Floristic Composition: Dynamic Biodiversity Indicator of Tree Canopy Effect on Dryland and Improved Mediterranean Pastures

Montado is a characteristic ecosystem of the Mediterranean region. The adequate management of this silvo-pastoral ecosystem requires good understanding of the effect of factors such as tree canopy, fertilization and soil amendment on pasture growth. The main objectives of this work were: (1) to evaluate the effect of tree canopy on soil characteristics and pasture productivity and quality; and (2) to test floristic composition assessment as a bio-indicator of soil improvements (amendment and fertilization) in each study area (under and outside tree canopy). Topsoil was characterized at the beginning of the project (October 2015) and at the end of the experiments (spring 2020). Soil parameters obtained by electronic sensors (soil moisture content, soil cone index and surface temperature) were monitored monthly during the 2017/2018 pasture vegetative cycle. Pasture productivity, quality and floristic composition were evaluated every two years (2016, 2018 and 2020) in the spring flowering period. The results of the floristic inventory were submitted to a multilevel pattern analysis (Indicator Species Analysis, ISA). Pasture biodiversity was evaluated based on the calculation of richness indices. This study showed a positive effect of tree canopy on soil fertility and pasture quality (e.g., CP). Pasture productivity, on the other hand, was higher in areas outside tree canopy. The great potential of ISA as a tool for identification of bio-indicator species was also demonstrated. Pasture species were identified as ecological and dynamic attributes characteristic of each study area, before and after soil amendment and fertilization.

National Mapping of New Zealand Pasture Productivity Using Temporal Sentinel-2 Data

A national map of pasture productivity, in terms of mass of dry matter yield per unit area and time, enables evaluation of regional and local land-use suitability. Difficulty in measuring this quantity at scale directed this research, which utilises four years of Sentinel-2 satellite imagery and collected pasture yield measurements to develop a model of pasture productivity. The model uses a Normalised Difference Vegetation Index (NDVI), with spatio-temporal segmentation and averaging, to estimate mean annual pasture productivity across all of New Zealand’s grasslands with a standard error of prediction of 2.2 t/ha/y. Regional aggregates of pasture yield demonstrate expected spatial variations. The pasture productivity map may be used to classify grasslands objectively into stratified levels of production on a national scale. Due to its ability to highlight areas of land use intensification suitability, the national map of pasture productivity is of value to landowners, land users, and environmental scientists.

Effect of deferred grazing during late spring and summer on pasture productivity in Waikato and Bay of Plenty hill country

Deferred grazing is a commonly used tool to manage feed surpluses. The effect of deferred grazing on pasture nutritive value and productivity was quantified in a split-paddock trial on three hill country farms in Waikato and Bay of Plenty from October 2018 until May 2020. Livestock were excluded from the deferred pasture between mid-October 2018 and March 2019. Thereafter, both treatments were rotationally grazed in common with cattle or sheep depending on the farm. Total annual dry matter production was 15% greater in the deferred than grazed treatment for the 12 months after deferring (8.9 vs 7.7 t DM/ha, P<0.05). Metabolisable energy (ME) values at the end of the deferred period were lower in the deferred than grazed treatment (P<0.01) but similar in both treatments thereafter. The content of legumes other than white clover (Trifolium repens) was higher in deferred than grazed pastures in spring 2019 on one of the farms (treatment × farm interaction P<0.05). Ground cover of perennial ryegrass was greater and the area of bare ground smaller, in the deferred than grazed treatment on three of five occasions from after the beginning of the deferred period until up to 8 months after deferring (P<0.05). There was no difference between treatments in decomposition and stabilisation of organic matter (P>0.05). The topsoil water content was higher in the deferred than grazed treatment for 12 months after deferring. In comparison to regular grazing between October and March, deferred pastures provided drought feed in autumn 2019. Pasture productivity was increased after the deferred period without negative impacts on ME.

Study of Pasture Productivity for Semi-Intensive Cattle System during Dry Season in the South Konawe Regency, Southeast Sulawesi

This study was aimed to evaluate the characteristics and productivity of pasture for semi-intensive cattle system in South Konawe Regency during dry season. The study was conducted in August-September 2019 as dry season based on the weather condition and was determine by Department of Statistics Center of South Konawe Regency. A total of 55 selected smallholder farmers in two areas in South Konawe Regency, namely West Ranomento (30) and Konda (25) Districts. Data were obtained by interview to find out the farmers profile and pasture characteristics. Production was measured by forage sampling (1x1m) to calculate annual production. Samples were proximately analyzed to calculate dry matter production and the carrying capacity. Data were analyzed descriptively and statistically with an independent sample t-test. The botanical composition in both locations was highly dominated (>90%) by field grasses. The grassland ownership was not different between two areas. Grassland ownership at West Ranomento and Konda were 0.78±0.14 and 0.21±0.04 Ha/farmer, respectively. The pasture production between West Ranomento (2.27±0.10 ton/ha) and Konda District (1.76±0.05 ton/ha) was significantly different (p<0.05),. Pasture production and carrying capacity indicated that conditions in West Ranomento were significantly higher (p<0.05) compared to Konda District. The fresh forages production was 10.98±2.26 vs. 2.17±0.51 ton/year, forages that can be consumed was 3.30±0.67 vs. 3.30±0.67ton/year, dry matter production was 0.72±0.15 vs. 0.18±0.04 ton/year and carrying capacity was 0.22±0.04 vs. 0.05±0.01 AU/year respectively. It was concluded that the pasture productivity at the study area during dry season was very low based on productivity and carrying capacity.

Estimation of Productivity in Dryland Mediterranean Pastures: Long-Term Field Tests to Calibration and Validation of the Grassmaster II Probe

The estimation of pasture productivity is of great interest for the management of animal grazing. The standard method of assessing pasture mass requires great effort and expense to collect enough samples to accurately represent a pasture. This work presents the results of a long-term study to calibrate a Grassmaster II capacitance probe to estimate pasture productivity in two phases: (i) the calibration phase (2007–2018), which included measurements in 1411 sampling points in three parcels; and (ii) the validation phase (2019), which included measurements in 216 sampling points in eight parcels. A regression analysis was performed between the capacitance (CMR) measured by the probe and values of pasture green matter and dry matter (respectively, GM and DM, in kg ha−1). The results showed significant correlations between GM and CMR and between DM and CMR, especially in the early stages of pasture growth cycle. The analysis of the data grouped by classes of pasture moisture content (PMC) shows higher correlation coefficients for PMC content >80% (r = 0.775; p < 0.01; RMSE = 4806 kg ha−1 and CVRMSE = 28.1% for GM; r = 0.750; p < 0.01; RMSE = 763 kg ha−1 and CVRMSE = 29.7% for DM), with a clear tendency for the accuracy to decrease when the pasture vegetative cycle advances and, consequently, the PMC decreases. The validation of calibration equations when PMC > 80% showed a good approximation between GM or DM measured and GM or DM predicted (r = 0.959; p < 0.01; RMSE = 3191 kg ha−1; CVRMSE = 23.6% for GM; r = 0.953; p <0.01; RMSE = 647 kg ha−1 and CVRMSE = 27.3% for DM). It can be concluded that (i) the capacitance probe is an expedient tool that can enable the farm manager to estimate pasture productivity with acceptable accuracy and support the decision-making process in the management of dryland pastures; (ii) the more favorable period for the use of this probe in dryland pastures in a Mediterranean climate, such as the Portuguese Alentejo, coincides with the end of winter and beginning of spring (February–March), corresponding to PMC > 80%.

Forecast‐Cartographic Modeling of Pasture Production of the Volgograd Region Based on Remote Sensing

Aim. The work is devoted to identifying the productivity of pasture landscapes in the Volgograd region. The aim was to determine the direction of trends and the values of the coefficients of proportionality which would permit the definition of areas where the productivity of natural zonal vegetation has increased or decreased from 2000 until today. Material and Methods. Pasture productivity assessment is based on the analysis of the NDVI vegetation index, which is widely used in such studies. For analysis, specific pasture areas were identified in accordance with Global Land Cover, divided into egular grids and given overlays corresponding with the boundaries of municipalities and landscapes. Results. The largest areas of natural zonal pastures are located in the Trans‐Volga region and on the sandy massifs of the Don River valley. About 60% of pasture land has an average weighted average long‐term NDVI value from 0.3 to 0.4, and approximately a quarter – from 0.4 to 0.5. In most parts of the region there are negative NDVI trends. The highest rate of degradation is noted in the Trans‐Volga region. This is associated with larger pasturing loads than in the rest of the region, as well as with the regular occurrence of steppe fires. Conclusion. In summation: the productivity trends of zonal pastures in the Volgograd region have been determined, as have areas with different NDVI directions and dynamics. The application of these results in practice should make it possible to predict pasture productivity in various municipal districts and landscapes of the region, and thus assist in the regulation of pasture loads and the mitigation of risks of vegetation degradation.