การวเคราะหเครอขายการเกดรวมกนของปจจยลดผลผลตขาวในระบบนเวศ

นาชลประทานเขตพนทราบลมภาคกลางของประเทศไทย

Co-occurrence Network Analysis of Yield-limiting Factors of Irrigated Lowland Rice

Ecosystems in Central Plain of Thailand

สทธ ใจสงฆ 1) เทดพงษ มหาวงศ 2) Adam H Sparks 3) Ireneo B Pangga 4)

Sith Jaisong 1) Terdphong Mahawong 2) Adam H Sparks 3) Ireneo B Pangga 4)

Abstract

Occurrences of rice injuries caused by weeds, animal pests and pathogens differ in

various seasons and rice-growing areas. To improve pest management, there is a need to

characterize these injuries at the field scale. Detailed on-farm surveys are useful sources of data

to help understand relationships of rice injuries in farmer’s fields. One hundred and two surveys

of this study were specifically conducted in Central Plain of Thailand during 2013-2015. In this

study, co-occurrence network analysis was used for identifying important rice injuries for pest

management. Networks constructed based on the relationships of rice injuries in different

seasons were examined. The node centrality measures were applied to determine the role of

rice injuries embedded in the networks. The syndromes of rice injuries were detected by means

of network analysis. The syndromes of rice injuries (combinations of coexistence of rice injuries)

were detected based on maximal modularity score, meaning that they may share rice field

conditions. According to rice injury occurrence network of dry season, there were four

syndromes. And false smut, weed below infestation and whorl maggot injuries had high value of

centrality, indicating they were more likely to occur and co-occur with same and other

syndromes in dry season. So, they could be used as good indicators to monitor the injury

Keywords: rice, irrigated lowland rice, yield-limiting factors, co-occurrence network analysis,

Central Plain of Thailand

1) Division of Rice Research and Development, Rice Department., Chatuchak, Bangkok, Thailand 10900. 2) Syngenta 25th Fl., Tower A, Cyber World Tower 90 Ratchadapisek Road, HuaiKhwang, Bangkok 10310. 3) Centre of Crop Health, University of Southern Queensland: Toowoomba, QLD, Australia. 4) College of Agriculture, University of the Philippines Los Baños, Laguna, Philippines.

2

occurrence in dry season. While in wet season, there were four syndromes. Brown spot, whorl

maggot injuries, and weed below infestation could be indicators for monitoring pest and

disease incidence. These findings could provide the understanding, analyzing and managing

multiple rice injuries.

บทคดยอ

รปแบบการเกดรวมกนของอาการของตนขาวทมสาเหตจากวชพช แมลงและสตวศตรขาวและ

เชอจลนทรยกอโรคขาว มความแตกตางกนไปตามพนทและฤดกาล เพอเปนการเพมประสทธภาพการ

จดการศตรขาว จาเปนตองทราบลกษณะเฉพาะของรปแบบการเกดรวมกนของอาการของตนขาวตาง ๆ

เหลานทปรากฏในแปลงนา พนทราบลมภาคกลางของประเทศไทยระหวางป พ.ศ. 2556 - 2558 จานวน

102 แปลง นามาวเคราะหโครงสรางรปแบบการเกดรวมกน (co-occurrence network analysis) ของ

อาการตาง ๆ ทปรากฏบนตนขาวในแปลงนา ในฤดนาปและนาปรง คาศนยกลาง (node centrality) ของ

อาการของตนขาวจากครอขายรปแบบการเกดรวมกนบงบอกถงบทบาทและความสาคญของอาการของ

ตนขาวนน ๆ ในการเกดรวมกน โครงสรางดงกลาวสามารถใชจดกลมอาการ (injury syndrome) ทมกเกด

รวมกนไดโดย สงเกตจากคา maximal modularity ซงอาการของตนขาวทอยในกลมเดยวกน อาจจะม

สภาพแวดลอมทเหมาะสมรวมกน เชน สภาพอากาศ หรอ การเขตกรรมแบบตาง ๆ เปนตน การวเคราะห

เครอขายรปแบบการเกดรวมกนของอาการของตนขาวทพบในฤดนาปรง พบวาสามารถจดกลมอาการได 4

กลม และพบวา โรคดอกกระถน การแขงขนของตนหญาทสงตากวาตนขาว และอาการทเกดจากหนอน

แมลงวนเจาะยอดขาวมคาศนยกลางสง บงชไดวา อาการเหลานมแนวโนมทจะเกดบอยในฤดนาปรงและ

เมออาการเหลานเกด จะมแนวโนมทอาการอน ๆทอยในกลมอาการเดยวกนเกดรวมดวย สามารถใชอาการ

ทงสามนเพอเตอนหรอเฝาระวงการเกดการระบาดของโรค แมลงและสตวศตรขาวอน ๆ ในฤดนาปรง สวน

ฤดนาปเครอขายรปแบบการเกดรวมกนของอาการของตนขาว สามารถจดกลมอาการได 4 กลม และ โรค

ใบจดสนาตาล อาการทเกดจากแมลงวนเจาะยอดขาว และ วชพชทสงตากวาตนขาว มความเปน

ศนยกลางสง

คาสาคญ: ขาว นาชลประทาน ปจจยลดผลผลต การวเคราะหโครงสรางรปแบบการเกดรวมกน

ภาคกลางประเทศไทย

3

Introduction

Agricultural crops are frequently damaged by more than one species of pests and

pathogens at the same time, which consequently, affect yields. To develop effective pest

management, there is a need to shift from single pest control to a holistic one. Studies on

complex plant injuries of rice crops have led to suggestions on how to improve pest

management based on the changes in agricultural ecosystem. The characterization of complex

rice injuries, referred to as “injury profiles”, revealed that similar patterns of injury profiles shared

similar patterns of production situations. (Savary, 2000b). Thus, understanding injury profiles is

integral in developing pest management. This study aims to explore patterns of injury

occurrences based on survey data, which can be important in defining new targets and

strategies for pest management.

Co-occurrence relationships are commonly found in nature. They are important patterns

in ecosystem, which are related to niche processes that lead to coexistence and diversity

maintenance within biological communities (Willams et al., 2014). The co-occurrence patterns in

the communities may reveal groups in the co-occurring species that share similar ecological

conditions or niche. Co-occurrence analysis and network theory have recently been used to

reveal the patterns of co-occurrence and identify key entities in a system (Faust and Raes,

2012; Willams et al., 2014). For instance, in the studies of microbial ecology, these two

approaches were applied to explore the co-occurrence between microorganisms in complex

environments, ranging from the human gut to the ocean and soils (Faust and Sathirapongsasuti,

2012; Ma et al., 2016), while network topology has been proven effective in studying the

characteristics of co-occurrence patterns of geological communities and identifying the

keystone species in microbial networks (Williams et al., 2014; Barberan et al., 2012).

To date, network analysis has not been applied to explore co-occurrence patterns

between rice injuries in farmers’ fields based on crop health survey data, and to untangle the

structure of complex data among the various parameters. With the analysis of network,

correlations of rice injury co-occurrences can be better understood. Moreover, results of the co-

occurrences presented in this study showed the associations between injuries proposed by

network analysis, which would help characterize injury syndromes (the combinations of co-

occurring injuries) in these survey data.

4

Materials and methods

Crop health survey data

Crop health survey data were collected at farmers’ fields over two production seasons,

and three consecutive years (2013 to 2015) in Central Plain of Thailand: Central Plain (14o 23’-

14o 53’N, 100o 1’ - 100o 12’E), Thailand (Fig.1).The total number of surveyed fields were 102

fields (20, 20, 14, 21, 15 and 12 fields in dry season and wet season in 2013 to 2015,

respectively)

Data collection and data preparation

The surveyed fields should best represent agricultural production environment of the

region. Villages were carefully selected as representative of villages in Central Plain, Thailand.

In each village, 10 to 15 fields were then chosen as representative of the diversity of cropping

practices and environments that prevail in each village. All fields selected were farmers’ fields

with local cropping practices (including pest control measures and fertilizer application).

The survey procedure and data were based on a standardized protocol, “A survey

portfolio to characterize yield-reducing factors in rice” (Savary and Castilla, 2009). Thirty-one

rice injuries were collected including the injuries caused by weeds, animal pests and

pathogens, which are harmful to rice plants, and importantly considered to reduce yield

productivity. The injuries were evaluated at booting and ripening stages according to the survey

procedure. They were found on different organs of rice plants, depending on their natures.

Except for weed infestation and systemic injuries, information pertaining to injuries was

collected in the form of number of injured organs (leaves, tillers and panicles), which later was

made relative to the corresponding total number of organs present in the sampling units (12 hills

per field for transplanted rice crops or 12 quadrats (10 x10 cm) for direct seeded rice crop).

Injuries on leaves such as bacterial leaf blight (BLB), bacterial leaf streak (BLS), brown spot

(BS), leaf blast (LB), leaffolder injury (LF), leaf miner injuries (LM), leaf scald (LS), neck blast

(NB), narrow brown spot (NBS), rice hispa (RH), red stripe (RS), rice thrip injury (RTH), and

whorl maggot injury (WM) were determined as a proportion of injured leaves. Injuries on tillers or

hills such as dead heart (DH), dirty panicle (DP), false smut (FS), neck blast (NB), panicle mite

injury (PM), rice bug injury (RB), rat injury (RT), stem rot (SR), silver shoot (SS), sheath blight

(SHB), sheath rot (SHR), and whitehead (WH) were determined as a proportion of injured tillers

or panicles.

5

Systemic injuries such as bugburn (BB), grassy stunt (GS), hopperburn (HB), ragged

stunt (RGS), and tungro (RTG) were determined as the proportion of area affected. As for weed

infestation, the proportions of soil area covered by any weed species at two levels of the rice

canopy (above the canopy (WA) and below the canopy (WB)) were assessed in three areas of

1 m2 each. The rice injury lists and their descriptions are shown in Table 1.

Before analysis, data were summarized over time during crop growth. Two types of data

were computed, which were based on the natures of injuries as defined by Savary and Castilla

(2009). One is an area under injury progress curve (AUIPC) used for injury variables (which

present on the leaves), systemic injuries, and weed infestation. Another is the maximum level at

any of the two observations used for injury variables that can be observed on tillers, panicles,

hills, and area. The area under injury progress curve (AUIPC) (Campbell et al., 1990) was

calculated by the mid-point method using the following equation:

𝐴𝑈𝐼𝑃𝐶 = �(𝑋𝑖 + 𝑋𝑖+1)

2

𝑛−1

𝑖=1

(𝑇𝑖+1 − 𝑇𝑖)

where Xi is %age (%) of leaves, tillers, panicles, or field areas injured due to rice pests (e.g.,

brown spot and leaffolder) at the ith observation, Ti is time in rice development stage units (dsu)

on a 0 to 100 scale (10: seedling, 20: tillering, 30: stem elongation, 40: booting, 50: heading,

60: flowering, 70: milking, 80: dough, 90: ripening, and 100: fully mature) at the ith observation

and n is total number of observations.

Network construction

A statistical approach written in the R environment, version 3.3.0 (R Core Team 2016),

was designed. All scripts used in this analysis are included in the appendix and in a Github

repository (https://github.com/sithjaisong/SJ_dissertation_appendices). The methodology presented

in this chapter was adopted from Williams et al., (2014) for constructing network models of co-

occurrence patterns of rice injuries across cropping seasons (dry and wet seasons) within

Central Plain of Thailand.

As illustrated in Fig. 2, network construction involved three steps. In step 1, an incidence

matrix was obtained using the data set of rice injury occurrences. An incidence matrix lists each

injury in the data set by row (farmers’ fields) with the columns corresponding to the injuries.

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In other words, the incidence matrix showed the co-occurrence of injuries by rice fields. In step

2, an adjacency matrix was computed from the incidence matrix using Spearman’s correlation

method. The square adjacency matrix gave the co-occurrence matrix, which contained

Spearman’ correlation coefficient between a pair of injuries. And finally, in step 3, a network

graph was drawn by connecting injuries that had a non-zero entry in the co-occurrence matrix.

The co-occurrence network was inferred based on adjacency matrix, which was

Spearman correlation matrix constructed with R function ‘cor.test’ with parameter method

‘Spearman’ (package stats) (R Core Team, 2016) used to calculate Spearman’s correlation

coefficient (ρ).

The adjacency matrix A of this network expressed co-occurrence matrix of pair of rice

injury i and j, and was written in A = [Cij], which is

where C is rank correlation coefficient (ρ from the Spearman’s correlation at p-value < 0.05)

between pairs of injures, and

where A is the adjacency (correlation) matrix, in which the rows and columns are injuries. If rows

and columns were ordered first by injury (1...n), and second by grid cells (j + 1...n + j), a

square matrix with i + j rows and i + j columns was produced.

From adjacency matrix, the networks were visualized with ‘igraph’ package (Csardi and

Nepusz, 2006) in the R environment using indirect network and the Fruchterman–Reingold

layout (Fruchterman and Reingold, 1991). Nodes in this network represented rice injuries, while

edges that connected these nodes represented correlations between injuries.

7

Topological feature analysis

Topological features of each network were measured using ‘igraph’ package. To

describe the topology of the resulted networks, a set of measures (node degree, local clustering

coefficient and betweenness) was calculated (Newman, 2006). Node degree is measured by

the number of the edges (connections) a node had. Betweenness of a node is defined by the

number of shortest paths going through a node, while the local clustering coefficients of a node

is the ratio of existing edges connecting a node’s neighbors to each other to the maximum

possible number of such edges. The network clustering coefficient measures the degree to

which nodes of the network tend to cluster together. It is also a measure of the connectedness

of the network and is indicative of the degree of relationships within the network.

Detection and characterization of modular structure in rice injury co-occurrence could

help identify groups of injuries that were closely related and often occurred together. The

networks constructed from survey data detected community structures by maximizing the

modularity measure over all possible partitions by using the ‘cluster optimal’ function of igraph

package. Nodes in the same group were called “syndrome”, which was a combination of

injuries that were closely related, and were most likely to co-occur.

The importance and the role of a node are evaluated by multiple indicators including

node degree, betweenness and local clustering coefficient. The injuries with high node degree

would indicate that the injury has relationships with many other injuries. The betweenness of an

injury in a network reflects the importance of control that the injury exerts over the relationships

of other injuries in the network. The injuries with high clustering coefficient are likely to have a

pronounced effect on injury syndrome because they can rapidly affect other injuries in a

syndrome. The importance of a node is equal to the normalized sum of its three indicators. A

candidate for core or bridge is selected from the great degree nodes or the nodes with great

betweenness respectively. Then, the role of a candidate is determined according to the

difference between its indicator’s relationships with the statistical correlation of the overall

network.

8

Result and discussion

The co-occurrence networks for crop health survey data collected in Central Plain,

Thailand are shown in Fig. 2 and 3. A connection stands for a positive (Spearman’s ρ > 0) and

significant (p < 0.05) correlation. To analyze co-occurrence network of rice injuries, node

properties, namely, node degree, local clustering coefficient and betweenness were highlighted

in the networks. Node degree is a measure of the number of connections a node has as

weighted by Spearman’s correlation coefficient. While the local clustering coefficient is a

measure of the degree to which nodes tend to cluster together. It is defined by the frequency or

the number of triangles formed by a node with its direct neighbors that are proportional to the

number of potential triangles the relevant node can form with its direct neighbors. Betweenness

measures how frequently a node lies on the shortest path between every combination of two

other nodes, indicating the importance of the node in the flow of information through the

network. (Toubiana et al., 2013).

The dry season network (Fig. 3a) was comprised of 20 associated injuries and 54

associations (edges). The network showed four groups of injury syndromes (the combination of

injuries) based on the optimal clustering algorithm. The syndrome, WH, SHR, SHB, DP, BS, RH,

NB, DH, FS, HB and RS had high clustering coefficient, which indicated that these injuries

developed complex co-occurrence relationships. Network properties (Fig. 3b) revealed FS and

WB, WM, BLS, RH, and DH were high-betweenness nodes. Compared to other injuries, BLB

and BLS had low scores on at least two centrality measures. Apparently, they were less likely to

occur as evident with its low betweenness and even to occur with other injuries, as suggested

by its low degree and clustering coefficient. Because of high value of centrality, FS, WB and

WM could be used as indicators to monitor the injury occurrence in dry season.

In the wet season, the co-occurrence network of rice injuries (Fig. 4a) revealed 4

syndromes, 22 injuries and 68 significant relationships (edges). Syndrome of BLS, RS, HB, SHB,

SHR and WM were placed closer to each other than other syndromes based on the structure

and clustering coefficient (Fig. 4b). Based on network structure and betweenness, BS, WM and

WB could be indicators for monitoring pest and disease incidences in this season.

9

Conclusion

To establish priorities and strategies for pest management program, there is a need for

characterization of multiple pests (Mew et al., 2004). Network analysis was used to characterize

co-occurrence patterns of rice injuries from crop health survey data, which were collected from

the farmers’ fields in Central Plain, Thailand for three consecutive years (2013-2015). The

resulting networks, which revealed varying structures, depicted the co-occurrence patterns of

rice injuries in different seasons. From the structures, the networks showed syndromes of rice

injuries (groups of injuries) that are co-occurring injuries in the networks. Moreover, based on

three components of node centrality measures (node degree, clustering coefficient and

betweenness), networks suggest important injuries that in turn could be used for monitoring and

predicting possible trends and occurrence of related injuries under certain conditions. The

networks also revealed the clusters of rice injuries, which were considered as injury syndrome

may share common favorable conditions. The structure of dry season network revealed high

value of centrality of false smut, Weed below infestation and whorl maggot injuries. These

indicated that they have high potential to be associated with other injuries. When these injuries

occurred, other injuries also are likely to occur. So, they could be used as indicators to monitor

the injury occurrence in dry season, whereas in wet season, based on network structure and

betweenness, brown spot, whorl maggot injuries, and weed below. Thus, they could be

indicators for monitoring pest and disease incidences in this season. This information was used

to better understand the variation of rice injury co-occurrence, and to develop more effective

strategies of pest management, specifically those that are based on seasons.

Acknowledgements

Syngenta provided the financial support and collected the on-farm survey data as part

of the SKEP collaboration.

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Ma, B., H. Wang, M. Dsouza, J. Lou, Y. He, Z. Dai, P.C. Brookes, J. Xu and J. A. Gilbert. 2016.

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continental scale in eastern China. The ISME Journal. 10: 1891-1901.

Newman, M. E. J. 2006. Modularity and community structure in networks. Proceedings of the

National Academy of Sciences 103: 8577–8582.

R Core Team 2016. R: A Language and Environment for Statistical Computing [Computer

software manual]. R Foundation for Statistical Computing, Vienna, Austria. Retrieved

from http://www.r-project.org/

Savary, S. and N. Castilla. 2009. A survey portfolio to characterize yield-reducing factors in rice.

IRRI Discussion Paper No 18. 32 p.

Savary, S., L. Willocquet, F.A. Elazegui, P.S Teng, P. Van Du,D. Zhu, Q. Tang, S. Huang, X. Lin,

H. Singh, et al. 2000. Rice pest constraints in tropical asia: characterization of injury

profiles in relation to production situations. Plant Disease 84(3): 341–356.

Williams, R.J.,A. Howe and K.S. Hofmockel. 2014. Demonstrating microbial co-occurrence

pattern analyses within and between ecosystems. Frontiers in microbiology 5: 358.

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Fig. 2 Workflow used for constructing a network that represents the co-occurrence of rice

injuries based on survey data. (A) sub-setting survey by season, and production

environment, (B) constructing correlation matrix using Spearman’s correlation method,

and (C) building network models.

Fig. 1 Map showing the surveyed farmers’ fields in Central Plain, Thailand.

(A) (B) (C)

12

Table 1 List of variables describing individual fields in surveys of rice injuries

Variable Acronym Description Unit1/

Bugburn BB Maximum %age of bugburn in a one-sqm area %

Bacterial leaf blight BLB Area under the progress curve of the mean %age of leaves with bacterial leaf

blight

%dsu

Bacterial leaf streak BLS Area under the progress curve of the mean %age of leaves with bacterial leaf

streak

%dsu

Brown spot BS Area under the progress curve of the mean %age of leaves with brown spot %dsu

Deadheart DH Maximum %age of tillers with deadheart %

Dirty panicle DP Maximum %age of panicles with dirty panicle %

False smut FS Maximum %age of panicles with false smut %

Grassy stunt disease GS Maximum %age of grassy stunt disease in a one-sqm area %

Hopperburn HB Maximum %age of hopperburn in a one-sqm area %

Leaf blast LB Area under the progress curve of the mean %age of leaves with leaf blast %dsu

Leaffolder injury LF Area under the progress curve of the mean %age of leaves with leaffolder injury %dsu

Leaf miner injury LM Area under the progress curve of the mean %age of leaves with leaf miner injury %dsu

Leaf scald LS Area under the progress curve of mean %age of leaves with leaf scald %dsu

Neck blast NB Maximum %age of panicles with neck blast %

Narrow brown spot NBS Area under the progress curve of the mean %age of leaves with narrow brown

spot

%dsu

Panicle mite injury PM Maximum %age of tillers with panicle mite injury %

Rice bug injury RB Maximum %age of panicles with rice bug injury %

Ragged stunt disease RGD Maximum %age of ragged stunt disease in a one-sqm area %

Rice hispa injury RH Area under the progress curve of the mean %age of leaves with rice hispa injury %dsu

Rat injury RT Maximum %age of tillers with rat injury %

Red stripe RS Area under the progress curve of mean %age of leaves with red stripe %dsu

Tungro RTG Maximum %age of tungro in a one-sqm area %

Rice thrip injury RTH Area under the progress curve of the mean %age of leaves with rice thrip injury %dsu

Sheath blight SHB Maximum %age of tillers with sheath blight %

Sheath rot SHR Maximum %age of tillers with sheath rot %

Stem rot SR Maximum %age of tillers with stem rot %

Silver shoot SS Maximum %age of tillers with silvershoot %

Weed above WA Area under the progress curve of the mean %age weed infestation (ground

coverage) above the crop canopy

%dsu

Weed below WB Area under the progress curve of the mean %age weed infestation (ground

coverage) below the crop canopy

%dsu

Whitehead WH Maximum %age of panicles with whitehead %

Whorl maggot injury WM Area under the progress curve of the mean %age of leaves with whorl maggot

injury

%dsu

1/dsu :development stage units on a 0 to 100 scale

13

(a)

(b)

Fig. 3 Co-occurrence network analysis of survey data in dry season at Central Plain. (a) Co-occurrence

network model (b) Three node centrality measures.A :Node degree, B :Clustering coefficient, and

C :Betweenness.

14

(a)

(b)

Fig. 4 Co-occurrence network analysis of survey data in wet season at Central Plain. (a) Co-occurrence

network model (b) Three node centrality measures . A :Node degree, B :Clustering coefficient, and

C :Betweenness.