A characterization of rice pests and quantification of yield losses in the japonica rice zone of Yunnan, China

Crop Protection 29 (2010) 603–611

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A characterization of rice pests and quantification of yield losses in the japonica rice zone of Yunnan, China Kun Dong a, b, Bin Chen a, Zhengyue Li a, *, Yan Dong a, c, Hailong Wang a, d a

Key Laboratory of Agricultural Biodiversity for Pest Management of Education Ministry of China, The National Center for Agricultural Biodiversity, College of Plant Protection, Yunnan Agricultural University, Kunming, Yunnan 650201, China b Yunnan Provincial Engineering and Research Center for Sustainable Utilization of Honeybee Resources, Eastern Bee Research Institute, College of Food Science and Technology, Yunnan Agricultural University, Kunming, Yunnan 650201, China c College of Resources and Environment, Yunnan Agricultural University, Kunming, Yunnan 650201, China d College of Agriculture, Heilongjiang Agricultural Economy Vocational College, Mudanjiang, Heilongjiang 157041, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 August 2009 Received in revised form 14 January 2010 Accepted 16 January 2010

Within any geographic area where pest management is to be improved or adapted to agricultural changes, there is a need for characterization to define constraints to crop production. The objective of this study was to assess pest injuries (diseases, insects and weeds) in farmers’ fields in the japonica rice zone of Yunnan, China, explore characteristics of rice injury profiles, analyse the relationships between injury profiles and yield levels, and estimate yield losses caused by individual injuries. Seven pest injury profiles (abbreviated as IN) were determined using cluster analysis; IN1, IN2 and IN3 were lower injury levels of pest combinations in seven profiles, while IN5, IN6 and IN7 were higher injury levels. Correspondence analysis based on patterns of injury profiles yielded a path of increasing yield levels associated with varying combinations of injuries. The use of principal component analysis with multiple regression generated estimates of yield reductions due to rice diseases, insects and weeds. The analysis indicated that injuries caused by weeds that are taller than the rice canopy, white heads, leaf folder, bacterial leaf blight, army worms, leaf blast and plant hoppers should be considered as potentially most damaging in this region. Results of this study will provide some foundations for developing pest management strategies and improving rice production at the regional scale. Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved.

Keywords: Multiple pest system Injury Yield loss assessment Correspondence analysis Multiple regression analysis

1. Introduction The rice crop system is an open artificial ecosystem, which is constantly influenced by cyclic interferences of cropping practices (Janzen, 1973; Greenland, 1997). This applies especially to the rice pest, disease and weed components of the system (Teng, 1990a, 1994) with cyclical changes associated with the transplanting of rice, water and fertilizer management action, pesticide use and harvest (Timsina and Connor, 2001). Global agricultural change has brought many improvements to rice production technologies, especially irrigation, introduction of high-yielding varieties, management of fertilizers and pest management (Savary et al., 2000a). Although these technologies have by and large had significant yield-enhancing effects, some of them have brought about increased frequencies of epidemics, as well as the emergence of a number of minor diseases and insect pests becoming new, major, yield-reducing factors (Cheng and Li, 2007).

* Corresponding author. Tel.: þ86 871 622 7309; fax: þ86 871 522 8581. E-mail address: [email protected] (Z. Li).

These changes in the prevalence and intensity of rice diseases and insects have been studied by several authors. New theories and practices have been proposed, including the notion of Ecological Pest Management (EPM) (Tshernyshev, 1995) and of Sustainable Pest Management (SPM) (Lewis et al., 1997; Zhu et al., 2000) following the Integrated Pest Management (IPM) and Integrated Control paradigms (Stern et al., 1957). But no matter what strategy is adopted within any region where pest management is to be improved, there is a need for characterization of multiple pests in order to establish priorities and strategies for management (Savary et al., 1994). Characterization of multiple pests requires that representative field data be available over a given period of time. A common approach is to collect such data using sample surveys, where each rice field should be considered as a unique combination of a number of attributes. Meanwhile, if this information is used to better understand the causes of yield variation, the consideration must shift from individuals (each with unique characteristics) to groups of fields that share common features (Savary et al., 1994). We selected the main japonica-rice-producing areas of the Yunnan plateau as surveyed sites in this study, where we (1)

0261-2194/$ – see front matter Crown Copyright Ó 2010 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.cropro.2010.01.007

604

K. Dong et al. / Crop Protection 29 (2010) 603–611

assessed pest injuries (diseases, insects and weeds) in farmers’ fields in this production system, (2) explored characteristics of rice injury profiles, (3) analysed the relationships between injury profiles and yield levels and (4) estimated yield losses caused by individual injuries. The purpose of this study is to provide an overall framework for the development of pest management strategies and improvement of rice production of this region. 2. Materials and methods 2.1. Survey sites and years The survey was conducted for 2 consecutive years (2006–2007) in the northeastern region of Yunnan province, China. A total of 106 individual fields were surveyed in two counties (Zhanyi and Xundian) of this region during two years. Zhanyi County is located in latitude 25 310 N–26 060 N; longitude 103 290 E104140 E and altitude of 1860 m above sea level; the average annual rainfall was 1002 mm; the average annual sunshine hours 2098 h; the average annual temperature of 14.5  C. Xundian County is located in latitude 25 200 N–26 010 N; longitude 102 410 E103 330 E and altitude of 1882 m above sea level; the average annual rainfall was 1034 mm; the average annual sunshine hours 2079 h; the average annual temperature of 15.2  C. The two counties have a typical lowlatitude plateau monsoon climate with good irrigation infrastructures. All the rice crops are transplanted, with one medium-season rice each year. The crop preceding rice is faba bean. The main cultivars are japonica varieties, including Chujing No.24, Chujing No.26, Dianxi No.10, Hexi No.41 and Hexi No.22. 2.2. Sampling and data collection in each field The survey procedure and the collection of field data were based on the ‘‘Survey Portfolio’’ developed by Savary et al. (1996). The surveyed fields should best represent agricultural production situation of the region. Villages were carefully selected as representative of villages of each county (site) to be surveyed. In each village, 7 to 10 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, fertilizer application).

The data that were collected at the individual field level were summarized in Table 1. These data were collected during four visits in each field during the rice growing season and described quantitative information on crop growth and levels of injuries to the crop due to pest (pathogens, insects and weeds) over time, as well as yield. Each field surveyed in each year was considered to be unique and was represented by a characteristic set of attributes pertaining to injuries and yield. Injury variables were derived from time-dependent information on diseases, insects and weeds observed at four development stages of the growing crop: tillering, booting, early dough and maturity in such a way that they would best account for possible yield reductions (Savary et al., 1994). While injuries due to diseases and insects were specific to species (or species groups), information on weed infestation was the area covered by any weed species, either above or below the crop canopy (Table 1). Except for weeds, information pertaining to injuries was collected in the form of number of injured organs (tillers, leaves and panicles), which later was made relative to the corresponding total number of organs present in the sampling units (10 hills per field for transplanted rice crops). As for weed infestation, the proportion of soil area covered at two levels of the crop canopy (below or above it) was assessed in three spots of 1 m2 each. Although a very large number of pathogens, insects and weeds are harmful to rice, many are seldom considered to cause yield losses (Teng, 1990b; Willocquet et al., 2004). The study therefore concentrated on injuries listed in Table 1. Data compaction over time during crop growth was necessary to achieve the objective of analysis. For this purpose, two types of injury indices were used: areas under injury progress curves or maximum level at any of the four observations, depending on the nature of the injury (Table 1; Savary et al., 1994, 1996). The area under injury progress curve (AUIPC) (Campbell and Madden, 1990) was calculated by the midpoint method using the following equation:

AUIPC ¼

n X

1=2ðXi þ Xi1 ÞðTi  Ti1 Þ

(1)

1

where Xi is percentage (%) of leaves, tillers or panicles injured due to rice pests (e.g., leaf blast, leaf folder), or number of insects (e.g., plant hoppers, leaf hoppers) per quadrat, or percentage (%) of weed infestation (ground coverage) at the ith observation, Ti is time in rice development stage units (dsu) on a 0 to 100 scale (10: seedling,

Table 1 List of variables and theirs attributes of describing individual fields surveyed. Variable type

Symbol

Variable description

Unit

Injuries

BLB LB BS RS ShR NB RB FSM ShB PH LH AW LF DH WH WA WB

Area under the progress curve of mean percentage of leaves with bacterial leaf blight symptoms (4 visitsa) Area under the progress curve of mean percentage of leaves with leaf blast symptoms (4 visits) Area under the progress curve of mean percentage of leaves with brown spot symptoms (4 visits) Area under the progress curve of mean percentage of leaves with rice stripe symptoms Maximum percentage of tillers with sheath rot symptoms Maximum percentage of panicles with neck blast symptoms Maximum percentage of tillers with rice bakanae symptoms Maximum percentage of panicles with false smut symptoms Maximum percentage of tillers with sheath blight symptoms Area under the progress curve of number of plant hoppers caught per hill of sampled quadrat (4 visits) Area under the progress curve of number of leaf hoppers caught per sweep net catches (10 strokes) (4 visits) Area under the progress curve of number of army worms per hill (4 visits) Area under the progress curve of mean percentage of leaves with leaf folder injury (4 visits) Maximum percentage of tillers with dead heart (stem borer) injury Maximum percentage of panicles with white head (stem borer) injury Area under the progress curve of percent weed infestation (ground coverage) above rice crop canopy (4 visits) Area under the progress curve of percent weed infestation (ground coverage) below rice crop canopy (4 visits)

% dsub % dsu % dsu % dsu % % % % % number dsu number dsu number dsu % dsu % % % dsu % dsu

Yield

Y

Estimated yield (grain yield, 14% moisture) from 3 crop cuts 6 m2 each

t ha1

a

Assessments in each field were done at the tillering, booting, early dough and maturity stages. %dsu: It was a combination unit of two units: rice pest incidences (%) and rice development stage units (dsu) on a 0 to 100 scale. It was derived from the area under injury progress curve (AUIPC). b

K. Dong et al. / Crop Protection 29 (2010) 603–611

20: tillering, 30: stem elongation, 40: booting, 50: heading, 60: flowering, 70: milk, 80: dough, 90: ripening, 100: fully mature) at the ith observation and n is total number of observations. 2.3. Data analysis Two analytical approaches were developed, the first aiming at describing characterization of injury profiles and relationship between injury profiles and yield variation and the second aiming at generating yield loss estimates. The first approach was carried out using nonparametric multivariate techniques: cluster analysis and correspondence analysis. It mainly has four steps. Firstly, the quantitative variables were categorized into classes. Table 2 shows the numerical boundaries and classes that were created. The number of categories was made as small as possible for all variables, in order to ensure a class-filling as regular as possible, and the numerical boundaries were chosen so as to have each class represented by commensurate numbers of individual fields (Savary et al., 1995). Regular and sufficient filling of each class determines the expected size of each class in chi-square tests, a prerequisite to their validity (Gibbons, 1976). Except for rice stripe (RS) and neck blast (NB), all other injuries were categorized into three classes, and the boundaries were chosen depending on each of their respective distribution frequencies. Five successive yield classes were defined, in order to enable a better description and analysis of variation of actual yield (Y), from very low (Y1) to very high (Y5) yield levels. As a result, the initial, quantitative variables were each replaced by a few classes. Secondly, cluster analysis using a complete linkage method and a chi-square distance was performed (Savary et al., 1994). The purpose of this second step was to identify groups of fields sharing similar injury characteristics. The categorized data set of 17 injury variables (Table 2) for 106 fields was analyzed to generate clusters of fields by using hierarchical cluster analysis. Each cluster of fields was one group of fields with more similar injury characteristics. Patterns of pest injury profiles were thus defined. These groupings summarized the injury information pertaining to individual field could then be used in turn as a new, synthetic variable: the clusters of injury profile (abbreviated as IN) to which a given field belongs. Thirdly, a contingency table was built: [Y  IN], which showed the bivariate frequency distribution of fields pertaining to patterns of injury profiles and rice yield levels (Y1–Y5), and where rows were the classes (clusters) of injury profiles (IN) and the columns

605

were the classes of yield levels (Y). The table can be used to apply a chi-square test, where the null hypothesis is the independence of the distribution frequencies of the two variables. The purpose of the chi-square test is to test the null hypothesis and confirm the suggested pattern of relationship in the contingency table. Although such table may suggest that patterns can be statistically tested, a technique that allows further insight into the relationship between the two variables and summarizes it was needed. Correspondence analysis (Greenacre, 1984) was used in the fourth step to provide such a summary. It is very convenient for synthesizing information contained in one (as here) or several contingency table(s) and produces graphic representations of the relationships among variables. Correspondence analysis was based on computations on a data matrix of frequencies, i.e., the contingency table built in the previous step. The procedure used in correspondence analysis is similar to principal component analysis and involves the computation of eigenvectors and eigenvalues (Greenacre, 1984). The sum of the eigenvalues is called the inertia and, with correspondence analysis, equals the chi-square statistic divided by the total number of observations. Each class contributes a fraction to the total inertia; summation of inertia over classes yields the total inertia. Coordinates for axes were defined, based on the eigenvalues. Besides the coordinates along the axes, classes are represented by their relative weights, contribution to each axis and reciprocal contribution to axes. The relative weight (or mass) of each class is the frequency of individual fields in the corresponding row (or column). The contribution to an axis is the percentage of inertia of that axis which is derived from a specific class. The reciprocal contribution (or class correlation) represents the proportion of inertia by the specified axis. The sign of the coordinate of each class along each axis indicates the direction that the class takes as it deviates from the origin. This second approach used to quantify yield loss is a set of parametric multivariate methods and derived from that used by Savary et al. (1997). In a first stage, principal component analysis was applied to these injury variables. The factors generated from principal component analyses, being independent linear combinations of the (normalized) initial variables, were then used as descriptors in stepwise, forward, multiple regression of yield variation. The resulting regression models were then used to estimate yields via setting values of the injury variables involved in the factors (Savary et al., 1996, 1997). The yield estimate obtained when all injuries are set to their minimal values in the data set was taken

Table 2 Categorization of variables describing individual fields surveyed. Variable type

Symbola

Categories

Category definition

Injuries

BLB LB BS RS ShR NB RB FSM ShB PH LH AW LF DH WH WA WB

BLB1, BLB2, BLB3 LB1, LB2, LB3 BS1, BS2, BS3 RS1, RS2 ShR1, ShR2, ShR3 NB1, NB2 RB1, RB2, RB3 FSM1, FSM2, FSM3 ShB1, ShB2, ShB3 PH1, PH2, PH3 LH1, LH2, LH3 AW1, AW2, AW3 LF1, LF2, LF3 DH1, DH2, DH3 WH1, WH2, WH3 WA1, WA2, WA3 WB1, WB2, WB3

BLB1: BLB ¼ 0 %dsu; BLB2: 0 < BLB  90 %dsu; BLB3: BLB > 90 %dsu LB1: LB ¼ 0 %dsu; LB2: 0 < LB  80 %dsu; LB3: LB > 80 %dsu BS1: BS ¼ 0 %dsu; BS2: 0 < BS  70 %dsu; BS3: BS > 70 %dsu RS1: RS ¼ 0 %dsu; RS2: RS > 0 %dsu ShR1: 0  ShR  15%; ShR2: 15 < ShR  30%; ShR3: ShR > 30% NB1: NB ¼ 0%; NB2: NB > 0% RB1: RB ¼ 0%; RB2: 0 < RB  3%; RB3: RB > 3% FSM1:0  FSM  3%; FSM2:3 < FSM  5%; FSM3:FSM > 5% ShB1: ShB ¼ 0%; ShB2: 0 < ShB  5%; ShB3: ShB > 5% PH1: 0 < PH  50 number dsu; PH2: 50 < PH  70 number dsu; PH3: PH > 70 number dsu LH1: 0 < LH  120 number dsu; LH2: 120 < LH  360 number dsu; LH3: LH > 360 number dsu AW1: AW ¼ 0 number dsu; AW2: 0 < AW  9 number dsu; AW3: AW > 9 number dsu LF1: 0  LF  10 %dsu; LF2: 10 < LF  25 %dsu; LF3: LF > 25 %dsu DH1: 0  DH  2.5%; DH2: 2.5 < DH  5%; DH3: DH > 5% WH1: 0  WH  2.5%; WH2: 2.5 < WH  5%; WH3: WH > 5% WA1: 0  WA  90 %dsu; WA2: 90 < WA  200 %dsu; WA3: WA > 200 %dsu WB1: 0  WB  500 %dsu; WB2: 500 < WB  1200 %dsu; WB3: WB > 1200 %dsu

Yield

Y

Y1, Y2, Y3, Y4, Y5

Y1: 5.50 < Y  7.75 t ha1; Y2: 7.75 < Y  8.50 t ha1; Y3: 8.50 < Y  9.25 t ha1; Y4: 9.25 < Y  9.75 t ha1; Y5: 9.75 < Y  11.0 t ha1

a

Variables and their units are listed in Table 1.

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K. Dong et al. / Crop Protection 29 (2010) 603–611

as an estimate of the attainable yield, Ya, i.e., the yield the crop would have without encountering reductions from harmful agents (Zadoks and Schein, 1979). Injuries were then considered separately and new yield estimates were made where factors’ values were computed with injury variables set to their mean value or maximum value in the date set. The new yield estimate, Yi, corresponded to the yield the crop would have had if it encountered only the mean or maximum yield-reducing effect of the considered specific injury and did not encounter any reductions from other injuries. An estimate of the mean or maximum yield-reducing effect of single injury was thus obtained from the difference: Ya  Yi. 3. Results 3.1. Characterization of injury profiles Cluster analysis yielded seven groups of injury profiles (abbreviations for such: IN1–IN7) with similar injury characteristics. Fig. 1 indicates the injury profiles corresponding to each cluster, with each injury represented as a percentage based on the highest cluster mean as a reference. The characteristics of the clusters are also summarized quantitatively in Table 3. IN1 is characterized by comparatively high rice stripe (RS), sheath rot (ShR), rice bakanae (RB) and false smut (FSM) injuries; low bacterial leaf blight (BLB), leaf blast (LB), brown spot (BS), leaf hopper (LH), army worms (AW), dead heart (DH) and weed infestation above rice crop canopy (WA); and absence of sheath blight (ShB). This profile is represented by n ¼ 18 fields. IN2 corresponds to high brown spot (BS), sheath rot (ShR), plant hoppers (PH) and weed below rice crop canopy (WB) injuries; low bacterial leaf blight (BLB), leaf blast (LB), neck blast (NB), sheath blight (ShB), leaf folder (LF) and weed infestation above rice crop canopy (WA). This profile accounts for n ¼ 19 fields. IN3 is characterized by high brown spot (BS) and dead heart (DH) injuries; low neck blast (NB), leaf hopper (LH), army worms (AW), leaf folder (LF), white head (WH) and weed below rice crop canopy (WB). This profile is represented by n ¼ 8 fields. IN4 represents fields with high leaf blast (LB) and neck blast (NB); low brown spot (BS) and sheath blight (ShB); and absence of rice stripe (RS) disease injuries. All insect injuries correspond to medium or up levels in this injury profile. It is also characterized by high weed infestation, both above and below the rice crop canopy (WA and WB). This profile accounts for n ¼ 8 fields. IN5 is characterized by high sheath blight (ShB), leaf folder (LF), dead heart (DH), white head (WH) and weed infestation above rice crop canopy (WA); low brown spot (BS) and false smut (FSM); and absence of neck blast (NB). This profile is represented by n ¼ 13 fields. IN6 corresponds to high bacterial leaf blight (BLB), leaf blast (LB), sheath blight (ShB), leaf hopper (LH), army worms (AW) and white head (WH) injuries; low brown spot (BS) and rice bakanae (RB). This profile accounts for n ¼ 21 fields. IN7 accounts for fields with comparatively high bacterial leaf blight (BLB), leaf blast (LB), sheath blight (ShB) and white head (WH); medium plant hoppers (PH), leaf hoppers (LH), army worms (AW), leaf folder (LF), dead heart (DH), weed infestation, both above and below the rice crop canopy (WA and WB). This profile is represented by n ¼ 19 fields. Overall, IN1, IN2 and IN3 correspond to low injury levels of pest combinations in seven profiles, while IN4 corresponds to medium levels, IN5, IN6 and IN7 to high injury levels. IN5, IN6 and IN7 are associated with the lowest mean yields (8.11, 8.03 and 8.23 t ha1, respectively, Table 3), while IN1, IN2 and IN3 are associated with the highest mean yields (9.58, 9.28 and 9.29 t ha1, respectively, Table 3). 3.2. Correspondence analysis The contingency table was built ([Y  IN] yield by injury profiles). The chi-square value associated with the contingency

table is 56.1 (p ¼ 0.0002). The hypothesis of independent distribution of fields amongst the categories of yield levels and injury profiles was therefore rejected. The results of the chi-square analysis suggested that strong links existed between injury profiles and yield levels. In other words, some patterns of injury profiles were more frequently associated with high yield levels, others with low yield levels. The [Y  IN] contingency table was submitted to correspondence analysis, which generated two major axes, the first accounting for 52.9% of total inertia (information) and the second for 26.1% (Table 4). Only the first two axes, which account for a large (79.0%) proportion of total inertia, were retained for further interpretation and provided a two-dimensional representation that is sufficient to interpret a large fraction of the relationships involved in the contingency table. Axis 1 involves large contributions of Y1, Y5 and injury profiles such as IN1, IN5, IN6 and IN7 (clusters of fields with similar injury characteristics). Axis 2 involves large contributions of Y2, Y4 and injury profiles such as IN5 and IN6. Examination of the reciprocal contributions indicate very high values for all yield classes except Y3, indicating an excellent overall representativeness of the analysis with respect to yield variation. The coordinates of the Y-classes along the first axis indicate that axis 1 primarily represents a gradient of increasing yields (Table 4). Fig. 2 shows the projection of the various classes on the plane defined by axes 1 and 2. IN6 and IN7 are closely linked to Y1, while IN4 and IN5 are associated with Y2; and IN2 and IN3 corresponds to Y3 and Y4; and IN1 is associated with Y5. This result indicates that some injury profiles (IN clusters: groups of fields with similar injury characteristics) correspond to low yield levels more frequently, others to high yield levels. A path of increasing yield, broadly from left to right, can be outlined and it progresses from injury profiles IN7 (or IN5, IN6) to IN4, then to IN3 and IN2 and finally to IN1. This would suggest that injury profiles (IN5, IN6 and IN7) located on the left in this graph may cause more considerable damage than others (IN1, IN2 and IN3) located on the right and that IN5, IN6 and IN7 are characterized by either very more injuries or injury with more impact on rice yield. 3.3. Correlations among variables As is predictable, many of the quantitative variables considered in the study are correlated (Table 5); although many of these correlations are significant, they are generally small and do not provide direct information on relationships among variables. Correlations with yield, however, suggest that bacterial leaf blight (BLB), leaf blast (LB), rice bakanae (RB), sheath blight (ShB), plant hopper (PH), army worm (AW), leaf folder (LF), white head (WH) and weed infestation, both above and below the rice crop canopy (WA and WB) have significant yield-reducing effects. 3.4. Preliminary principal component analysis on injuries and regression of injury factors on yield In order to better address relationships among yield and injury variables, a preliminary principal component analysis was performed on the latter variables (normalized data). The resulting factors were then submitted to a step-wise, forward, multiple regression of yield. Four factors (1, 9, 15 and 16) were retained in a multiple regression model accounting for 34.6% of yield variation (n ¼ 106, F ¼ 13.4, p < 0.0001). Among these four injury factors, factor 1 had the largest contribution to description of yield variation (t ¼ 6.44, p < 0.0001) and accounted for 26.9% of yield variation alone. Factor 1 had a negative coefficient and was the following combination of the initial (normalized) injury variables:

K. Dong et al. / Crop Protection 29 (2010) 603–611

607

Fig. 1. Cluster analysis of injuries using a complete linkage method and a chi-square distance in two sites in the japonica rice zone of Yunnan, China. Seven injury profiles (IN) are characterized. The analysis involves 17 categorized variables (Table 2). Characteristics of injury profiles (IN) are outlined on the left (the star-shaped graphs). Cluster dendrogram is indicated on the right. Spokes of the star-shaped graphs represent injury levels made relative to the highest cluster mean level. For example, IN1 is dominated by a few comparatively high injuries: RS, RB, FSM, PH, WH and WB.

608

K. Dong et al. / Crop Protection 29 (2010) 603–611

Table 3 Characteristics of injury profiles of rice crops generated from cluster analyses in the japonica rice zone of Yunnan, China. Variablea

Clusters of injury profilesb IN1(18)

BLB LB BS RS ShR NB RB FSM ShB PH LH AW LF DH WH WA WB Yc a b c

IN2(19)

IN3(8)

IN4(8)

IN5(13)

IN6(21)

IN7(19)

Mean

S.E.

Mean

S.E.

Mean

S.E.

Mean

S.E.

Mean

S.E.

Mean

S.E.

Mean

S.E.

9 10 18 53 30 2.1 8.6 8.0 0.0 71 129 0.1 24 2.9 3.5 115 1062 9.58

3 4 6 9 3 0.7 1.7 1.0 0.0 14 35 0.1 3 0.5 0.6 28 224 0.17

4 4 184 27 36 0.1 4.9 3.5 1.0 91 176 1.6 7 3.6 3.2 81 1472 9.28

4 3 37 8 2 0.1 1.6 0.7 0.4 17 33 1.0 2 0.5 1.6 15 202 0.20

48 48 149 25 25 0.4 5.9 3.7 2.5 75 144 0.6 13 5.3 2.3 165 213 9.29

31 17 63 13 3 0.4 1.8 0.8 1.3 18 40 0.6 4 0.7 0.9 24 50 0.28

76 184 4 0 19 5.3 1.4 3.9 0.4 61 379 4.2 18 3.1 5.0 176 1658 8.78

43 64 3 0 4 4.9 0.4 1.1 0.4 6 140 1.7 4 0.8 0.6 31 305 0.45

69 95 3 19 17 0.0 1.9 1.9 5.8 57 252 7.8 45 7.5 5.2 286 758 8.11

18 23 3 13 4 0.0 0.5 0.7 1.7 7 71 1.5 10 1.9 1.0 68 192 0.21

247 152 11 13 18 2.0 0.6 4.8 4.3 66 851 12.0 32 4.4 5.6 218 1100 8.04

44 47 7 9 2 0.8 0.3 0.7 1.4 12 298 2.1 10 0.9 0.8 36 158 0.31

187 192 3 5 19 1.7 1.3 3.4 4.8 71 461 8.3 24 4.4 5.1 193 888 8.27

75 40 2 4 3 0.7 0.7 0.5 1.1 4 70 1.3 4 0.8 0.6 44 102 0.19

See Table 1 for list of variables. Names of clusters are followed by the total number of fields belonging to the clusters. Yield estimates were not included in the clustering.

Fð1Þ ¼ 0:28 BLB0 þ 0:40 LB0 þ 0:09 BS0 þ 0:01 RS0  0:13 ShR0

injuries and their corresponding pests played an important role in the rice crop system and they should be paid more attention to.

þ0:20 NB0 þ 0:15 RB0 þ 0:04 FSM0 þ 0:13 ShB0 þ 0:27 PH 0 þ 0:02 LH0 þ 0:45 AW 0 þ 0:30 LF 0 þ0:12 DH 0 þ 0:31 WH 0 þ 0:35 WA0 þ 0:23 WB0

(2)

3.5. Selection of injury variables Two criteria were used to select injury variables that would be involved in a final principal component and multiple regression analyses: a significant, negative correlation with yield (Table 5) and/or a high contribution to the factor (factor 1) that contributed most to yield reduction (Savary et al., 1997). Based on two criteria above, the following injuries were therefore selected: bacterial leaf blight (BLB), leaf blast (LB), neck blast (NB), rice bakanae (RB), sheath blight (ShB), plant hoppers (PH), army worms (AW), leaf folder (LF), white head (WH), weed infestation, both above and below the rice crop canopy (WA and WB). In other words, these

3.6. Final principal component analysis on juries and regression of injury factors on yield The five first factors generated by a principal component analysis involving the selected injury variables accounted for accumulated proportion more than 75%. The equation resulting from a step-wise, forward, multiple regression of yield variation using these factors as independent variables was:

Y ¼ 8:73  0:42 Fð1Þ þ 0:03 Fð2Þ þ 0:04 Fð3Þ  0:09 Fð4Þ  0:04 Fð5Þ

(3)

The regression accounted for 61.9% of yield variation (n ¼ 106, F ¼ 18.3, p < 0.0001), with intercept and F(1) significantly (p < 0.0001) contributing to the regression. Examination of

Table 4 Correspondence analysis: relative weights and contribution to axes. Y1–Y5: classes of rice yield levels; IN1–IN7: Clusters of injury profiles of rice pests. Classes

Relative weight

Axis 1 Coordinate

Axis 2 Contribution

Coordinate

To axis

Reciprocal

Columns Y1 Y2 Y3 Y4 Y5

0.208 0.217 0.198 0.198 0.179

0.792 0.265 0.061 0.338 0.798

46.06 5.38 0.26 7.98 40.33

81.87 14.20 4.36 22.00 76.57

Rows IN1 IN2 IN3 IN4 IN5 IN6 IN7

0.170 0.179 0.076 0.076 0.123 0.198 0.179

0.826 0.417 0.395 0.031 0.602 0.448 0.471

40.96 11.00 4.16 0.02 15.73 14.04 14.08

81.07 61.04 21.75 0.24 42.41 38.54 80.73

Inertia accounted for by axes

52.9%

Contribution To axis

Reciprocal

0.232 0.577 0.082 0.489 0.201

8.01 51.84 0.94 34.02 5.18

7.02 67.43 7.88 46.20 4.85

0.174 0.204 0.229 0.582 0.666 0.462 0.054

3.68 5.36 2.84 18.34 39.02 30.38 0.37

3.59 14.66 7.31 87.36 51.82 41.09 1.05

26.1%

K. Dong et al. / Crop Protection 29 (2010) 603–611

0.8

IN

A xi s 2

Y4

IN6

0.4

Y

IN3

Y1 IN7

0.0

Y3

IN2

IN1 Y5

-0.4 IN5

-0.8 -1.0

-0.5

IN4

Y2

0.0 Axis 1

0.5

1.0

Fig. 2. Correspondence analysis between patterns of injury profiles (IN) and yield (Y). Clusters of injury profiles (IN1–IN7) and yield levels (Y1–Y5) are plotted on the two first axes of the analysis pertaining to the [Y  IN] contingency table (actual yield by injury profiles). A path of increasing yield levels (Y1–Y5) is indicated. In this analysis, the factorial plane shown (i.e., two first axes) accounts for 79% of total inertia.

residuals showed that the yield estimated by the statistical model can better fit the actual yield of each field surveyed. 3.7. Yield loss estimates The above regression model (Eq. (3)) was used to produce yield loss estimates for each injury, when their mean or their maximum values were considered (Table 6). The largest individual yield reduction was attributable to white heads (WH: 0.30 t ha1 and 2.99% of the attainable yield), followed by weed infestation above rice crop canopy (WA), leaf folders (LF), army worms (AW), leaf blast (LB), weed infestation below rice crop canopy (WB) and bacterial leaf blight (BLB). Maximum yield losses were large and comparable in magnitude. High maximum yield loss (greater than 10%) corresponded to bacterial leaf blight (BLB), leaf folder (LF), white head (WH) and weed infestation above rice crop canopy (WA). Intermediate maximum yield loss (from 5 to 10%) was caused by leaf blast (LB), plant hoppers (PH) and army worms (AW). Maximum yield losses (less than 5%) corresponded to neck blast (NB), rice bakanae (RB), sheath blight (ShB) and weed infestation below rice crop canopy (WB). When all injuries were considered simultaneously and were set to their mean values, a yield reduction of 1.30 t ha1 was computed, corresponding to 12.95% of attainable yield. 4. Discussion The diversity of geographic climate environment in Yunnan province formed the diverse rice planting ecological environment. With respect to the rice planting compartments, Yunnan may be

609

divided into three kinds of rice cropping zones: the indica rice zone, the japonica-indica interlacing zone and the japonica rice zone (Yang et al., 2007). Xundian and Zhanyi counties are located in the japonica rice zone and are also one of the largest rice-producing areas in the northeastern region of Yunnan. Injury data collected in this survey reflects actual situation of farmers’ fields at the current levels of pest management. Some of the injuries showed a higher prevalence (percent fields affected by a given injury, the data were not shown in this paper), e.g., bacterial leaf blight (BLB), leaf blast (LB), sheath rot (ShR), rice bakanae (RB), false smut (FSM), plant hoppers (PH), leaf hoppers (LH), army worms (AW), leaf folder (LF), dead heart (DH), white head (WH) and weed infestation, both above and below the rice crop canopy (WA and WB), while others are not, e.g., brown spot (BS), rice stripe (RB), neck blast (NB) and sheath blight (ShB). Small or migrating insects were ubiquitous in surveyed region, such as, plant hoppers (PH), leaf hoppers (LH) and leaf folder (LF). The population of local insects (army worms and stem borers) were also high. This is consistent with the trend of rice insect succession in China (Cheng and Li, 2007). Cluster analysis generated seven clusters of injuries (Fig. 1), which represented different combinations of injuries and reflected different intensities of injury across the surveyed region. The linkage of actual yields with clusters of injuries is significant, as indicated by the chi-square test. This linkage, however, is complex, as shown by the correspondence analysis (Fig. 2). Higher yields were more frequently associated with very distinct patterns of injury profiles (IN1, IN2 and IN3): either very few injuries or injury with little impact on production. A clear path of increasing yield levels is essentially defined on the first (horizontal) axis and cuts across a number of patterns of injury profiles, from IN7 (associated with low yields) to IN4 (low to medium yields), then to IN2 and IN3 (medium to high yields) and finally to IN1 (high yields). Cluster and correspondence analyses are dependent on distribution frequencies (Savary et al., 1997). As a result, interpretations are always relative. High intensity of a disease according to its economic injury level (EIL), for instance, refers to the median of the intensity of this disease from the survey sample. While these techniques are convenient to describe patterns of relationships between meta-variables accounting for greater organization levels, such as injury profiles and yield levels, they cannot provide quantitative description of relationships among individual variables (e.g., disease and yield). Parametric multivariate methods were used to produce such descriptions. Assessment of the contribution of each injury or injury combinations to yield loss was conducted using principle component and multiple regression analyses. The regression model that was derived from factors generated by principal component analysis accounts for a large fraction of yield variation. It was thus considered an adequate basis for estimating yield losses due to injuries. It should be stressed here that principal

Table 5 Pearson correlation coefficient matrix among quantitative variables (*, **: significant at p < 0.05 or p < 0.01, respectively).

BLB LB NB RB ShB PH AW LF WH WA WB Y

Y

WB

WA

WH

LF

AW

PH

ShB

RB

NB

LB

BLB

0.38** 0.42** 0.15 0.27** 0.24* 0.28** 0.45** 0.32** 0.41** 0.40** 0.24* 1

0.03 0.30** 0.18 0.17 0.17 0.04 0.20* 0.05 0.24* 0.15 1

0.20* 0.33** 0.08 0.19* 0.12 0.08 0.30** 0.23* 0.26** 1

0.27** 0.26** 0.07 0.18 0.01 0.03 0.14 0.24* 1

0.15 0.14 0.10 0.17 0.21* 0.08 0.13 1

0.29** 0.29** 0.11 0.14 0.27** 0.04 1

0.04 0.04 0.02 0.12 0.08 1

0.14 0.13 0.02 0.27** 1

0.01 0.09 0.10 1

0.13 0.54** 1

0.06 1

1

610

K. Dong et al. / Crop Protection 29 (2010) 603–611

Table 6 Yield loss estimates for selected injuries. Injuries

Mean yield lossesa

Maximum yield losses

Relative (%)

Absolute (t ha1)

Absolute (t ha1)

Relative (%)

Mean

S.E.

Mean

S.E.

BLB LB NB RB ShB PH AW LF WH WA WB

0.12 0.15 0.02 0.07 0.05 0.11 0.18 0.21 0.30 0.28 0.15

0.04 0.04 0.01 0.02 0.02 0.02 0.05 0.05 0.05 0.05 0.02

1.2 1.5 0.2 0.7 0.5 1.1 1.8 2.1 3.0 2.8 1.5

0.4 0.4 0.1 0.2 0.2 0.2 0.5 0.5 0.5 0.5 0.2

1.11 0.78 0.24 0.47 0.25 0.55 0.87 1.36 1.43 1.07 0.36

11.1 7.8 2.4 4.7 2.5 5.5 8.7 13.6 14.3 10.7 3.6

All injuries combined

1.30

0.36

13.0

3.6

–

–

a

Estimates are followed by their confidence interval at p < 0.05.

component analysis excluded yield as a variable. The resulting eigenvectors can then be seen as new, synthetic and independent variables. The latter characteristics allow multiple regression analysis. One feature of the analysis is the very high impact of white heads (caused by stem borers) on yield, while that of dead hearts was not significant (Table 5, Table 6). Although the mean values of two injuries are equal, the difference between the yield-reducing effects of the two injuries is significant, which might be attributed to compensation for dead hearts through rice tillering. Results from this analysis are consistent with earlier results which have demonstrated that no yield loss occurrences when occurrence of dead hearts caused by stem borers is below 10% in irrigated rice (Litsinger et al., 1987; Rubia et al., 1988). Table 6 indicates that injuries caused by weeds above rice canopy, white heads, leaf floder, bacterial leaf blight, army worms, leaf blast and plant hoppers should be considered as potentially most damaging. When all injuries are considered at their survey mean levels, i.e., when a mean, region-wide injury profile is considered, a mean yield–loss estimate of 12.95% is computed (Table 6). The figure is well below the commonly cited estimates (28.5%, 37.2%) by Savary et al. (1997; 2000b). This leads two interpretations: (1) absolute yield losses due to each injury (except for weeds) do not seemingly decrease, but the attainable yield have greatly increased, with the improvement of the levels of rice production within recent ten years and (2) yield losses due to weeds (weed infestation, both above and below the rice crop canopy: WA and WB) is well below the estimates (10%–20%) from reports by Savary et al. (1997, 2000b), because all the rice crops are transplanted in the zone, with herbicide use and handing weeds. Table 6 indicates that injuries interact and that their yield-reducing effects are less than additive effects, a result often found in studies on yield losses caused by multiple pests on various crops. Interactions among injuries, causing less than additive effects on yield reduction seem a common feature of multiple pest systems in many different crops (Padwick, 1956; Johnson et al., 1986; Savary and Zadoks, 1992). 5. Conclusions Analysis of this survey illustrates the strong link between patterns of injury profiles and yield levels and the relative importance of rice pests in the region. Higher yields were associated with very distinct patterns of injury profiles: either very few injuries or

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