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Article

Pest Rodents’ Responses to Rice Farming in Northern Peninsular Malaysia

by
Hafidzi Mohd Noor
1,*,
Maisarah Burhanuddin
1,
Hasber Salim
2,
Nur Athirah Asrif
1,
Syari Jamian
1,3 and
Badrul Azhar
4,5,6
1
Department of Plant Protection, Faculty of Agriculture, Universit Putra Malaysia, Serdang 43400, Selangor, Malaysia
2
School of Biological Sciences, Universit Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia
3
Laboratory of Climate-Smart Food Crop Production, Institute of Tropical Agriculture and Food Security (ITAFoS), Universit Putra Malaysia, Serdang 43400, Selangor, Malaysia
4
School of Environmental and Geographical Sciences, University of Nottingham Malaysia, Semenyih 43500, Selangor, Malaysia
5
Biodiversity Unit, Institute of Bioscience, Universit Putra Malaysia, Serdang 43400, Selangor, Malaysia
6
Department of Forest Science and Biodiversity, Faculty of Forestry and Environment, Universit Putra Malaysia, Serdang 43400, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(1), 85; https://doi.org/10.3390/agronomy13010085
Submission received: 25 October 2022 / Revised: 3 December 2022 / Accepted: 5 December 2022 / Published: 27 December 2022
(This article belongs to the Special Issue Rodents in Crop Production Agricultural Systems—2nd Edition)
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Abstract

:
Pest rodents significantly reduce crop yields globally each year, necessitating an efficient rodent management program. In small rice-producing nations like Malaysia, these rodents might lead to food insecurity, thus a science-based pest rodent management strategy is crucial. We attempted to identify the key habitat structure that affects rodent pest populations by investigating the relationships of rodent pest populations with farming practices, site-level habitat, and landscape characteristics. We found that rodent abundance in the rice fields was positively correlated with bund height and width. In addition, rice growing stages and planting seasons affected rodent abundance. However, rodent abundance was negatively related to the distance from active burrows to residential areas. As an alternative to chemical control, we suggest that trapping exercises with a covered trap should be conducted around active burrows located nearby residential areas, with high and wide bunds during early rice planting stages in the dry rice planting season.

1. Introduction

The predominant crop in Asia is rice, which is grown on a far larger area of agricultural land than other grains [1,2]. The importance of rice in the context of Asia can never be over emphasized as it feeds the most populous part of the world (Indian subcontinent, China and the Far East, Indochina, and South East Asia). Rice fortification in Asia is crucial, particularly in the areas with less developed economies, because it is the livelihood for most traditional farmers and an important grain to tackle malnutrition among children in most developing countries [3,4]. Rice has also been traditionally defined a food security, whereby having stable prices for rice indicates a positive marker [5]. Therefore, depredation by pests, notably rodents, is one of the many difficulties that most of Asia’s rice industry is currently facing [6].
In Southeast Asia, the most prevalent and significant rat pest species is the rice field rat, Rattus argentiventer [7,8]. Other commensal rodent pest species that can be found in Malaysia include the black rat (Rattus rattus) and the brown rat (Rattus norvengicus) [9]. The greater bandicoot rat, Bandicota indica, on the other hand, is an introduced rodent pest that is only present in the northern states of Peninsular Malaysia, specifically in the state of Kedah, the state of Perlis, and the island of Penang. A recent observation by [10] in Sabah’s oil palm has revealed new invasion records for B. indica. They were also introduced to the Java Island, Indonesia, and Taiwan [11]. Studies on the DNA suggested the greater bandicoot rat here is not native to the Sundaland region [12] and chromosome studies have concluded that they were identified as Bandicota indica nevorivaga [13]. As a pest, bandicoot rat invasions are feared for several reasons: they are physically large [11,14]; adaptable in various landscapes [15,16]; have high reproductive rates [17]; are disease carriers [18,19]; and are capable of causing devastating damage, whether towards crops, storage products, or infrastructure [20,21].
According to [22], one of the sustainable rodent control techniques involved a coordinated management strategy in line with the fundamentals of a rodent management system with the aim of preserving the ecological features, specifically ecologically based rodent management (EBRM). This manual promotes rodent pest control that is sensible in terms of social, economic, and ecological elements of the area in question. Intriguingly, studies that specifically address the greater bandicoot rats are lacking, despite the recommendation and encouragement to use ecologically based rodent management to control rodent pests [22,23]. Consistent patterns between habitat characteristics and rodent populations in tropical agriculture areas are yet to be determined. Despite the fact that there are numerous rodent ecology studies available, chemicals are used as a method of control [24,25]. However, in order to implement an effective EBRM in agriculture, a thorough understanding of the relationships between rodent species and variables in the agricultural ecosystem settings is essential.
Habitat plays an important role in rodents’ survival. For example, bunds will provide a safe haven and nesting site for rodents [26], while the diversity of small mammals can be influenced by vegetation characteristics [27,28]. Other important factors include crop stage and seasons [28,29]. The occurrence of rodent pests is linked to environmental factors such as planting season and rainfall [30]. However, owing to rodent behavioral plasticity, life history traits, and high breeding potential, the control of rodents is notoriously difficult [31]. It is challenging to design evidence-based management methods for the species without having a thorough understanding of the composition of the small animal species in rice fields and their degree of harm. We thus used the number of trapped rats, existing burrows, and bunds collected in the rice field together with landscape and habitat features as our dataset to investigate the relationships between agricultural habitat and the diversity of rodent communities in the rice field.
The goal of this study was to identify the key habitat structure that affects rodent pest populations in agricultural landscapes, as well as to investigate the relationships between rodent pest populations, farming practices, site-level habitat, and landscape characteristics. Owing to high occurrences of rodent pest populations in the Kedah rice field recently [32] and their previous records of being notorious pests in agricultural lands focusing on cereal production [33,34], we hypothesized that the occurrence of the rodent pests increases with the rice field areas and human alterations such as railways, roads, and residential areas.

2. Materials and Method

2.1. Study Area

We conducted the study between Ketol Village and Raja Village, Jitra (6°18′56.6″ N 100°21′07.6″ E), located within Kubang Pasu District in Kedah, about 35 km south of Perlis state and 50 km south of the Malaysia–Thailand border (Figure 1). The western and southern part of the area is dominated by a rice field area and human settlements. There is a reserve forest, namely the Bukit Wang Forest Reserve, located north-east from the study site. There are two planting seasons per year: dry months (March–September) and monsoonal season (October–February). In the drier months, there are usually limited water sources for agricultural purposes, thus rice planting activities are delayed early in this period, or are carried out using the direct seeding method.

2.2. Rodent Sampling

Sixty rodent live traps (width × length × height: 25 cm × 18 cm × 10 cm) were deployed in the rice field throughout five rice growing stages (Table 1); (i) land preparation, (ii) vegetative, (iii) tillering, (iv) booting, and (v) harvesting, over the dry and wet rice planting season in 2020. We conducted rodent trapping for 2400 nights (60 traps × 4 nights × 5 growing stages × 2 planting seasons). We covered the traps with dried grass to prevent non-target animals like birds from being captured and to provide theft camouflage [35]. The traps were installed alongside bunds or between the intersection of the bunds in the rice field with a minimum 5 m distance from each other. Ten traps were installed over three locations in Ketol Village and three locations in Raja village. These were distributed so that each trapping location contained traps both with and without burrow entrances, and fairly distributed for an even spread.
During the dry season, shrimp paste and dried fish were used as bait, while river crab was employed during the rainy season. Different baits were employed during the wet season owing to the baits’ lack of weather resistance. The shrimp paste and dried fish were found to fall off easily when the rain came, thus reducing the efficiency of the trap, whereas the fresh crab and prawn remain on the bait hook because of their hard exoskeleton and provide a nearly similar odor. On the contrary, the usage of fresh baits would attract more insects and ants if applied during the dry season when compared with the other sets of baits.
We inspected and reset the traps in the same location at 0800 H on the next day. Captured rodents were euthanized, identified to species level, sexed, weighed, and measured. Rodent handling was carried in accordance with the guidelines of the American Society of Mammologists for the use of wild animals in research and education [36].

2.3. Assessment of Stand-Level Habitat Quality Characteristics

We measured six characteristics of stand-level habitat quality at each trapping location in the rice field. The characteristics of the habitat were as follows: (i) bund height (cm), (ii) bund width (cm), (iii) percentage of ground vegetation cover, (iv) height of ground vegetation cover (cm), (v) rice growing stages, and (vi) rice planting season. The parameters were collected across five different rice planting stages for both wet and dry planting seasons of the year 2020. The five rice planting stages were (i) land preparation, (ii) vegetative, (iii) tillering, (iv) booting, and (vi) harvesting (Table 2).

2.4. Measurement of Landscape Metrics

To assess the landscape-level effects of habitat structure on rodent populations, we measured the following habitat characteristics at each point sampled for rodents (Table 3): (i) distance to the nearest rice field, (ii) distance to the nearest residential area, (iii) distance to the nearest shrub, (iv) distance to the nearest water source, and (v) rice planting season. We used circular measurements in Google Earth Pro to estimate the distance between a sampling point and the nearest forest edge.

2.5. Statistical Analysis

We used generalized linear mixed models (GLMMs) to determine the relationships between captured rodents and explanatory variables. For our analysis, we used relative abundance of rodents (i.e., number of captured rodents) as a response variable (Table 3). We examined nine explanatory variables. We assigned a separate model for stand-level and landscape-level variables. We used Poisson distribution and a log-link function to analyse the data. To avoid distortion in model estimation, we performed Spearman’s rank correlation coefficient tests to assess multi-collinearity among predictor variables within each model. None of the explanatory variables were strongly correlated variables (|r| > 0.7) [37]. Dredging method was used to fit the model.
We evaluated support for competing models’ investigations of the relationship between stand-level, landscape parameters, and all explanatory variables of interest. We used a likelihood-based method to quantify the alternative models and estimate their parameters. The Mallows’ Cp statistic was used in multiple regression analysis to select parsimonious models. The Mallows’ Cp is likely to select similar models to the Akaike information criterion (AIC). We reported the adjusted coefficient of regression, r2, for the models. GenStat 12th Edition (VSN International, Hemel Hempstead, UK) was used to complete our analysis [38,39].

3. Results

3.1. Outcomes from the Small Mammal Trapping Effort

We captured 179 rodents over 2400 trap-nights with a trapping success of 7.5% (number of rodents per trap night). Four species of the order rodentia, that is, the greater bandicoot rat (Bandicota indica), the rice field rat (Rattus argentiventer), and the black rat (Rattus rattus), were trapped in the rice field together with a shrew species, the Asian house shrew (Suncus murinus). All of the species were identified as least concern by IUCN and pest species in Malaysia [9]. B. indica species abundance was found to exceed other rodent species in the rice field in early dry planting season (11.67) and early wet planting season (9.58). B. indica and S. murinus were successfully captured throughout both rice planting seasons, while R. rattus and R. argentiventer were only discovered scarcely throughout the year (Figure 2).

3.2. Influence of Environmental Variables on Rodent Populations at Stand- and Landscape-Level

At stand-level, the most parsimonious model explaining relative abundance of rodents included four explanatory variables (Mallows’ Cp = 526.30) (Table 4). The dredged method models consist of explanatory variables, which are bund height, bund width, rice growing stages, and rice planting season, and explained about 16.54% of the rodent abundance in the sampling area (Table 4). Rodent abundance increased with the increasing bund height (slope = 0.001195) and bund width (slope = 0.1233) (Table 5). In terms of rice growing stages, the relative abundance of rodents during land preparation (slope = 0.6189), vegetative stage (slope = 0.3797), and tillering (slope = 0.3377) was higher than those in booting (slope = 0.0000). Only harvesting (slope = 0.4231) had a lower rodent abundance than booting. In terms of the rice planting season, rodent abundance in the wet planting season (slope = −0.4980) was lower than in the dry season (slope = 0.0000) (Table 5).
At landscape-level, the most parsimonious model explaining rodent relative abundance with landscape parameters included two explanatory variables (Mallows’ Cp = 706.87) (Table 6). The dredged method models consist of explanatory landscape model variables, which are distance to residential areas and rice planting season, and explained about 4.10% of the rodent abundance variation in the sampling area (Table 6). Rodent abundance decreased with the increasing distance to residential areas (slope = −0.0003606). Rodent abundance during the wet rice planting season (slope = 0.06593) was higher than during the dry rice planting season (slope = 0.0000) (Table 5).
The wet season was included in both the stand-level (slope = −0.498) and landscape-level model (slope = 0.06593) (Table 5). Further to the analysis, we compare the value using Wald statistical analysis for season as fixed effects for more precise data comparison. The season variable in the stand-level model has a higher Wald value (WSL = 14.22) than in the landscape model (WLM = 0.87), thus the season variable analyzed in the stand-level model gives a more reliable result. The data support a theory where rodent abundance in the wet season was lower than during the dry season (slope = −0.498).

4. Discussion

The main finding to emerge from the analysis is that multiple rodent species were trapped from the Jitra rice fields, although they seem to be dominated by the greater bandicoot rats, with much lower numbers of the other species. One of the difficulties in rodent management is dealing with multiple rodent species coexisting in one place, as small rodent populations have unpredictable dynamics [40]. Unexpected epidemics could result from this, affecting the health, conservation, and economic sectors. With multiple rodent species in an area, their competition for resources can be perilous, especially towards small-scale rice farmers. In addition, different rodent species may have unique traits, ecological requirements, and ecosystem roles that may affect how effective pest management is employed. In Cambodia’s rice field, at least three different species, namely, the fawn-colored mouse (Mus cervicolor), the tanezumi rat (Rattus tanezumi), and the red spiny rat (Mus surifer), were dominant and usually abundant during the wet season [41], while in Assam, India, a study conducted by Phukon and Borah [20] reported four rodent species associated with rice fields in Assam, out of which Bandicota bengalensis was the most predominant species with a relative abundance of 59.76%, followed by B. indica with 19.08%. Despite that the occurrence of multiple rodent species is common, the situation may cause high crop loss, especially in vast agricultural areas.
This study discovered that several stand-level variables such as bund height, bund width, rice growing stages, and the rice planting season had an impact on rodent abundance in rice fields (Table 5), while only two landscape-level variables—distance from sampling point to residential area and rice planting season—were highlighted as the most parsimonious model (Table 6). As the measurements of bund heights and width increase, so does the abundance of rodents in the rice field. A larger size of the bunds would allow the rodents to build deeper and more complicated tunnels and encourage their nesting sites. Allowing the development of large bunds would encourage rodents to nest and spread. The bandicoot rat colonies in India were discovered to be able to build a burrow system that spreads up to 300 m2 with multiple chambers and openings [20]. The complex burrows are able to store up to 3 kg of hoarded food, which usually consists of nearby agriculture harvests such as rice, wheat, and sugarcane, which later would impose a threat to farmers’ harvesting yield [42].
Rodent abundance was also found to have a dynamic correlation with rice growing stages [43]. Rodents were found to be abundant in the early planting stages and reduced as the harvesting stage started. The reproduction of the rice field rat (R. argentiventer) was found to coincide with the generative stage of the rice crop [44]. Similarly, in West Java, breeding occurred during early rice plant production; the booting stage [40]. The greater bandicoot rats, however, may have a slightly different breeding biology and social organizations [6]. We agree with [6] and concluded that their main breeding season would coincide with the monsoonal season. According to [17], it takes almost one month for a new-born pup to reach maturity. The mean age at sexual maturity of male greater bandicoots was 59.9 ± 2.3 days and that for females was 63.4 ± 1.8 days.
Rodent activity depends on weather conditions [2,45,46]; however, in our study, observation of trapped rodents over rice planting seasons indicated that their abundance is negatively correlated with the wet season. Thitipramote [6] discovers that, even though the greater bandicoot rats have the capability to breed all year long, their breeding seasons peak during the wet planting seasons in Thailand. Although the abundance of most rodent species was higher during the wet season [28], our results are consistent with those of [47], where rodent abundance in Jitra, Kedah is higher during the dry season owing to a drier habitat and forces rodents to wander off in search for food, making them prone to being trapped. We also theorized, based on low females captured in this study, that, during the monsoonal seasons, most females would spend their time underground giving birth and taking care of their young, which explained the negative correlation of rodent abundance with the wet season in the stand-level variable model (slope = −0.498) (Table 6). The absence of food source in the early rice planting season would allow the rodents to be more responsive to bait offered in the rodent traps installed nearby their burrows, when compared with the harvesting stage, where the food supply is sufficient.
The occurrences of R. rattus and S. murinus in the rice fields may be due to aggressive human settlement expansion into the agriculture lands. Rattus rattus and S. murinus are known to live alongside human settlements [2]. They are highly adapted to coexisting with human populations and are particularly ubiquitous in the urban environment. Their occurrences in the rice field can be a threat to crops and storage facilities [19] as well as become a health hazard towards human [48]. So far, not much is known about the effects of land conversion and small mammal communities in rice fields in Malaysia and how they affect crop yields, storage, health, and food security—this is an area for future work. As a solution, deep ploughing of fields immediately following harvest would destroy the burrow system, followed by reducing surrounding vegetation density [27], exposing the rodents above ground and increasing their chances of predation by available predators [12].
Undergrowth vegetation has no apparent association with the rodent abundance (Table 5). In other studies, Wells [49] discovers that vegetation density does not play role in nesting patterns of rodents in Borneo; instead, the nesting behaviour largely reflects the species’ space use. For example, the Whitehead’s rat, Maxomys whiteheadi, and the red spiny rat, Maxomys surifer, dominated the terrestrial community and nests below the ground, whereas the large pencil-tailed tree mouse, Chiropodomys major, was found to utilize higher habitat layers, mostly nests in the canopy. In our study, B. indica and R. argentiventer foraged on the ground, but nested mostly below ground, regardless of the condition of vegetation coverage and height in the surroundings. GLMM analysis indicated that the vegetation coverage and height factors do not contribute much towards rodent abundance in the rice field.
In this study, rodent abundance in the rice field increased in the vicinity of residential areas (Table 6) and rodent burrows were discovered to be built under permanent infrastructure such as roadsides, railways, riverbanks, and other man-made structures that may provide refuge to more rodent pests. Therefore, rice fields nearby these areas require constant monitoring, and our analyses suggest that trapping exercises with covered traps should be conducted around active burrows located nearby residential areas, with high and wide bunds during the early rice planting stage in the dry rice planting season.

4.1. Management Implications

This finding has important implications for helping rice farmers to understand and make informed decisions regarding rodent pest management. GLMM models indicated that bund height and width play a crucial role in rodent abundance (Table 5), especially for the subterranean species; that is, the rice field rat and the greater bandicoot rats in the rice field. High and wide bunds (such as in main roads and railroads) may allow rodent harborage and increase rodent pest in abundance; in contrast, usage of too low bund sizes may allow overflow of the water in the rice field, thus exposing the rice crops to weeds and other invertebrate pests. It also will hinder any farmer’s activity to manage their lands. Although rice bunds are recommended to be built less than 50 cm in width [50] and should be within 50 cm (wide) × 30 cm (height) [51], there is still potential for rodents to nest. Future studies may explore improvements in developing more compacted bunds and the usage of better bund materials in rice fields, which not only provide a stable structure for human assessments and improved water management in rice fields, but at the same time limit the accessibility for any rodent pests for nesting.
Each year, farmers must decide whether or not to allocate funds for rodent management. While predicting rodent outbreaks requires complex calculation of various variables [52], and eruptive rodent population dynamics can be triggered by interannual variation in environmental factors [53], the decision to invest in rodent control can be a huge gamble on the crop yield and profit. As many minor or sporadic pests today probably are kept in check by the action of natural enemies [54], maintaining natural enemies in the rice field can be an asset, as it is not only ecologically based but also environmentally friendly. An established barn owl program in an agroecosystem can provide a constant presence of barn owls (Tyto alba) to suppress rodent pest population under a tolerable level in the rice field agro-system [55,56]. Future studies should also explore other potential candidates of rodent natural predators such as black shouldered kite, Elanus caeruleus [57,58], and how to manipulate the habitat landscape in their favor [59].

4.2. Limitations of the Present Study

Owing to limited data, this paper could not provide a comprehensive correlation of landscape- and stand-level variables with specific rodent species or a sex-based rodent study, which may require a longer period of observation for such data. We are also unable to provide information on food habits and the type and amount of damage occurring to crops. We also have not discovered a consistent bait to be used in both planting seasons as the changes in rodent abundances could be significantly affected by the change in bait, especially for the different species of rodents. Although bandicoot rats were found to dominate the chart, it is still unclear whether the bandicoot rats are overtaking the rice field rat as the dominant species, as protein-based bait in this study favors the bandicoot rat species. For a better species composition study, we would suggest a bait favored by all species or the use of supporting observations using track plate or camera traps. It is also beyond the scope of this study to examine the relationship of natural predators with rodent abundance in the study area, albeit several natural predators were sighted at least once throughout the study, including barn owls (T. alba), black-shouldered kites (E. caeruleus), brahminy kite (Haliastur Indus), mongoose (Hepestes sp.), Asian water monitor (Varanus salvator), brown rat snake (Ptyas fusca), and equatorial spitting cobra (Naja sumatrana). Exploring other potential biological control agents and developing program to conserve them will allow multiple existence of natural predators in the areas and assist farmers in lowering rodent pest attack incidences.

5. Conclusions

As various kinds of rodents have been seen living alongside rice fields in Kedah, they could seriously harm harvested crops and necessitate ongoing observation. Previous crop losses amply demonstrate the need for improved food security and efficient rodent control, both of which are necessary to safeguard food storage and people from infections spread by rodents. In order to control the rodent populations, rice farmers should pay attention to factors associated with rodent abundance in the rice field such as bund height and width, rice growing stages, rice planting seasons, and the distance of the active burrow to residential areas. We recommended to local rice farmers to conduct trapping at the beginning of the dry planting season as it coincides with the emergence of juvenile generations bred during previous wet seasons. Our understandings of the interplay of small mammals with their environment using modelling should be improved in the future research on larger dataset over a longer period of time, which may explicate the small mammal populations’ abundance subject to different local conditions to support the implementation of ecologically based rodent management (EBRM) in a sustainable integrated pest management (IPM) in the rice field.

Author Contributions

M.B.: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Validation (equal); Project administration, (equal); Resources (equal); Visualization (equal); Writing—original draft (equal); H.M.N.: Conceptualization (equal); Data curation (equal); Formal analysis (equal); Investigation (equal); Methodology (equal); Project administration (equal); Resources (equal); Software (equal); Supervision (equal); Visualization (equal); Writing—review and editing (equal). H.S.: Investigation (equal); Methodology (equal); Validation (equal); Writing—review and editing (equal). N.A.A.: Investigation (equal); Methodology (equal); Validation (equal); Writing—review and editing (equal). S.J.: Investigation (equal); Methodology (equal); Validation (equal); Writing—review and editing (equal). B.A.: Conceptualization (equal); Data curation (equal); Formal analysis, (equal); Investigation (equal); Methodology (equal); Project administration, (equal); Software (equal); Supervision (equal); Visualization, (equal); Writing—original draft (equal); Writing—review and editing (equal). All authors have read and agreed to the published version of the manuscript.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Acknowledgments

This research has been conducted with permission by Department of Wildlife and National Parks Peninsular Malaysia (PERHILITAN). We thank the local rice farmers for their cooperation in the field works. We are also grateful to Inne Ziah Firlana, Nabilah Ahmad, Hasna Faizul, and Arif Ramly for assisting us in the field. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Agronomy 13 00085 g001 550
Figure 1. Map of Northern Peninsular Malaysia. The trapping sites used in this study area are marked with bold dots and labelled with the village names.
Figure 1. Map of Northern Peninsular Malaysia. The trapping sites used in this study area are marked with bold dots and labelled with the village names.
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Agronomy 13 00085 g002 550
Figure 2. Rodent species and abundance throughout the dry (May–September) and wet rice planting season (October–February) in Jitra, Kedah. Protein-based bait was used in trapping.
Figure 2. Rodent species and abundance throughout the dry (May–September) and wet rice planting season (October–February) in Jitra, Kedah. Protein-based bait was used in trapping.
Agronomy 13 00085 g002
Table
Table 1. Growth cycle of a rice plant according to the International Rice Research Institute IRRI), Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale and sample structure.
Table 1. Growth cycle of a rice plant according to the International Rice Research Institute IRRI), Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) scale and sample structure.
VegetativeReproductiveMaturative
IRRI-12345
BBCH (days)-1–1921–4143–5354–7783–99
Our studyLand preparationVegetativeTilleringBootingHarvesting
Table
Table 2. Summary statistics for environmental variables.
Table 2. Summary statistics for environmental variables.
Environmental VariablesMean ± SDMedianMin–Max
Stand-level habitat characteristics
Burrow count1.661 ± 2.17010–17
Bund height (cm)21.28 ± 8.44719.517.18–66.42
Bund width (m)1.47 ± 1.1618.0.8530.35–8.8
Undergrowth coverage (%)1.176 ± 2.240.5080.01–15.32
Undergrowth height (cm)23.81 ± 9.76626.441.30–46.85
Landscape metrics
Distance to shrubs (m)29.15 ± 71.666.860.8–493
Distance to residential area (m)351.2 ± 253358.90–882.7
Distance to rice field (m)18.34 ± 81.341.750.1–914
Distance to water source (m)92.64 ± 120.7026.610.0–616.2
Table
Table 3. Summary statistics for rodent variables.
Table 3. Summary statistics for rodent variables.
VariablesMean ± SDMedianMin–Max
Relative abundance1.92 ± 2.26910–18
Rodent
Greater bandicoot rat (B. indica)0.181 ± 0.45700–5
Rice field rat (R. argentiventer)0.0157 ± 0.12400–1
Black rat (Rattus rattus)0.0143 ± 0.11900–1
Insectivores
Asian House Shrew (Suncus murinus)0.0443 ± 0.20600–1
Total capture0.256 ± 0.54400–5
Table
Table 4. Best subsets from candidate models. The most parsimonious model (labeled with * and bolded) has six explanatory stand-level variables with the lowest Mallow Cp and highest adjusted R2 influence to relative rodent abundance.
Table 4. Best subsets from candidate models. The most parsimonious model (labeled with * and bolded) has six explanatory stand-level variables with the lowest Mallow Cp and highest adjusted R2 influence to relative rodent abundance.
ModelExplanatory VariableDFMallows CPAdjusted R2
1Bund width2580.5212.73
2Bund width + Rice growing stage6541.7915.44
3Bund width + Rice growing stage + Rice planting season7526.5816.51
4 *Bund height + Bund width + Rice growing stage + Rice planting season8526.3016.54
5Bund height + Bund width + Undergrowth height + Rice growing stage + Rice planting season9526.5616.54
6Bund height + Bund width + Undergrowth coverage + Undergrowth height + Rice growing stage + Month10528.2716.44
Table
Table 5. Slopes and back-transformed means for explanatory stand- and landscape-level variables determining variables’ influence to relative rodent abundance.
Table 5. Slopes and back-transformed means for explanatory stand- and landscape-level variables determining variables’ influence to relative rodent abundance.
Explanatory VariableSlopeBack Transformed Means (Relative Abundance)
Stand-level variables
Bund Height0.0011954.646
Bund Width0.12333.187
Rice growing stages
Land preparation0.61892.719
Vegetation stage0.37972.141
Tillering0.33772.053
Booting0.0001.464
Harvesting−0.42310.959
Dry0.00002.255
Wet−0.4981.37
Distance to residential area−0.0003606
Dry0.00001.41
Wet0.065931.506
Table
Table 6. Best subsets from candidate models using the dredging method. The most parsimonious model (labeled with * and bolded) has two landscape-level explanatory variables with the lowest Mallow Cp and highest adjusted R2 influence to relative rodent abundance.
Table 6. Best subsets from candidate models using the dredging method. The most parsimonious model (labeled with * and bolded) has two landscape-level explanatory variables with the lowest Mallow Cp and highest adjusted R2 influence to relative rodent abundance.
ModelExplanatory VariableDFMallows CPAdjusted R2
1Distance to residential area2706.944.09
2 *Distance to residential area + Rice planting season3706.874.10
3Distance to residential area + Distance to water source + Rice planting season4708.663.97
4Distance to shrub + Distance to residential area + Distance to water source + Rice planting season5710.423.85
5Distance to shrub + Distance to rice field + Distance to residential area + Distance to water source + Rice planting season6712.353.72
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Noor, H.M.; Burhanuddin, M.; Salim, H.; Asrif, N.A.; Jamian, S.; Azhar, B. Pest Rodents’ Responses to Rice Farming in Northern Peninsular Malaysia. Agronomy 2023, 13, 85. https://doi.org/10.3390/agronomy13010085

AMA Style

Noor HM, Burhanuddin M, Salim H, Asrif NA, Jamian S, Azhar B. Pest Rodents’ Responses to Rice Farming in Northern Peninsular Malaysia. Agronomy. 2023; 13(1):85. https://doi.org/10.3390/agronomy13010085

Chicago/Turabian Style

Noor, Hafidzi Mohd, Maisarah Burhanuddin, Hasber Salim, Nur Athirah Asrif, Syari Jamian, and Badrul Azhar. 2023. "Pest Rodents’ Responses to Rice Farming in Northern Peninsular Malaysia" Agronomy 13, no. 1: 85. https://doi.org/10.3390/agronomy13010085

APA Style

Noor, H. M., Burhanuddin, M., Salim, H., Asrif, N. A., Jamian, S., & Azhar, B. (2023). Pest Rodents’ Responses to Rice Farming in Northern Peninsular Malaysia. Agronomy, 13(1), 85. https://doi.org/10.3390/agronomy13010085

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