving) periods in 24-36 cows, selected to create 8 groups (4 per block) of HA x cow genotype treatment. Cows grazed native grassland year-round, under High and Low HA (except in winter). We analyzed the effect of cow genotype (straightbred vs. crossbred cows) and HA (High vs. Low) on herbage mass and height, daily herbage intake rate (DMI), cow body condition score (BCS), calf average daily gain (ADG) and body weight at weaning (BWW) and g of calf weaned/kg DMI. High allowance improved DMI during lactation periods (High 115.6 vs Low 94.1±5.3 P<0.05 g/MBW/d). Crossbred cows decreased DMI during gestation (crossbred 81 vs. straightbred 94±4.3 P=0.05 g/MBW/d) compared to straightbred cows. Crossbred and High HA improved biological efficiency, 40.0 vs. 26.2 and 36.0 vs. 29.7 g of calf/kg DMI respectively. High allowance increased herbage mass and sites with greater canopy height which allow greater DMI, positively associated with cow BCS at weaning, calf ADG, BWW, and g of calf/kg DMI. Crossbred cows reduced DMI during gestation showing no greater annual DMI. Animal productivity and biological efficiency can be improved using High HA and crossbred cows, which should decrease the environmental impact of cow-calf systems.