# Effects of Weather on Sugarcane Aphid Infestation and Movement in Oklahoma

^{1}

^{2}

^{3}

^{4}

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## Abstract

**:**

## 1. Introduction

## 2. Methods and Data

#### 2.1. Structural Model of SCA Movement

#### 2.1.1. Effect of Temperature on SCA Survivability

#### 2.1.2. Effect of Rainfall on SCA Survivability

#### 2.1.3. Effect of Wind on SCA Movement

_{PDIR}, with a corresponding angular range of ±11.25° between each of the 16 possible wind directions. The probability function assumes that flight varies from the primary direction by ±1 wind-directional unit, e.g., a primary direction of 7 is assumed to vary between 6 and 8 by ±11.25°. This parametrization places lower limits on the probability distribution, which declines linearly from a probability of 1 along P

_{PDIR}to zero at the boundary between P

_{PDIR}and its nearest direction. The probability distribution is shown in Figure 4 and is given by the following formula:

_{i}is the angular difference in degrees between ${P}_{DIR}$ and angles, and σ is a parameter (σ > 0) that establishes the boundary where probability equals zero. For this study, a value of σ = 1 was chosen so that the zero-probability boundary occurs at ${P}_{DIR}$± 1 wind-direction value, whereas other values such as σ = 2 or σ = 3 shift the boundary inward (Figure 4).

#### 2.1.4. Joint Probability of SCA Movement

#### 2.2. Explaining Spatial Patterns of SCA Movements over Time

_{t}are year dummy variables, a is the intercept, and ${\epsilon}_{rt}$ is an independently and identically distributed error term for sorghum field $r$ on day $t$ with mean zero and variance ${\mathsf{\sigma}}^{2}$. The ${X}_{i,j}$,${\mathrm{Y}}_{\mathrm{i},\mathrm{j}}$ coordinates were obtained from the georeferenced latitude–longitude of each field’s centroid using ARC-MAP software. The model was estimated in Stata software using the fracreg statement. The estimated regression equation is essentially a probability distribution of SCA movement across two-dimensional space.

#### 2.3. Data

_{DIR}= 8, indicating a due north wind direction. As a result, only fields contained within a 22.5° spoke emanating from the infested field in Kiowa County, oriented in a due north direction, had non-zero probabilities for this simulation day (Figure 6). All fields outside the spoke had zero probability of SCA migration and are not drawn. Based on Equation (4), fields due north of the infested field had the highest probability based on the prevailing wind direction (P

_{DIR}= 8). Moving from the middle of the cone to its boundary decreases the probability of SCA landing on fields in that region. The sample field is oriented −0.19° W of the source field, resulting in a directional probability of P

_{PDIR}= 0.983. The effect of wind speed is also evident as the highest probabilities are near the midway point between the infested field and fields furthest to the north. For the sample field, located 91.1 miles from the source field, on a day with a wind speed of 17.9 mph, resulted in a 5.08 h flight. This is extremely close to the ideal 5 h flight, the highest-probability flight time in Equation (3), generating a wind-speed probability of P

_{WSD}= 0.9775. SCA colonies successfully landing on a destination field were then updated according to daily rainfall and temperature. The sample field with 0.1766 in of rain and a temperature of 30.1 °C resulted in losses of P

_{RAIN}= 0.588 and P

_{TEMP}= 0.649 using Equations (1) and (2). Using Equation (7), the overall probability of an SCA colony on the red field was P = 0.37.

## 3. Results

#### 3.1. Effect of Weather Variables on Predicted Cumulative Probability

^{2}of 0.30 and a highly significant Wald test of 1102.13 (Table 2). All of the model parameters were significant at the 5% level or greater except for the year 2014 dummy variable (Table 2). The effect of each regression coefficient on SCA movement was consistent with expectations, as illustrated in Figure 7. For example, ignoring second-order terms, the northwesterly movement found in each year of the simulation is explained by the negative sign on the X variable (westward movement) and the positive sign on the Y variable (northerly movement).

^{2}that explained roughly one third of the model variance (Table 2). Figure 7 illustrates three years of SCA movement: red subfigures represent simulated movements from the SCA model, whereas green subfigures are predicted movements from the regression model. Colored dots represent sorghum fields where output from either the fracreg regression model or the SCA simulated model was greater than 0.5, the threshold value used when interpreting binary variables. Results indicate that the fracreg regression model is able to predict the overall trend in SCA movement across years, which with an average PDIR = 7.42 over the eight years of simulation corresponds to a north-to-northwesterly movement towards the Oklahoma panhandle. Variations in wind patterns across years, in both speed and direction, were not entirely explained by the fracreg regression model (Figure 7). For example, in 2014, there were unusually strong winds to the northeast that were not well-captured by the fractal regression model (Figure 7). Conditions in 2018, when temperature and rainfall events were unusually strong and resulted in fewer forecasted infestations, were also difficult for the regression model to accurately predict (Figure 7).

#### 3.2. Discussion

#### 3.3. Population Survivability

#### 3.4. Weather Persistence and Improved Forecasting

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Aruna, C.; Suguna, M.; Visarada, K.; Deepika, C.; Ratnavathi, C.; Tonapi, V. Identification of sorghum genotypes suitable for specific end uses: Semolina recovery and popping. J. Cereal Sci.
**2020**, 93, 102955. [Google Scholar] [CrossRef] - Foreign Agricultural Service (FAS). Available online: https://www.fas.usda.gov/data (accessed on 28 February 2023).
- Brewer, M.J.; Gordy, J.W.; Kerns, D.L.; Woolley, J.B.; Rooney, W.L.; Bowling, R.D. Sugarcane aphid population growth, plant injury, and natural enemies on selected grain sorghum hybrids in Texas and Louisiana. J. Econ. Entomol.
**2017**, 110, 2109–2118. [Google Scholar] [CrossRef] - Gordy, J.W.; Brewer, M.J.; Bowling, R.D.; Buntin, G.D.; Seiter, N.J.; Kerns, D.L.; Reay-Jones, F.P.; Way, M. Development of economic thresholds for sugarcane aphid (Hemiptera: Aphididae) in susceptible grain sorghum hybrids. J. Econ. Entomol.
**2019**, 112, 1251–1259. [Google Scholar] [CrossRef] [PubMed] - Bergtold, J.S.; Ramsey, S.; Maddy, L.; Williams, J.R. A review of economic considerations for cover crops as a conservation practice. Renew. Agric. Food Syst.
**2017**, 34, 62–76. [Google Scholar] [CrossRef] - Smith, C.M.; Chuang, W.P. Plant resistance to aphid feeding: Behavioral, physiological, genetic and molecular cues regulate aphid host selection and feeding. Pest Manag. Sci.
**2014**, 70, 528–540. [Google Scholar] [CrossRef] [PubMed] - Elliott, N.; Brewer, M.; Seiter, N.; Royer, T.; Bowling, R.; Backoulou, G.; Gordy, J.; Giles, K.; Lindenmayer, J.; McCornack, B. Sugarcane Aphid1 Spatial Distribution in Grain Sorghum Fields. Southwest. Entomol.
**2017**, 42, 27–35. [Google Scholar] [CrossRef] - Szczepaniec, A. Assessment of a density-based action threshold for suppression of sugarcane aphids,(Hemiptera: Aphididae), in the Southern High Plains. J. Econ. Entomol.
**2018**, 111, 2201–2207. [Google Scholar] - Ukoroije, R.B.; Otayor, R.A. Review on the bio-insecticidal properties of some plant secondary metabolites: Types, formulations, modes of action, advantages and limitations. Asian J. Res. Zool.
**2020**, 3, 27–60. [Google Scholar] - Chen, C.; Harvey, J.A.; Biere, A.; Gols, R. Rain downpours affect survival and development of insect herbivores: The specter of climate change? Ecology
**2019**, 100, e02819. [Google Scholar] [CrossRef] [Green Version] - Kobori, Y.; Amano, H. Effect of rainfall on a population of the diamondback moth, Plutella xylostella (Lepidoptera: Plutellidae). Appl. Entomol. Zool.
**2003**, 38, 249–253. [Google Scholar] [CrossRef] [Green Version] - Huberty, A.F.; Denno, R.F. Plant water stress and its consequences for herbivorous insects: A new synthesis. Ecology
**2004**, 85, 1383–1398. [Google Scholar] [CrossRef] - Ye, S.; Rogan, J.; Zhu, Z.; Hawbaker, T.J.; Hart, S.J.; Andrus, R.A.; Meddens, A.J.; Hicke, J.A.; Eastman, J.R.; Kulakowski, D. Detecting subtle change from dense Landsat time series: Case studies of mountain pine beetle and spruce beetle disturbance. Remote Sens. Environ.
**2021**, 263, 112560. [Google Scholar] [CrossRef] - Gutierrez, J.; Barry-Ryan, C.; Bourke, P. The antimicrobial efficacy of plant essential oil combinations and interactions with food ingredients. Int. J. Food Microbiol.
**2008**, 124, 91–97. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Magstadt, S.; Gwenzi, D.; Madurapperuma, B. Can a Remote Sensing Approach with Hyperspectral Data Provide Early Detection and Mapping of Spatial Patterns of Black Bear Bark Stripping in Coast Redwoods? Forests
**2021**, 12, 378. [Google Scholar] [CrossRef] - Meddens, A.J.; Hicke, J.A. Spatial and temporal patterns of Landsat-based detection of tree mortality caused by a mountain pine beetle outbreak in Colorado, USA. For. Ecol. Manag.
**2014**, 322, 78–88. [Google Scholar] [CrossRef] - Leal-Sáenz, A.; Waring, K.M.; Sniezko, R.A.; Menon, M.; Hernández-Díaz, J.C.; López-Sánchez, C.A.; Martínez-Guerrero, J.H.; Mariscal-Lucero, S.D.R.; Silva-Cardoza, A.; Wehenkel, C. Differences in cone and seed morphology of Pinus strobiformis and Pinus ayacahuite. Southwest. Nat.
**2021**, 65, 9–18. [Google Scholar] [CrossRef] - Robertson, C.; Wulder, M.; Nelson, T.; White, J. Risk rating for mountain pine beetle infestation of lodgepole pine forests over large areas with ordinal regression modelling. For. Ecol. Manag.
**2008**, 256, 900–912. [Google Scholar] [CrossRef] - White, J.; Coops, N.; Hilker, T.; Wulder, M.; Carroll, A. Detecting mountain pine beetle red attack damage with EO-1 Hyperion moisture indices. Int. J. Remote Sens.
**2007**, 28, 2111–2121. [Google Scholar] [CrossRef] - O’Neal, M.R.; Frankenberger, J.R.; Ess, D.R. Use of CERES-Maize to study effect of spatial precipitation variability on yield. Agric. Syst.
**2002**, 73, 205–225. [Google Scholar] [CrossRef] - Kravchenko, A.; Robertson, G.; Thelen, K.; Harwood, R. Management, topographical, and weather effects on spatial variability of crop grain yields. Agron. J.
**2005**, 97, 514–523. [Google Scholar] [CrossRef] [Green Version] - Stevens, S.C. Evidence for a Weather Persistence Effect on the Corn, Wheat and Soybean Growing Season Price Dynamics. 1990. Available online: https://ageconsearch.umn.edu/record/13907/ (accessed on 28 February 2023).
- Weatherhead, E.; Gearheard, S.; Barry, R.G. Changes in weather persistence: Insight from Inuit knowledge. Glob. Environ. Chang.
**2010**, 20, 523–528. [Google Scholar] [CrossRef] - Duffy, C.; Fealy, R.; Fealy, R.M. An improved simulation model to describe the temperature-dependent population dynamics of the grain aphid, Sitobion avenae. Ecol. Model.
**2017**, 354, 140–171. [Google Scholar] [CrossRef] [Green Version] - Ma, Z.S.; Bechinski, E.J. A survival-analysis-based simulation model for Russian wheat aphid population dynamics. Ecol. Model.
**2008**, 216, 323–332. [Google Scholar] [CrossRef] - Acreman, S.; Dixon, A. The effects of temperature and host quality on the rate of increase of the grain aphid (Sitobion avenae) on wheat. Ann. Appl. Biol.
**1989**, 115, 3–9. [Google Scholar] [CrossRef] - Angilletta, J.; Michael, J.; Dunham, A.E. The temperature-size rule in ectotherms: Simple evolutionary explanations may not be general. Am. Nat.
**2003**, 162, 332–342. [Google Scholar] [CrossRef] [Green Version] - Asin, L.; Pons, X. Effect of high temperature on the growth and reproduction of corn aphids (Homoptera: Aphididae) and implications for their population dynamics on the northeastern Iberian peninsula. Environ. Entomol.
**2001**, 30, 1127–1134. [Google Scholar] [CrossRef] - Auad, A.; Alves, S.; Carvalho, C.; Silva, D.; Resende, T.; Veríssimo, B. The impact of temperature on biological aspects and life table of Rhopalosiphum padi (Hemiptera: Aphididae) fed with signal grass. Fla. Entomol.
**2009**, 92, 569–577. [Google Scholar] [CrossRef] - Bale, J.S.; Masters, G.J.; Hodkinson, I.D.; Awmack, C.; Bezemer, T.M.; Brown, V.K.; Butterfield, J.; Buse, A.; Coulson, J.C.; Farrar, J. Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. Glob. Chang. Biol.
**2002**, 8, 1–16. [Google Scholar] [CrossRef] - Murúa, M.G.; Vera, M.A.; Michel, A.; Casmuz, A.S.; Fatoretto, J.; Gastaminza, G. Performance of field-collected Spodoptera frugiperda (Lepidoptera: Noctuidae) strains exposed to different transgenic and refuge maize hybrids in Argentina. J. Insect Sci.
**2019**, 19, 21. [Google Scholar] [CrossRef] [Green Version] - Souza, M.; Davis, J. Detailed characterization of Melanaphis sacchari (Hemiptera: Aphididae) feeding behavior on different host plants. Environ. Entomol.
**2020**, 49, 683–691. [Google Scholar] [CrossRef] [PubMed] - De Souza, M.A.; Armstrong, J.S.; Hoback, W.W.; Mulder, P.G.; Paudyal, S.; Foster, J.E.; Payton, M.E.; Akosa, J. Temperature dependent development of sugarcane aphids Melanaphis Sacchari, (Hemiptera: Aphididae) on three different host plants with estimates of the lower and upper threshold for fecundity. Curr. Trends Entomol. Zool. Stud.
**2019**. [Google Scholar] [CrossRef] - Weisser, W.W.; Volkl, W.; Hassell, M.P. The importance of adverse weather conditions for behaviour and population ecology of an aphid parasitoid. J. Anim. Ecol.
**1997**, 1, 386–400. [Google Scholar] [CrossRef] - Rodríguez-del-Bosque, L.A.; Silva-Serna, M.M.; Aranda-Lara, U. Effect of Natural and Simulated Rainfall and Wind on Melanaphis sacchari1 on Sorghum. Southwest. Entomol.
**2020**, 45, 357–364. [Google Scholar] [CrossRef] - Balikai, R.; Lingappa, S. Bio-Ecology and Management of Sorghum Aphid: Melanaphis Sacchari; LAP LAMBERT Academic Publishing: Saarland, Germany, 2012. [Google Scholar]
- Patel, D.; Purohit, M. Influence of different weather parameters on aphid, Melanaphis sacchari infesting Kharif sorghum. Int. J. Plant Prot.
**2013**, 6, 484–486. [Google Scholar] - Colares, F.; Michaud, J.; Bain, C.L.; Torres, J.B. Indigenous aphid predators show high levels of preadaptation to a novel prey, Melanaphis sacchari (Hemiptera: Aphididae). J. Econ. Entomol.
**2015**, 108, 2546–2555. [Google Scholar] [CrossRef] - Mann, J.A.; Tatchell, G.M.; Dupuch, M.J.; Harrington, R.; Clark, S.J.; McCartney, H.A. Movement of apterous Sitobion avenae (Homoptera: Aphididae) in response to leaf disturbances caused by wind and rain. Ann. Appl. Biol.
**1995**, 126, 417–427. [Google Scholar] [CrossRef] - Shao, X.; Zhang, Q.; Liu, Y.; Yang, X. Effects of wind speed on background herbivory of an insect herbivore. Écoscience
**2020**, 27, 71–76. [Google Scholar] [CrossRef] - Leslie, P.H. On the use of matrices in certain population mathematics. Biometrika
**1945**, 33, 183–212. [Google Scholar] [CrossRef] - Nordheim, E.V.; Hogg, D.B.; Chen, S.-Y. Leslie matrix models for insect populations with overlapping generations. In Estimation and Analysis of Insect Populations; Springer: Berlin/Heidelberg, Germany, 1989; pp. 289–298. [Google Scholar]
- Carter, N.; Aikman, D.; Dixon, A. An appraisal of Hughes’ time-specific life table analysis for determining aphid reproductive and mortality rates. J. Anim. Ecol.
**1978**, 47, 677–687. [Google Scholar] [CrossRef] - Bannerman, J.A.; Roitberg, B.D. Impact of extreme and fluctuating temperatures on aphid–parasitoid dynamics. Oikos
**2014**, 123, 89–98. [Google Scholar] [CrossRef] - Koralewski, T.E.; Wang, H.-H.; Grant, W.E.; Brewer, M.J.; Elliott, N.C. Evaluation of Areawide Forecasts of Wind-borne Crop Pests: Sugarcane Aphid (Hemiptera: Aphididae) Infestations of Sorghum in the Great Plains of North America. J. Econ. Entomol.
**2022**, 115, 863–868. [Google Scholar] [CrossRef] [PubMed] - Wennergren, U.; Landin, J. Population growth and structure in a variable environment: I. Aphids and temperature variation. Oecologia
**1993**, 93, 394–405. [Google Scholar] [CrossRef] [PubMed]

**Figure 1.**Relationship between probability of SCA survival and temperature based on Equation (1) (κ = 0.1, λ = −0.0026).

**Figure 3.**Probability of SCA movement along a straight-line distance from initial infestation to destination field.

**Figure 4.**Probability of SCA migratory flight landing on destination field as a function of angular deviation from daily observed wind direction.

**Figure 5.**Georeferenced map of sorghum fields in Oklahoma identified during the 2018 growing season. Source: CROPSCAPE.

**Figure 6.**SCA flight migration on day 1 of simulation, 15 June 2018, showing the probability of movement from initial infestation to destination fields. Source: Authors’ calculations.

**Figure 7.**Regression predicted (green) and model forecasted (red) SCA movements in Oklahoma: 2014 (

**top**), 2018 (

**middle**), and 2019 (

**bottom**).

Year | TAVG (°F) | RAIN (Inches) | WSPD (Miles/Hours) | PDIR (16-Point) |
---|---|---|---|---|

2013 | 79.80 | 0.13 | 14.80 | 7.29 |

2014 | 77.40 | 0.12 | 13.90 | 7.53 |

2015 | 78.50 | 0.03 | 11.90 | 7.65 |

2016 | 81.40 | 0.10 | 11.10 | 7.63 |

2017 | 77.00 | 0.08 | 10.50 | 6.43 |

2018 | 76.30 | 0.27 | 12.80 | 7.70 |

2019 | 73.30 | 0.13 | 9.78 | 8.32 |

2020 | 77.50 | 0.14 | 13.30 | 6.83 |

Ave | 77.34 | 0.12 | 12.26 | 7.42 |

**Table 2.**Fractional regression results for sugarcane aphid predicted cumulative probability (2013–2020).

Variable | Coeff. | Std. Err | Z | p > |z| | [95% CI] Lower Upper | |
---|---|---|---|---|---|---|

X | −0.0132 ^{a} | 0.001 | −10.22 | 0 | −0.0158216 | −0.010 |

Y | 0.0094 ^{a} | 0.001 | 8.88 | 0 | 0.00739 | 0.0115 |

X^{2} | −0.0003 ^{a} | 1.1 × 10^{−5} | −29.41 | 0 | −0.0003467 | −0.0003 |

Y^{2} | −0.5 × 10^{−3 a} | 8.1 × 10^{−6} | −5.81 | 0 | −0.0000632 | −3.1 × 10^{−5} |

XY | −0.23 × 10^{−3 b} | 1.2 × 10^{−5} | −2.00 | 0.046 | −0.0000467 | −4.20 × 10^{−7} |

2014 | −0.0484 | 0.0612 | −0.79 | 0.429 | −0.1684028 | 0.0715 |

2015 | −0.8925 ^{a} | 0.0981 | −9.09 | 0 | −1.084918 | −0.7001 |

2016 | −0.3168 ^{a} | 0.0778 | −4.07 | 0 | −0.4694808 | −0.1642 |

2017 | −0.2949 ^{a} | 0.0556 | −5.30 | 0 | −0.4040613 | −0.1857 |

2018 | 0.25337 ^{a} | 0.0434 | 5.83 | 0 | 0.168127 | 0.3386 |

2019 | −0.2769 ^{a} | 0.0511 | −5.41 | 0 | −0.3773219 | −0.1766 |

2020 | 0.2020 ^{a} | 0.0445 | 4.53 | 0 | 0.11464 | 0.2894 |

a | −2.305402 ^{a} | 0.035933 | −64.16 | 0 | −2.375828 | −2.23498 |

^{a},

^{b}indicate 1% and 5% significance levels.

Var. | Coef. | Std. Err | z | p > |z| | [95% CI] Lower Upper | |
---|---|---|---|---|---|---|

X | 3.19 × 10^{−6} | 2.03 × 10^{−6} | −1.57 | 0.116 | −7.17 × 10^{−6} | 7.83 × 10^{−7} |

Y | 1.85 × 10^{−5} | 1.77 × 10^{−6} | 10.49 | 0 | 1.51 × 10^{−5} | 0.000022 |

2014 | −0.00036 | 0.000435 | −0.82 | 0.415 | −0.0012079 | 0.0004979 |

2015 | −0.00292 | 0.000218 | −13.4 | 0 | −0.0033454 | −0.00249 |

2016 | −.001765 | 0.000344 | −5.13 | 0 | −0.0024399 | −0.00109 |

2017 | −0.00168 | 0.000282 | −5.95 | 0 | −0.0022324 | −0.00113 |

2018 | 0.002551 | 0.000486 | 5.25 | 0 | 0.001598 | 0.0035047 |

2019 | −0.00161 | 0.000273 | −5.88 | 0 | −0.0021415 | −0.00107 |

2020 | 0.001927 | 0.000462 | 4.17 | 0 | 0.001022 | 0.0028326 |

Test Statistic | TAVG | RAIN | PDIR | WSPD |
---|---|---|---|---|

Serial correlation | ||||

BSJK (LM) | 4.3 ^{b} | 3.3098 ^{c} | 2.6254 | 17.333 ^{a} |

Breusch–Godfrey (LM) | 212.34 ^{a} | 89.813 ^{a} | 235.96 ^{a} | 345.01 ^{a} |

Spatial dependence | ||||

BSJK (LM) | 104.84 ^{a} | 146.37 ^{a} | 60.99 ^{a} | 130.65 ^{a} |

Pesaran CS Dependence (Z) | 108.14 ^{a} | 97.8 ^{a} | 64.473 ^{a} | 105.26 ^{a} |

Joint Serial–Spatial correlation | ||||

BSJK (LM) | 894.68 ^{a} | 579.17 ^{a} | 1042.4 ^{a} | 1569.4 ^{a} |

^{a},

^{b}, and

^{c}mean indicate rejecting the null hypothesis at the 1%, 5%, and 10% significance levels, respectively.

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**MDPI and ACS Style**

Lee, S.; Vitale, J.; Lambert, D.; Vitale, P.; Elliot, N.; Giles, K.
Effects of Weather on Sugarcane Aphid Infestation and Movement in Oklahoma. *Agriculture* **2023**, *13*, 613.
https://doi.org/10.3390/agriculture13030613

**AMA Style**

Lee S, Vitale J, Lambert D, Vitale P, Elliot N, Giles K.
Effects of Weather on Sugarcane Aphid Infestation and Movement in Oklahoma. *Agriculture*. 2023; 13(3):613.
https://doi.org/10.3390/agriculture13030613

**Chicago/Turabian Style**

Lee, Seokil, Jeffrey Vitale, Dayton Lambert, Pilja Vitale, Norman Elliot, and Kristopher Giles.
2023. "Effects of Weather on Sugarcane Aphid Infestation and Movement in Oklahoma" *Agriculture* 13, no. 3: 613.
https://doi.org/10.3390/agriculture13030613