Vegetation height of patch more important for phytodiversity than that of paddock

Wrage, N.; Demirbağ, Nurdan Şahin; Hofmann, M.; Isselstein, J.

doi:10.1016/j.agee.2012.04.008

Cited by 18 publications

(22 citation statements)

References 25 publications

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“…The plant association was a moderately species-rich Lolio-Cynosuretum [25]. The pastures exhibited pronounced heterogeneity in sward structure, with short and tall patches and various sward height classes [26,27].…”

Section: Study Area and Site Characteristicsmentioning

confidence: 99%

“…Three levels of grazing intensity were allocated to adjacent pasture paddocks of 1 ha size, which were continuously stocked by cows from the beginning of May to mid-September. Grazing intensities were: (a) moderate stocking, average of 3.4 standard livestock units (SLU, i.e., 500 kg live weight) ha −1 ; (b) lenient stocking, average 1.8 SLU ha −1 ; and (c) very lenient stocking, average 1.3 SLU ha −1 [25]. To ensure extensive sward variation for data assessment, one representative study plot of 30 × 50 m size was selected within each of the three paddocks using a grazed/ungrazed-classified aerial image to obtain comparable surface proportions.…”

Section: Study Area and Site Characteristicsmentioning

confidence: 99%

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Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

et al. 2017

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An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH) with narrow band normalized difference spectral index (NDSI) (R 2 CV = 0.52) or simulated WorldView2 (WV2) (R 2 CV = 0.48) satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R 2 CV = 0.48). Spectral regions related to plant water content were found to be of particular importance (996-1225 nm). Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.

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Section: Study Area and Site Characteristicsmentioning

confidence: 99%

Section: Study Area and Site Characteristicsmentioning

confidence: 99%

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

et al. 2017

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“…2014, respectively). Swards of the latter two stocking rates showed similar USH levels in both years, although pastures were managed and monitored by the use of a compressed sward height meter (CSH; according Castle, 1976), maintaining levels at 6 cm (moderate), 12 cm (lenient) and 18 cm (very lenient) (Wrage et al, 2012). This disparity may indicate the influence of sward structure on the conducted measurement methods: while CSH reflects the resistance of biomass according to stem density and sward height (Hakl et al, 2012), USH predominantly detects protruding objects regardless of other sward conditions in subordinate layers (Fricke et al, 2011).…”

Section: Assessment Of Position Accuracymentioning

confidence: 99%

“…Three target paddocks of 1 ha each with different continuous stocking treatments were selected from the experiment. Treatments were (a) moderate stocking, with an average of 3.4 standard livestock units (SLUs, i.e., 500 kg live weight) ha −1 ; (b) lenient stocking, with an average 1.8 SLU ha −1 ; and (c) very lenient stocking, with an average 1.3 SLU ha −1 (Wrage et al, 2012). In each paddock one study plot of 30 × 50 m size was established, to represent spatial variability in pastures under different grazing intensities during 2-year measurements.…”

Section: Experimental Site and Setupmentioning

confidence: 99%

Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system

Safari

Fricke

Reddersen

et al. 2016

J. Sens. Sens. Syst.

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Abstract. The present study aimed to test a mobile device equipped with ultrasonic and spectral sensors for the assessment of biomass from diverse pastures and to compare its prediction accuracy to that from static measurements. Prediction of biomass by mobile application of sensors explained > 63 % of the variation in manually determined reference plots representing the biomass range of each paddock. Accuracy of biomass prediction improved with increasing grazing intensity. A slight overestimation of the true values was observed at low levels of biomass, whereas an underestimation occurred at high values, irrespective of stocking rate and years. Prediction accuracy with a mobile application of sensors was always lower than when sensors were applied statically. Differences between mobile and static measurements may be caused by position errors, which accounted for 8.5 cm on average. Beside GPS errors (±1-2 cm horizontal accuracy and twice that vertically), position inaccuracy predominantly originated from undirected vehicle movements due to heaps and hollows on the ground surface. However, the mobile sensor system in connection with biomass prediction models may provide acceptable prediction accuracies for practical application, such as mapping. The findings also show the limits even sophisticated sensor combinations have in the assessment of biomass of extremely heterogeneous grasslands, which is typical for very leniently stocked pastures. Thus, further research is needed to develop improved sensor systems for supporting practical grassland farming.

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“…This is also a method accepted for predicting yield in lucerne (Medicago sativa L.) monocultures (Hakl et al 2012). In grasslands, RPM was mostly used for estimating yield in pastures (Virkajärvi 1999, Martin et al 2005, Wrage et al 2012). The observed R 2 value was in the range 0.22-0.88 for correlations between CH and the above-ground dry-matter for naturalized pastures (Martin et al 2005, Fehmi andStevens 2009).…”

mentioning

confidence: 99%

The use of compressed height to estimate the yield of a differently fertilized meadow

Ondřej¹,

Hakl²,

Hejcman³

et al. 2018

Plant Soil Environ.

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Monitoring of grassland dry matter yield (DMY) is important for the economy and ecosystem management, but it is a time-consuming process. Calculating the correlation between compressed height (CH) and DMY is a faster way to estimate DMY. The aim of our study was to use CH in order to predict DMY for a meadow with different fertilization management and plant species composition. Four fertilization treatments and one unfertilized control were established in a mesophilic meadow in the Czech Republic. Using a rising plate meter (RPM), CH was measured before the first and second cuts. In addition, the cover of individual vascular plant species was estimated. Significant correlations between CH and DMY were ranging from 0.41 to 0.79 for treatments without nitrogen fertilization in the first and second cuts; for treatments with nitrogen fertilization there was a significant correlation only in the second cut. According to our results, the RPM method seems to be suitable for a rough DMY estimate for meadows with coverage of about 60% grasses, 10% legumes and 30% forbs. However, considerable changes in the cover of tall forbs (e.g. Urtica dioica L.) or tall grasses (e.g. Dactylis glomerata L.) could be the main sources of DMY estimation inaccuracy.

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Vegetation height of patch more important for phytodiversity than that of paddock

Cited by 18 publications

References 25 publications

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

Fusion of Ultrasonic and Spectral Sensor Data for Improving the Estimation of Biomass in Grasslands with Heterogeneous Sward Structure

Comparing mobile and static assessment of biomass in heterogeneous grassland with a multi-sensor system

The use of compressed height to estimate the yield of a differently fertilized meadow

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