Rev 06/13/22
See Summary of Management Strategies and Tactics
Key Pests - cause significant crop reductions each year. Management systems are built around key pests, and have often developed through trial and error rather than design.
Example - Meloidogyne incognita, Paratrichodorus minor and Pratylenchus in cotton - key pest is M. incognita and the management system is built around it, even if Fusarium is present as the nematode predisposes cotton to the fungus. However, Verticillium may be the key pest as nematode control will not improve yield if the fungus is present.
Limiting Factors - the biotic or abiotic factors constraining use of inputs by the crop; until these constraints are relieved the system will not respond to nematode control.
Example - In experiments conducted by Dr. Richard Baines in Southern California, citrus orchards did not respond to citrus nematode control with DBCP if irrigation, nutrient status, Phytophthora and viruses were limiting factors.
Consider the logistic as a basis for considering nematode management strategies.
a) Consider a simple logistic description of a plant of genetic potential size (Q), growth rate per unit size (a), and of current size (P):
then..... dP/dt=aP(Q-P)/Q
b) Consider a nematode population of current size (N), with growth rate (r), limited by carrying capacity of the root system (K):
then..... dN/dt=rN(K-N)/K
c) The carrying capacity for the nematode at any point in time is approximated by the size of the plant at that time:
K=P*s, where s is the number of nematodes that can be supported per unit of plant size.
d) Besides being constrained by its genetic potential, the growth of the plant is also constrained by the nematode population level in relation to its current carrying capacity; that is, the closer the nematode population to the carrying capacity, the less functional the root system:
dP/dt=aP((Q-P)/Q)(K-N)/K)
e) The following general management strategies are suggested by this model, and are testable:
The following is the BASIC code for the Management Strategy Simulator, accessible through NEMAPLEX, that allows testing of these strategies: |
-NEMAPOP- Rev 11/88 0005 REM A simple program in BASIC to simulate the double logistic. 0010 TPLT=100.0 0020 APLT=1.0 0030 RPLT=0.11 0040 ANPG=10.0 0050 PI=5.0 0060 RNEM=0.15 0070 DT=1.0 0072 N=6 0075 DT=DT*N 0080 CLS 0090 INPUT "ENTER % MORTALITY OF INITIAL POPULATION eg PESTICIDE",X 0100 INPUT "ENTER % REDUCTION ON RATE OF INCREASE eg HZNTL RESIST.",Y 0110 INPUT "ENTER % INCREASE IN CARRYING CAPACITY eg TOLERANCE",Z 0120 PI=PI*(100.-X)/100. 0130 RNEM=RNEM*(100.-Y)/100. 0135 ANPG=ANPG*((100.+Z)/100.) 0136 print " DAY PLANT WT. POPULATION" 0138 L=0 0140 FOR I=1 TO 100 STEP N 0142 NDAY=I 0145 L=L+1 0150 PK=ANPG*APLT -carrying capacity = nem per g * no. of g 0160 FAC=(PK-PI)/PK -logistic factor 0165 PFAC=FAC -use same for plant 0170 IF FAC<0. THEN PFAC=0. 0180 DNEM=DT*PI*RNEM*FAC -change in nematode pop in unit time 0190 PI=PI+DNEM -new population level 0200 IF PI<0. THEN PI=0. 0210 DPLT=DT*APLT*RPLT*((TPLT-APLT)/TPLT)*PFAC 0220 APLT=APLT+DPLT -growth of plant - how close to max and how many nematodes 0230 LOCATE L+4,5:PRINT NDAY;:LOCATE L+4,13:PRINT APLT;:LOCATE L+4,28:PRINT PI 0240 NEXT I 0242 INPUT " AGAIN? (YES=1, NO=0)",K 0244 IF K=1 THEN GOTO 0010 ELSE GOTO 0250 0250 SYSTEM 0260 END |
The distributed-delay simulation model (POPSIM) is generalized for use with any nematode population.
The model opens and reads a soil temperature weather data file and allows a crop to grow during a standard spring and summer growing season.
The nematode population will only grow and reproduce in the presence of the crop. The user is able to define the number of individuals in each life stage on January 1, the developmental time of each life stage, the standard deviation of this developmental time, the fecundity rate of females, and stage-specific survivorship for each stage. The pathogenicity of the nematode, and hence the level of crop damage can also be established.
For example, the simulation can be performed with parameter values reflective of r-selected attributes and K-selected attributes in separate runs.
The probability of success of the organism in an annual cropping system can be determined.
A. Sensitivity of the population dynamics and final densities of the organism to length and variance of life-stage duration, to stage-specific mortality levels, and to fecundity rates can be determined.
Reasonable estimates of life-stage duration for Meloidogyne incognita, Xiphinema index, Paratrichodorus minor, and Mesocriconema xenoplax should be used.
Some parameter values will
need to be estimated. A useful approximation is derived
from Shaffer (Env. Ent., 1984), who noted that the standard deviation for development in 113 species of insects and mites could be described by:
S.D. = 0.209 X -0.73 where X - is the average development time.
B. An interesting exercise is to plot log final population (Pf) against log initial population (Pi), using a range of initial densities for a single nematode species on a host to which it is moderately pathogenic.
Repeat for a host on which it is highly pathogenic.
The maximum multiplication rate and equilibrium density can be determined in each case, as can the effect of host status on these parameters.
C. The potential of management strategies can be determined that:
Nematode | Egg/J1 | J2 | J2 | J4 | Adult | Eggs/Fem/DD |
Mesocriconema xenoplax | 120 | 65 | 80 | 90 | 180 | 0.4 |
s.d. (%) | 25.0 | 15.0 | 16.0 | 20.0 | 60.0 | |
Meloidogyne incognita | 160 | 120 | 300 | 100 | 500 | 0.8 |
s.d. (%) | 33.0 | 30.0 | 55.0 | 22.0 | 110.0 | |
Paratrichodorus minor | 68 | 50 | 45 | 88 | 100 | 0.7 |
s.d. (%) | 11.2 | 7.5 | 6.7 | 26.9 | 70.0 | |
Xiphinema index | 200 | 300 | 500 | 800 | 0.1 | |
s.d. (%) | 41.0 | 62.0 | 104.0 | 167.0 |