Assessing Livestock Forage Diversity: Species-family Extrapolation in Lowland Regions of West Papua, Indonesia

Deny Anjelus Iyai^1*, Meky Sagrim², Yubelince Yustensi Runtuboi³ and Stepanus Pakage^1
*Correspondence: Deny Anjelus Iyai, Faculty of Animal Science, Papua University, Jl. Gn. Salju, Amban, Kec. Manokwari Bar., Kabupaten Manokwari, Papua Bar. 98314, Indonesia, Tel: +81-240925007, Email:

Keywords

Extrapolation • iNext • Manokwari • Rareflaction • Warpramasi • West Papua

Introduction

The intricate interplay between biodiversity and ecological dynamics is a subject of paramount importance in the realm of environmental science. In the unique ecosystem of Warpramasi Valley, located in Manokwari, Papua Barat, Indonesia, the rich tapestry of lowland grasses plays a crucial role in shaping the ecological landscape [1]. This study delves into the nuanced intricacies of species-family districts, employing extrapolation as a tool to unravel the diversities and potencies inherent in the lowland grasses of Warpramasi Valley. Understanding the distribution, characteristics, and ecological roles of lowland grasses is essential for comprehensive ecosystem management, biodiversity conservation, and sustainable land use practices. By examining these elements within the context of species-family districts, this research seeks to contribute valuable insights into the intricate web of ecological relationships that define the Warpramasi Valley.

Papua Barat, a region known for its exceptional biodiversity, offers a unique ecological laboratory for studying plant species, particularly the lowland grasses that thrive in the distinctive conditions of Warpramasi Valley. The valley, situated in the larger context of Manokwari, presents an ecosystem characterized by a confluence of diverse environmental factors, including topography, climate, and soil composition, contributing to the rich tapestry of flora. However, despite the ecological significance of lowland grasses, there exists a notable gap in the understanding of their distribution, diversity, and ecological roles within the Warpramasi Valley.

Extrapolation is the process of estimating or predicting values outside the range of observed data based on existing information. In the context of our study, "Species-family (in-ex) trapolation as diversities measurement of livestock forage potencies from lowland Warpramasi Manokwari West Papua Indonesia," it seems to involve using extrapolation techniques to measure the diversities of livestock forage potencies in lowland areas. Species-family extrapolation suggests that the study involves analysing different species within a particular family of plants or forages [2]. Extrapolation in this case may involve predicting the forage potencies of species within the family that are not directly observed or measured. Diversities measurement aims to measure the diversity of livestock forage potencies. This could involve assessing the nutritional content, availability and suitability of various forage species for livestock in the specified region [3].

Lowland Warpramasi, Manokwari, West Papua, Indonesia is the geographical context which is crucial to explored. Warpramasi lowland is surrounded by primary forest patches, palm oil plantation, paddy field, farming land and communal land [1]. This exotic environment will shape unique, high, and rich diversities of the native plants as forages sources. Different regions may have unique ecosystems and forage varieties, making it important to understand the specific conditions influencing livestock forage in this location.

The importance of this study is concepted in several scientific ways. Livestock management is practiced widely in this lowland of Warpramasi. Understanding the diversity of forage potencies can contribute to more effective and sustainable livestock management practices. Farmers and animal breeders can make informed decisions about feeding strategies, leading to improved animal health and productivity [4].

Biodiversity Conservation is the crux to assessing the diversity of forage species also indirectly contributes to the understanding of local biodiversity. Identifying and preserving a variety of forage plants can have positive implications for the overall ecosystem and contribute to biodiversity conservation efforts.

Agricultural sustainability may provide insights into sustainable agricultural practices by promoting the cultivation and utilization of diverse forage species [5]. This can help in mitigating the impact of monoculture and enhance the resilience of livestock farming systems. At last, this study on species-family extrapolation for measuring livestock forage potencies in Warpramasi, Manokwari, West Papua, Indonesia, is important for its potential contributions to livestock management, biodiversity conservation, and agricultural sustainability in the region. It aims to fill knowledge gaps in understanding the diversity and potential of forage resources, ultimately benefiting both farmers and the local ecosystem.

The identification and categorization of species-family districts provide a structured framework for analyzing the intricate patterns of lowland grasses, facilitating a comprehensive understanding of their ecological significance. This research aims to address this gap by employing extrapolation techniques, shedding light on the spatial and temporal variations of lowland grasses and their relationships with the broader ecosystem. Through this study, we aim to not only contribute to the academic discourse on biodiversity but also to provide practical insights that can inform conservation efforts and sustainable land use practices in the dynamic and ecologically sensitive region of Warpramasi Valley. The novelty of this field research is analysis of identified forage plant species around Warpramasi lowland valley using application of iNEXT will increase the numbers of identified plants as forages. Trends of the figures will be recognized by the graphs that have not been detected and are still present in the ecosystem.

Materials and Methods

Sites description

Manokwari Regency is divided into 9 districts, which have a total area of 4,650.32 km². Manokwari Regency with its 9 districts is astronomically placed below the equator, between 0"14' S and 130"31' E [6]. The geographical boundaries of Manokwari Regency are in the West bordering Tambrauw Regency, in the North it is bordered by the Pacific Ocean, while in the East is the Pacific Ocean and the South is the Arfak Mountains District and South Manokwari (Figure 1).

The selected fourth districts are Warmare, Prafi, Masni, and Sidey. These selected sites chosen by considering areas where livestock are massively raised in the free-ranches systems and bordering with primary forest and palmoil plantation areas. Land use in the study areas are predominantly grown by tropical rain forest (64.31%), followed by palm oil plantation (23.16%), community land (4.88%), transmigrating land (2.12%), arable land (2.09%), and the riverbanks (1.54%). The rest of less than 1.00% is occupied by ponds (0.11%), grasslands (0.0016%), terrestrial empty land (0.85%), coastal empty land, paddy field (0.78%).

Sample collection

The plant species and family selection identified using quadrant 1 × 1 m² specifying the criteria used for selecting 5 plots from each of the fourth districts of lowland grass plants. In total we counted 20 plots and resulting 100 sampling plots [1].

Data collection

The diversity indices used to quantify the diversity of lowland grasses. Describe the calculations or statistical methods employed. Extrapolation Method was used by outline the extrapolation method used to draw speciesfamily districts' extrapolation [7].

Statistical analysis

We employed specify the statistical software used for data analysis is iNext by Chao A, et al. [7]. Include details on statistical tests and significance levels. Identification of grass species including families was carried out by using a determination key book (identification book) entitled Weed of Rice in Indonesia editor by Soerdjani M, et al. [8] and Steenis Van. Data analysis results are presented in the form of maps, tables, graphs and pictures.

Results

Species distribution

Around research location of the fourth districts, at least there are 195 species found. In Sidey district, 41 (21.03%) species of plants were identified. Meanwhile, in the Masni district, 43 (22,05%) species identified. In the Prafi districts, 53 (27,18%) plant species were found each (Table 1). In Warmare district 58 (29,74%) plant species.

In the table above, there are 21 plant species that were not found in all district plots, which were not evenly distributed in all four districts. The Sidey district includes 10 species viz Paspalum conjugate P.J. Bergius, Phyllanthus niruri L., Croton hirtus L. her., Sida rhombifolia L., Oldenlandia corymbosa L., Cleome rutidosperma DC., Leucas davandulifolia SM., Mimosa pudica Linn., Ficus septica, and Musa akuminata Colla. In the Masni district there are 5 species namely Borreria laevis (Lamk.) Griseb., Hyptis capitata jacq, Selaginella willdenowii (Desv.ex.Poir.) Baker, Piper aduncum L and Cynedrella nodiflora (L.) Kunth. In the Prafi district there are 4 species namely Cyperus rotundus L., Gynura sp., Grona triflora (L.) H. Ohashi & K. Ohashi, and Cyperus distans L. In the Warmare district there are 3 species namely Asystasia gengatica (L.) T Anderson , Mikania micrantha Kunth and Eleusine indica (L) Geartn.

In Sidey district, Mikania micrantha Kunth has dominant distribution (29,27%) and therefore exist in the first rank, followed by Ageratum conyzoides L. on second rank with 21.95%, Nephrolepis falcata (Sw.) Schoott in the third rank along with Paspalum conjugatum P.J. Bergius (17.07%). The fourth rank is Phyllanthus nururi L. (14.63%). Along with district of Masni, the first is ranked by Cyperus monocephala Endl. (23.26%), followed by Borreria leavis (Lamk.) Griseb. (20.93) in the second rank, and in the third ranked by three species, i.e. Axonopus compressus (Sw.) P. Beauv, Hyptis capitata jacq., and Selaginella willdenowii (Desv.ex.Poir) Baker (18.60%).

Plant species-dominant distribution in the Prafi district consist of Chromolaea adorate (L.) Ramking & H.rob (22.64%), followed by Cyperus rotundus L. (20.75%) as second rank, and in the third position ranked by Gynura sp. (18.87%), Axonopus compressus (Sw.) P. Beau. (18.87%), and Grona trifloral (L.) H. Ohashi & K. Ohashi (18.87%). Figure in Warmare district shown Asystasia gengatica (L.) T Anderson (34.48%) as the first rank, followed by the second ranked, i.e. Mikania micrantha Kunth (17.24%), and Chromolaena odorata (L.) Rmking & H. Rob. (17.24%). In the third place, there are two species, i.e. Ageratum conyzoides L. (15.52%) and Colopoginium mucunoides DESV. (15.52%).

The trend of species population growth was shown by employing iNext graphs. The iNEXT features two statistical analyses (non-asymptotic and asymptotic) for species diversity based on Hill numbers. The first one is a nonasymptotic approach based on interpolation and extrapolation. The second one is an asymptotic approach to infer asymptotic diversity.

iNEXT computes the estimated asymptotic diversity profiles. It is based on statistical estimation of the true Hill number of any order q >= 0; see Chao and Jost (2015) for the statistical estimation detail. iNEXT computes the estimated diversities for standardized samples with a common sample size or sample completeness. This approach aims to compare diversity estimates for equallylarge (with a common sample size) or equally-complete (with a common sample coverage) samples; it is based on the seamless rarefaction and extrapolation (R/E) sampling curves of Hill numbers for q = 0, 1 and 2. iNEXT offers three types of R/E sampling curves, i.e. sample-size-based (or size-based) R/E sampling curves. This type of sampling curve plots the diversity estimates with respect to sample size. Coverage based R/E sampling curves. This type of sampling curve plots the diversity estimates with respect to sample coverage. Sample completeness curve: This curve depicts how sample coverage varies with sample size. The sample completeness curve provides a bridge between the size- and coverage-based R/E sampling curves.

The vast majority of the sample-based rarefaction and extrapolation curves assessing richness, and all of the curves addressing Shannon and Simpson diversity 20 quadrat assessments (Figures 2-19). Analysis of the flat curve of species and family richness shows that with this technique of identifying and calculating the number of species and families, it is able to reach its detection limit with a horizontal graphic location and position. However, it can still be predicted that there are other plant species that have not been detected and are still present in the ecosystem.

Family distribution

At the research location in Sidey district, 7 families of grass, legume and non-grass/legume plants were found. Meanwhile, in the Masni district, 6 families were found (Table 2). In the other two districts, 5 and 6 plant families were found, respectively. Thus, in the plains of Warpramasi, 24 families of grass, legume and non-grass/non-legume plants were found.

In the Warpramasi lowland valley we identified 428 families. 23,36% found in Sidey district, 21,03% found in Masni district, and the last two districts subsequently Prafi and Warmare are 28,04% and 27,57%. There are 428 families found in total above fifth rank of family plants in the observation plots. The eleventh plants are commonly distributed around the districts, namely Compositae, Poaceae, Fabaceae, Rubiaceae, Cyperaceae, Moraceae, Lamiaceae, Melastomataceae, Acantaceae, Peperomiaceae and Verbenaceae.

Compositae is more commonly found in the fourth districts compared to Fabaceae (Warmare n=9, 7,73%), Acantaceae (Warmare n=27, 22,88%) and Peperomiaceae (Warmare n=9, 7,63%). In addition, Peperomiaceae and Verbenaceae are only found in the Warmare district. Several types of grass and legume plant families spread over these four districts are Compositae, Poaceae, Fabaceae, Rubiaceae, Cyperaceae, Moraceae and Lamiaceae. Kusmana and Hikmat (2015) [9], Teuscher, Firison and Brata, Nahlunnisa Zuhud and Santosa and Prihantoro confirmed similar finding from some numbers of Family plants. In the other sides of the world, the plant family also recorded by several scholars such as Naah and Braun in West Africa, Qian and Liu in China, Tulu and Hernández-Yáñez in United State of America.

Discussion

The identification of plant species and families is a crucial aspect of optimizing livestock forage potentials [10], especially in tropical environments where biodiversity is high. This concept aims to explore the intersection of botanical sciences [11] and agricultural knowledge [12] to enhance our understanding of plant species and families that hold significant value for livestock forage in tropical regions. Botanical sciences and taxonomy constitute plant morphology studies. Understanding the morphological characteristics of plants is fundamental to identifying different species. Leaf structure, growth habits, and reproductive features provide valuable insights. Taxonomic classification frequently done by utilizing taxonomic systems which helps to categorize plants into families, genera, and species. This knowledge forms the basis for understanding plant relationships and characteristics. Genetic and Molecular Approaches dealt with Employing DNA barcoding techniques helps in accurate species identification, particularly when dealing with morphologically similar plants. Studying the genetic diversity within plant populations [13] aids in selecting forage species that are resilient to environmental changes and diseases. Our finding under tropical lowland ecosystem which is bordered with primary forest has less numbers reported by Iyai DA, et al. [1] di Manokwari and greater numbers of species compared to several studies in Indonesia such as Riau and Seluma regency [14].

Ecological adaptations for biotic and abiotic factors will examine how plants adapt to the tropical environment, considering factors like soil composition, precipitation [10], and temperature aids in identifying species suitable for livestock forage. Ecological niches as well will be useful in understanding the ecological niches occupied by specific plant species and helps in predicting their distribution and potential success as forage under varying conditions.

Chemical analysis and nutritional profiling consist of phytochemical analysis. Conducting phytochemical analyses allows for the identification of compounds that may influence forage quality, including anti-nutritional factors or beneficial compounds. Nutritional content, assessing the nutritional value of plants, including protein, fiber, and mineral content, is vital for evaluating their suitability as livestock forage.

Ethnobotany and traditional knowledge is an indicator of indigenous knowledge. Incorporating traditional knowledge from local communities provides insights into historically valued forage plants. This knowledge may highlight resilient species with proven benefits for livestock. Further study may be planned to cover the crucial issue of ethnobotanical survey. Conducting surveys shall be done to document the use of plants in traditional medicine or as forage can contribute to identifying valuable species.

Agronomic practices and livestock preferences shall considering the palatability of forage plants is crucial for successful integration into livestock diets. Adaptation to Grazing will rule our understanding on how plant species respond to grazing pressure and their ability to recover is vital for sustainable forage management.

Integrating botanical sciences with agricultural knowledge is paramount for identifying plant species and families with optimal livestock forage potentials in tropical environments. By combining traditional wisdom, ecological insights, chemical analyses, and advanced genetic approaches, a comprehensive understanding can be achieved. This will lead to sustainable forage management practices that benefit both livestock and the ecosystems they inhabit [15].

Conclusion

Identified 195 species distributed around Warpramasi lowland valley as well as identified 428 families. We succeed in identifying species plants in Sidey district, i.e. 41 (21.03%), Masni district is 43 species (22.05%), Prafi district 53 species (27.18%), and in Warmare district 58 species (29.74%). The distribution of Families around the districts consists of 23,36% in Sidey district, 21,03% found in Masni district, and the last two districts subsequently Prafi and Warmare are 28,04% and 27,57%. The eleventh plants are commonly distributed around the districts, namely Compositae, Poaceae, Fabaceae, Rubiaceae, Cyperaceae, Moraceae, Lamiaceae, Melastomataceae, Acantaceae, Peperomiaceae and Verbenaceae. From analysis of interpolation and extrapolation using Rareflaction and Extrapolation, the flat curve of species and family richness shows identified and calculated and it is able to reach its detection limit with a horizontal graphic location and position. It is recognized that there are other plant species that have not been detected and are still present in the ecosystem.

Acknowledgment

The authors thanked all participants from farmers, community leaders, youth leaders and sub district officers for their valuable helps and guide.

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