Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands

Han, Fusong; Fu, Gang; Yu, Chengqun; Wang, Shaohua

doi:10.3390/rs14143410

Cited by 20 publications

(61 citation statements)

References 25 publications

(32 reference statements)

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“…Secondly, compared with vegetation productivity models (e.g., gross/net primary production models of the Moderate Resolution Imaging Spectroradiometer) [17][18][19], plant species diversity models are relatively rare. Thirdly, along with the rapid development of various science and technology including computer science and 3S technology, data mining technology has gradually entered the human field of vision and has played key roles in all walks of life [20][21][22][23][24][25][26][27], which makes it possible for us to quantify massive primary plant α-diversity data from field plant community surveys. However, there are a variety of data mining technologies, such as the models of random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees [20,27].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands

Tian

2022

Remote Sensing

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Quantitative plant species α-diversity of grasslands at multiple spatial and temporal scales is important for investigating the responses of biodiversity to global change and protecting biodiversity under global change. Potential plant species α-diversity (i.e., SRp, Shannonp, Simpsonp and Pieloup: potential species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by climate data (i.e., annual temperature, precipitation and radiation) and actual plant species α-diversity (i.e., SRa, Shannona, Simpsona and Pieloua: actual species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by normalized difference vegetation index and climate data. Six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) were used in this study. Overall, the constructed random forest models performed the best among the six algorithms. The simulated plant species α-diversity based on the constructed random forest models can explain no less than 96% variation of the observed plant species α-diversity. The RMSE and relative biases between simulated α-diversity based on the constructed random forest models and observed α-diversity were ≤1.58 and within ±4.49%, respectively. Accordingly, plant species α-diversity can be quantified from the normalized difference vegetation index and climate data using random forest models. The random forest models of plant α-diversity build by this study had enough predicting accuracies, at least for alpine grassland ecosystems, Tibet. The proposed random forest models of plant α-diversity by this current study can help researchers to save time by abandoning plant community field surveys, and facilitate researchers to conduct studies on plant α-diversity over a long-term temporal scale and larger spatial scale under global change.

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Section: Introductionmentioning

confidence: 99%

Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands

Tian

2022

Remote Sensing

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“…Asymmetric warming among elevations may homogenize gale days and wind speed among elevations, which in turn may increase dispersal limitation of anemophilae plants among elevations. Third, radiation is an important limit variable of plant species α-diversity (Tian and Fu, 2022), and generally closely correlated with precipitation Han et al, 2022a). Homogenization of wind among elevations may lead to homogenization of precipitation among elevation, which in turn may weaken the difference of radiation among elevations.…”

Section: Discussionmentioning

confidence: 99%

“…This phenomenon might be consequent to the fact that the effects of asymmetric warming among elevations on water availability, plant species and phylogenetic β-diversity, soil nitrogen and phosphorus availability, and soil pH varied with years (Wang et al, 2021b). Moreover, the effects of warming on plant production and α-diversity can be related to the background values of climatic conditions Fu and Sun, 2022), and climate conditions generally change with years (Wang et al, 2022;Han et al, 2022a;Wang and Fu, 2023). Third, the effects of warming on plant production and α-diversity can be also related to warming duration, and warming may have lagging effects on plant production and α-diversity (Fu et al, 2019;Wang et al, 2021a;Fu and Shen, 2022;Han et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…Grasslands, as an important vegetation type in global ecosystems, are key resources for high-quality development of livestock industry and an important place for wildlife protection Zha et al, 2022;Han et al, 2022a;Wang and Fu, 2023). Alpine grasslands are important sections of global grasslands, and may be the most sensitive and fragile grassland types to global change (Zhang et al, 2015(Zhang et al, , 2022Fu and Sun, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Asymmetric warming among elevations may homogenize plant α-diversity and aboveground net primary production of alpine grasslands

Han

2023

Front. Ecol. Evol.

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It is well known that asymmetric warming among elevations (i.e., warming magnitude increases with increasing elevation) will weaken the difference of air temperature among elevations. However, it remains controversial on whether asymmetric warming among elevations can homogenize plant α-diversity and above-ground net primary production (ANPP) in alpine regions. In the present study, we conducted an experiment of asymmetric warming among elevations in alpine grasslands, Northern Tibet since 2010. There were four experiment treatments, including a treatment under natural conditions at elevation 4,313 m (C4313), a treatment under natural conditions at elevation 4,513 m (C4513), a treatment under warming conditions at elevation 4,513 m (W4513) and a treatment under warming conditions at elevation 4,693 m (W4693). We investigated ANPP, taxonomic α-diversity (i.e., species richness, Shannon, Simpson and Pielou) and phylogenetic α-diversity (mean nearest taxon distance, MNTD; phylogenetic diversity, PD) in 2011–2019. There were no significant differences of mean air temperature between C4313 and W4513, or between C4513 and W4693 in 2011–2019, indicating the differences of air temperature were eliminated among elevations. Then we found that the differences of plant α-diversity and ANPP were also eliminated among elevations: (1) there were no significant differences of ANPP, Pielou and MNTD between C4313 and W4513, or between C4513 and W4693 in 2011–2019. (2) There were also no significant differences of mean species richness, Shannon and Simpson between C4513 and W4693 in 2011–2019. (3) There were also no significant differences of ANPP, species richness, Shannon, Simpson, Pielou, PD and MNTD between C4313 and W4513, or C4513 and W4693 in 2019. Therefore, asymmetric warming among elevations may homogenize plant α-diversity and aboveground net primary production in alpine grasslands, at least in Northern Tibet.

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“…The Tibetan Plateau is one of the regions with widely distributed grassland ecosystems, and thus many earlier studies have tried to explore grazing impacts on ecosystem structures and functions in alpine grasslands and related driving mechanisms (Xiong et al, 2016 ; Han et al, 2022 ). These earlier studies can provide an essential scientific foundation for adaptive grazing management of alpine grasslands.…”

Section: Introductionmentioning

confidence: 99%

Response of forage nutrient storages to grazing in alpine grasslands

et al. 2022

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Forage nutrient storages can determine livestock size and husbandry development. There is insufficient research on the response of forage nutrient storages to grazing and related driving mechanisms in alpine grasslands, especially on the Tibetan Plateau. This study conducted a grazing experiment in three alpine grassland sites along an elevation gradient (two warm-season pastures and one cold-season pasture; two alpine steppe meadow sites and one alpine meadow) of Northern Tibet. Different types of alpine grassland ecosystems, at least for forage nutrient storages, may have different responses to grazing. Warm-season grazing significantly reduced crude protein (CP) storage, acid detergent fiber (ADF) storage, and neutral detergent fiber (NDF) storage of high-quality forage by 53.29, 63.82, and 63.26%, respectively, but cold-season grazing did not significantly alter the CP, ADF and NDF storages of high-quality forage. Warm-season grazing significantly reduced CP, ADF, NDF, crude ash (Ash), ether extract (EE) and water-soluble carbohydrate (WSC) storages of the plant community by 46.61, 62.47, 55.96, 64.94, 60.34, and 52.68%, and forbs by 62.33, 77.50, 73.69, 65.05, 57.75, and 62.44% in the alpine meadow site but not the alpine steppe meadow site, respectively. Plant species and phylogenetic diversity had different relationships with forage nutrient storages. The elevation distribution of forage nutrient storages under fencing conditions were different from those under grazing conditions. Therefore, cold-season grazing can have lower negative effects on forage nutrient storages than warm-season grazing. Combined plant species with phylogenetic diversity and composition can be better in predicting forage nutrient storages. Grazing can restructure the elevation distribution of forage nutrient storages in alpine grasslands.

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Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands

Cited by 20 publications

References 25 publications

Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands

Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands

Asymmetric warming among elevations may homogenize plant α-diversity and aboveground net primary production of alpine grasslands

Response of forage nutrient storages to grazing in alpine grasslands

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