# Drivers of Postharvest Loss among Citrus Farmers in Eastern Cape Province of South Africa: A Zero-Inflated Poisson (ZIP) Regression Model Analysis

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## Abstract

**:**

## 1. Introduction

## 2. Conceptual Framework

## 3. Materials and Methods

#### 3.1. Study Area

^{2}of land, accounting for 13.8% of South Africa’s total land area. The province is situated at an altitude of between 600 and 1600 m as demarcated by the escarpment and thus experiences a mild climate that is suitable for citrus production [23]. Figure 2 shows the study area location.

#### 3.2. Sampling Procedure

#### 3.3. Data Analysis

#### 3.3.1. Estimating PHLs

#### 3.3.2. Empirical Modelling

## 4. Results

#### 4.1. Socio-Economic Characteristics

#### 4.2. ZIP Regression Results

## 5. Discussion

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

AIC | The Akaike’s information criterion |

BIC | Bayesian information criterion |

CGA | Citrus Growers Association |

CRI | Citrus Research International |

ECM | Eastern Cape Midlands |

GAP | Good Agricultural Practices |

GDP | Gross domestic product |

GAIN | Global Agricultural Information Network |

IRR | Incidence risk ratio |

MLE | Maximum likelihood estimation |

OLS | Ordinary least squares |

OR | Odds ratio |

PHL | Postharvest loss |

PHLS | Postharvest loss score |

SDI | Simpson’s diversity index |

SRV | Sundays River Valley |

SSI | Shannon index |

TPHL | Total postharvest loss |

USDA | United States Department of Agriculture |

WPHLI | Weighted postharvest loss index |

ZIP | Zero-inflated Poisson |

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**Figure 2.**Map showing Eastern Cape Province and the sampled municipalities. Source: Stats SA [23], The map was developed by the authors with the help of GIS experts.

Variable | Variable Description |
---|---|

Dependent | |

PHLS | A summed measure of PHLs (calculated in Equation (5)). |

Independent | |

Age | A continuous variable representing the age of the farmer. |

FarmE | A continuous variable showing the experience of the farmer in citrus production in years |

FarmS | A continuous variable showing the farm size in hectares |

Tree age | A continuous variable representing the age of a tree in years |

Yield | A continuous variable showing the average yield for the farm in tonnes |

Gender | Gender of the respondent is a categorical variable representing sex of the farmer (0 = Female, Male = 1) |

Education | A categorical variable showing the level of education attained by the respondent (1 = Tertiary, 2 = Secondary, 3 = Primary, 4 = No education) |

Marital | A categorical variable representing the marital status of the respondent (1 = Single, 2 = Married, 3 = Widow, 4 = Other) |

Extension | A categorical variable showing access to extension services agents (0 = None, 1 = Seldom, 2 = Moderate, 4 = Always) |

Cultivar | A categorical variable showing the citrus tree cultivar grown by the farmer (1 = Novas, 2 = Navels, 3 = Cara Cara, 4 = Lemons) |

Government | A dummy variable showing access to government support (0 = No, 1 = Yes) |

Credit | A dummy variable showing access to credit (0 = No, 1 = Yes) |

Curling | A categorical variable showing the major postharvest curling factors related to weather and climate change as experienced at the farm (1 = Minor injury, 2 = Wind damage, 3 = Long stem, 4 = Red scale, 5 = Creasing, 6 = Thrip damage) |

Challenge | A categorical variable showing the major constraining factors faced by the farmer (1 = High costs, 2 = Pests and diseases, 3 = Lack of reliable markets) |

Pruning | A categorical variable showing whether the farmer practices pruning (0 = No, 1 = Yes) |

Irrigation | A categorical variable showing the type of irrigation system used at the farm for managing weather induced losses (1 = Drip, 2 = Sprinkler, 3 = Flood) |

Tenure | A categorical variable showing the type of land tenure (1 = Own, 2 = Lease, 3 = Communal, 4 = Other) |

Labour | A categorical variable showing the type of labour available at the farm (1 = Permanent, 2 = Seasonal, 3 = Casual) |

Farmers Location | No. of Farmers | Share of Farmers (%) | No. in Sample |
---|---|---|---|

ECM | 40 | 15 | 22 |

Patensie | 90 | 33 | 45 |

SRV | 140 | 52 | 70 |

Total | 270 | 100 | 137 |

Equation | Narration and Sources |
---|---|

$\mathrm{SDI}=1-\left(\frac{{{\displaystyle \sum}}_{i=1}^{n}{n}_{i}\left({n}_{i}-1\right)}{N\left(N-1\right)}\right)\left(1\right)$ | Simpson’s diversity index (SDI): ${n}_{i}$is the contribution of each PHL dimension; N is the total postharvest loss for the farmer. Simpson’s index was used to estimate the diversity of the PHL dimensions in the sampled farming communities. The value of the index determines the extent of prevalence for different PHL dimensions in the farming community and how evenly distributed the population of each dimension is. |

$\mathrm{SI}=-{\displaystyle {\displaystyle \sum}_{i=1}^{n}}{P}_{i}{\mathrm{ln}P}_{i}\left(2\right)$ | Shannon index (SI): n is the number of PHL dimensions and ${P}_{i}$ is the occurrence of each of the PHL dimensions. The Shannon diversity index determines the extent of PHL dimensions diversity in the farming community. The index is closely related to the number of PHL dimensions and the evenness of their occurrence. |

$\mathrm{TPHL}={\displaystyle {\displaystyle \sum}_{i=1}^{n}}\left[{P}_{i}{+G}_{i}{+H}_{i}{+S}_{i}\right]\left(3\right)$ | Total postharvest loss (TPHL): this is a continuous variable where${P}_{i}$,${G}_{i}$, ${H}_{i}$, and ${S}_{i}$ are the PHLs (as percentages) due to picking, grading, handling, and secondary spoilage, respectively. |

$\mathrm{PHLS}\left(4\right)$ | These were the four dimensions of PHLs experienced by farmers, equal to sorting loss + picking loss + mechanical loss. Thus, 0 means no PHLs were encountered, while 1, 2, 3, and 4 indicate a farmer experienced 1, 2, 3, and 4 types of these PHLs, respectively. This was used as the count variable in the regression models. |

$\mathrm{WPHLI}=\frac{\mathrm{TPHL}}{\mathrm{TH}}\left(5\right)$ | TPHL is the total PHLs and TH is the total harvest. WTPHLI is a measure of the total loss due to postharvest management practices relative to the total yield at the farm. It shows the magnitude of the PHLs and how they compromise the net marketable yield. |

Variable | Mean | Variance | Difference Factor |
---|---|---|---|

PHLS_{total} | 0.3577 | 0.793 | 91% |

PHLS_{non-zeros} | 0.614 | 0.719 | 14.6% |

Model | ll (Null) | ll (Model) | df | AIC | BIC |
---|---|---|---|---|---|

Poisson | −114.8647 | −87.9755 | 28 | 231.951 | 313.711 |

ZIP | −106.1129 | −76.12845 | 34 | 220.257 | 294.536 |

Continuous Variables | ||||
---|---|---|---|---|

Variable | Mean | SD | Min | Max |

Experience | 16.102 | 7.059 | 5 | 34 |

Age | 51.971 | 8.895 | 29 | 65 |

Tree age | 17.891 | 6.588 | 5 | 31 |

Yield | 51.336 | 17.037 | 13 | 74 |

Land size | 62.990 | 42.181 | 10.09 | 215.78 |

Loss total | 4.143 | 1.191 | 0 | 60 |

Categorical variables | ||||

Variable | Frequency | Percentage | ||

PHLS | ||||

0 | 105 | 76.64 | ||

1 | 22 | 16.06 | ||

2 | 6 | 4.38 | ||

3 | 1 | 0.73 | ||

4 | 3 | 2.19 | ||

Gender | ||||

Male | 115 | 83.94 | ||

Female | 22 | 16.06 | ||

Marital status | ||||

Single | 50 | 36.50 | ||

Married | 25 | 18.25 | ||

Widow | 27 | 19.71 | ||

Other | 35 | 25.55 | ||

Land tenure | ||||

Own | 72 | 52.55 | ||

Lease | 36 | 26.28 | ||

Communal | 20 | 14.60 | ||

Other | 9 | 6.57 | ||

Irrigation | ||||

Drip | 58 | 42.34 | ||

Sprinkler | 64 | 46.72 | ||

Flood | 15 | 10.95 | ||

Labour | ||||

Permanent | 34 | 24.82 | ||

Seasonal | 55 | 40.15 | ||

Casual | 48 | 35.04 | ||

Extension | ||||

None | 23 | 16.79 | ||

Seldom | 31 | 22.63 | ||

Moderate | 55 | 40.15 | ||

Always | 28 | 20.44 | ||

Market access | ||||

Seldom | 33 | 24.09 | ||

Frequent | 97 | 70.80 | ||

Always | 7 | 5.11 | ||

Education | ||||

Tertiary | 54 | 39.42 | ||

Secondary | 34 | 24.82 | ||

Primary | 25 | 18.25 | ||

None | 24 | 17.52 | ||

Credit access | ||||

Yes | 74 | 54.01 | ||

No | 63 | 45.99 | ||

Pruning | ||||

Yes | 101 | 73.72 | ||

No | 36 | 26.28 | ||

Cultivar | ||||

Novas | 62 | 45.26 | ||

Navels | 30 | 21.90 | ||

Cara Cara | 8 | 5.84 | ||

Lemons | 37 | 27.01 | ||

Curling | ||||

Minor injury | 31 | 22.62 | ||

Wind damage | 33 | 24.09 | ||

Long stem | 13 | 9.49 | ||

Red scale | 28 | 20.44 | ||

Creasing | 17 | 12.41 | ||

Thrip damage | 15 | 10.95 | ||

Challenge | ||||

High costs | 61 | 44.53 | ||

Pests and diseases | 45 | 32.85 | ||

Lack of reliable markets | 31 | 22.63 |

Cultivar | Curling Factors | Simpson’s Index Cluster | Novas | Navels | Cara Cara | Lemons |
---|---|---|---|---|---|---|

Postharvest curling factors * Postharvest loss diversity cluster | Minor injury | 1 | 0.73 | - | - | - |

2 | 2.19 | 0.73 | - | 0.73 | ||

3 | 3.65 | 1.46 | 1.46 | 4.38 | ||

Wind damage | 1 | 2.92 | - | - | - | |

2 | 2.19 | 1.46 | - | 5.84 | ||

3 | 6.57 | 6.57 | 1.46 | 2.19 | ||

Long stem | 1 | - | - | - | - | |

2 | 5.11 | 1.46 | - | 0.73 | ||

3 | 2.92 | 1.46 | 0.73 | 0.73 | ||

Red scale | 1 | 0.73 | - | - | - | |

2 | - | - | - | 1.46 | ||

3 | 2.92 | 1.46 | - | 4.38 | ||

Creasing | 1 | 1.46 | - | - | - | |

2 | 3.65 | 0.73 | - | - | ||

3 | 2.92 | 3 | 0.73 | 5.11 | ||

Thrip damage | 1 | - | - | - | - | |

2 | 2.19 | 0.73 | - | 0.73 | ||

3 | 5.11 | 3.65 | 1.46 | 0.73 |

ZIP Model | IRR/OR | |||
---|---|---|---|---|

Variable | Coef. | Robust (Std. Err.) | Coef. | Std. Err. |

Count component | ||||

Gender_male | −0.015 | 0.634 | 0.985 | 0.625 |

Marital_married | −1.685 ** | 0.859 | 0.185 ** | 0.159 |

Marital_widow | 0.477 | 0.559 | 1.611 | 0.902 |

Marital_other | −0.969 | 0.605 | 0.379 | 0.229 |

Tenure_lease | 1.734 * | 1.047 | 5.666 * | 5.933 |

Tenure_communal | 2.027 | 1.272 | 7.589 | 9.652 |

Tenure_other | 0.460 | 0.879 | 1.584 | 1.392 |

Irrigation_sprinkler | 1.681 *** | 0.554 | 5.371 *** | 2.977 |

Irrigation_flood | 0.374 | 1.341 | 1.453 | 1.949 |

Labour_seasonal | 1.270 ** | 0.522 | 3.562 ** | 1.858 |

Labour_casual | 1.566 *** | 0.488 | 4.784 *** | 2.333 |

Extension_seldom | −1.433 | 1.132 | 0.238 | 0.269 |

Extension_moderate | −1.985 | 1.207 | 0.137 * | 0.166 |

Extension_always | −3.866 *** | 1.356 | 0.021 *** | 0.028 |

Market_frequent | 0.970 | 0.432 | 2.639 | 1.139 |

Market_always | 2.188 *** | 0.841 | 8.914 *** | 7.494 |

Education_secondary | −1.707 *** | 0.636 | 0.181 *** | 0.115 |

Education_primary | −1.910 | 0.887 | 0.148 | 0.131 |

Education_none | −0.735 | 0.615 | 0.479 | 0.295 |

Credit_none | −2.128 * | 1.236 | 0.119 * | 0.147 |

Prunning_none | 1.464 *** | 0.430 | 4.322 *** | 1.859 |

Cultivar_Navels | 0.811 | 0.655 | 2.251 | 1.475 |

Cultivar_Caracara | −1.041 | 0.769 | 0.353 | 0.271 |

Cultivar_lemons | 1.560 *** | 0.389 | 4.759 *** | 1.853 |

Constant | −0.779 | 1.943 | 0.459 | 0.892 |

Logit component | ||||

Yield | −0.236 *** | 0.072 | 0.789 * | 0.429 |

Credit_none | −3.249 * | 1.879 | 1.037 ** | 0.520 |

Extension_seldom | −4.757 | 1.646 | 2.348 | 0.099 |

Extension_moderate | −5.021 | 1.655 | 1.945 ** | 1.058 |

Extension_always | −9.901 * | 5.322 | 0.886 | 0.524 |

Tree_age | −0.277 * | 0.121 | 1.323 ** | 0.663 |

Cultivar_Navels | 6.931 | 2.143 | 1.389 | 0.704 |

Cultivar_Caracara | 4.571 ** | 1.400 | 0.009 ** | 0.021 |

Cultivar_lemons | 6.339 *** | 1.862 | 2.348 * | 0.949 |

Experience | −0.299 | 0.125 | 0.889 | 0.427 |

Age | 0.172 * | 0.098 | 0.789 ** | 0.376 |

Constant | 13.464 *** | 3.927 | 4.036 *** | 1.297 |

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## Share and Cite

**MDPI and ACS Style**

Nohamba, S.; Musara, J.P.; Bahta, Y.T.; Ogundeji, A.A. Drivers of Postharvest Loss among Citrus Farmers in Eastern Cape Province of South Africa: A Zero-Inflated Poisson (ZIP) Regression Model Analysis. *Agriculture* **2022**, *12*, 1651.
https://doi.org/10.3390/agriculture12101651

**AMA Style**

Nohamba S, Musara JP, Bahta YT, Ogundeji AA. Drivers of Postharvest Loss among Citrus Farmers in Eastern Cape Province of South Africa: A Zero-Inflated Poisson (ZIP) Regression Model Analysis. *Agriculture*. 2022; 12(10):1651.
https://doi.org/10.3390/agriculture12101651

**Chicago/Turabian Style**

Nohamba, Siphiw’okuhle, Joseph P. Musara, Yonas T. Bahta, and Abiodun A. Ogundeji. 2022. "Drivers of Postharvest Loss among Citrus Farmers in Eastern Cape Province of South Africa: A Zero-Inflated Poisson (ZIP) Regression Model Analysis" *Agriculture* 12, no. 10: 1651.
https://doi.org/10.3390/agriculture12101651