Mango Orchards Research Articles

Exploring the Relationship Between Very-High-Resolution Satellite Imagery Data and Fruit Count for Predicting Mango Yield at Multiple Scales

Tree- and block-level prediction of mango yield is important for farm operations, but current manual methods are inefficient. Previous research has identified the accuracies of mango yield forecasting using very-high-resolution (VHR) satellite imagery and an ’18-tree’ stratified sampling method. However, this approach still requires infield sampling to calibrate canopy reflectance and the derived block-level algorithms are unable to translate to other orchards due to the influences of abiotic and biotic conditions. To better appreciate these influences, individual tree yields and corresponding canopy reflectance properties were collected from 2015 to 2021 for 1958 individual mango trees from 55 orchard blocks across 14 farms located in three mango growing regions of Australia. A linear regression analysis of the block-level data revealed the non-existence of a universal relationship between the 24 vegetation indices (VIs) derived from VHR satellite data and fruit count per tree, an outcome likely due to the influence of location, season, management and cultivar. The tree-level fruit count predicted using a random forest (RF) model trained on all calibration data produced a percentage root mean squared error (PRMSE) of 26.5% and a mean absolute error (MAE) of 48 fruits/tree. The lowest PRMSEs produced from RF-based models developed from location, season and cultivar subsets at the individual tree level ranged from 19.3% to 32.6%. At the block level, the PRMSE for the combined model was 10.1% and the lowest values for the location, seasonal and cultivar subset models varied between 7.2% and 10.0% upon validation. Generally, the block-level predictions outperformed the individual tree-level models. Maps were produced to provide mango growers with a visual representation of yield variability across orchards. This enables better identification and management of the influence of abiotic and biotic constraints on production. Future research could investigate the causes of spatial yield variability in mango orchards.

Balanço nutricional em mangueiras no semiárido brasileiro utilizando métodos diagnósticos nutricionais

ABSTRACT Nutritional assessment of mango trees based on diagnostic methods considering nutritional balance is recommended. This study aimed to establish optimum nutritional ranges using diagnostic methods, compare them, identify the most efficient diagnostic method, and select the nutrients responding best to the application of the diagnostic method. The study was conducted in commercial mango orchards in the São Francisco Valley. Nutritional content was calculated using the diagnosis and recommendation integrated system (DRIS-Beaufils, DRIS-Jones), modified DRIS (M-DRIS-Beaufils; M-DRIS-Jones), compositional nutrient diagnosis (CND), and mathematical chance (ChM) methods and compared using the chi-square test. Principal component analysis was applied to select the most efficient diagnostic method and the nutrients responsible for the greatest variability. The DRIS-Beaufils, M-DRIS-Beaufils, DRIS-Jones, M-DRIS-Jones, CND, and ChM methods generated nutritional sufficiency ranges for the evaluated cultivars. The nutritional diagnoses of the DRIS-Beaufils and M-DRIS-Beaufils methods were similar and discordant with those of DRIS-Jones, M-DRIS-Jones, and CND. The DRIS-Beaufils method, updated by Maia, proved to be more consistent for the nutritional assessment of mango trees. The nutrients N, P, K, Mg, and S in the Tommy Atkins cultivar; N, P, Mg, S, B, Mn, Zn, Mo, and Cl in the Kent cultivar; and N, P, K, Ca, S, B, Cu, Fe, Zn, Mo, and Cl in the Keitt cultivar showed significant responses to the application of the DRIS-Beaufils method updated by Maia.

A SURVEY REGARDING MANGO FRUIT BORER INFESTATION IN MANGO ORCHARDS OF PUNJAB PAKISTAN

Mango is an important tropical fruit crop grown in most parts of the world including India, Pakistan, China, Bangladesh, Burma, Thailand, Japan, New Zealand, Australia, Indonesia, Africa, Brazil, Mexico and some US states. Mangan o is an important fruit crop for Pakistan due to its delicious taste, rich in vitamins A and C-and high-income returns to mango growers. However, in recent years a new pest problem mango fruit borer extensively damaged mango fruit crop in all mango growing districts of Punjab and Sindh. This insect was previously not even recognized as an important pest of mangoes in Pakistan among farmers and the scientific community, however since 2017, very serious complaints have been received from mango orchard owners regarding damage caused by MFB in the fruit clusters. Hence a survey was conducted in four districts of Punjab Pakistan viz., Multan, Muzaffargarh, Khanewal, and Rahim Yar Khan. About 145 mango growers were randomly chosen for interviews to learn about farmer perceptions and management of mango fruit borer. A series of open-ended and closed-ended questions were asked. Data was analyzed through the Chi-square test using Statistix 8.1 software. Farmers were asked various questions regarding farmers' identification of MFB stages and the nature of the damage, varietal preference of MFB, resting or hibernating places of the pest, insecticides used to control the pest, weeds as the reservoir of the pest and many more. Overall, most of the farmers were primarily qualified, males and had orchard sizes of 2 to 10 acres. Farmers reported that Sindhri was mostly affected by variety. Farmers used insecticides to control the pest menace and achieved around 40-80 % control. The knowledge about species recognition was very low among farmers. Based on this survey, we recommend farmer training programs regarding pest management, enhancing awareness among farmers regarding pest attacks, pest scouting, pest recognition and incorporation of cultural control techniques along with insecticides as a necessary part of pest management should be explained.

Mango leaf disease diagnosis using Total Variation Filter Based Variational Mode Decomposition

Mango leaf diseases significantly threaten mango cultivation, impacting both yield and quality. Accurate and early diagnosis is essential for effectively managing and controlling these diseases. This study introduces a novel approach for diagnosing mango leaf diseases, leveraging Total Variation Filter-based Variational Mode Decomposition. The proposed method enhances the extraction of disease-specific features from leaf images by decomposing them into intrinsic mode functions while simultaneously reducing noise and preserving important edge information. Experimental results demonstrate that the proposed method effectively isolates relevant patterns associated with various mango leaf diseases, improving diagnostic accuracy compared to traditional methods. Deep learning models, DenseNet121 and VGG-19, are used for feature extraction from sub-band images, and extracted features are concatenated and fed to Random Forest for classification. Utilizing tenfold cross-validation, our model demonstrated enhanced classification accuracy (98.85 %), specificity (99.37 %), and sensitivity (98.0 %) in detecting diseases from Mango leaf images. Feature maps and Gradient-weighted Class Activation Mapping analysis was conducted to visualize and scrutinize the essential regions crucial for accurate predictions. Statistical analysis indicates that our proposed architecture outperforms pre-trained models and existing mango leaf disease detection methods. This diagnostic approach can be a rapid disease detection tool for imaging specialists utilizing leaf images. The robustness and efficiency of the presented work in handling complex and noisy image data make it a promising tool for automated agricultural disease diagnosis systems, facilitating timely and precise interventions in mango orchards.

Evaluating the Efficacy of the Male Annihilation Technique in Managing Oriental Fruit Fly (Diptera: Tephritidae) Populations through Microscopic Assessment of Female Spermathecae.

The male annihilation technique (MAT) plays a crucial role in the pest management program of the oriental fruit fly, Bactrocera dorsalis (Hendel) (Diptera: Tephritidae). However, a suitable method for real-time and accurate assessment of MAT's control efficiency has not been established. Laboratory investigations found that motile sperms can be observed clearly under the microscope when the spermathecae dissected from mated females were torn, and no sperms were found in the spermathecae of virgin females. Furthermore, it was confirmed that sperms can be preserved in the spermathecae for more than 50 days once females have mated. Laboratory results also indicated that proportion of mated females decreased from 100% to 2% when the sex ratio (♀:♂) was increased from 1:1 to 100:1. Further observation revealed that there were no significant differences in the superficial area of the ovary or spermatheca between mated females and virgin females. Field investigations revealed that the proportion of mated females (PMF) could reach 81.2% in abandoned mango orchards, whereas the PMF was less than 36.4% in mango orchards where MAT was applied. This indicates that the PMF of the field population can be determined by examining the presence of sperms in the spermathecae. Therefore, we suggest that this method can be used to monitor the control efficiency when MAT is used in the field.

Identification of entomopathogenic fungi from insect pests of mango and their efficacy against mango hoppers

Surveys were made in the Sindhudurg and Ratnagiri districts of the Konkan region of Maharashtra, India during 2017-2018 for the collection of mummified insect samples from different mango orchards in order to isolate entomopathogenic fungi. The collected mummified insect samples were brought to the laboratory and incubated on PDA plates. Those samples showing growth on PDA plates were sub-cultured until pure fungus growth was obtained. They were tested for their pathogenicity in the laboratory and 14 samples were found pathogenic to mango hoppers. They were sent to NFCCI, Agarkar Research Institute, Pune for identification. Out of the 14 entomopathogenic samples identified, 7 were from Genus Fusarium, 4 were mixed cultures of Fusarium and Aspergillus, and one sample each from Genus Trichoderma, Mucor and Nigrospora. Studies showed that Fusarium species were the most common entomopathogenic fungi associated with mango pests, especially mango hoppers. Laboratory evaluation of these samples showed that amongst different isolated entomogenus fungi, <i>Fusarium semitectum</i> sample 16(1) was more pathogenic to mango hoppers than other fungal samples. Field evaluation of this sample also proved its efficacy.

More the merrier? influence of mango orchards on the composition of the reptile communities of the lateritic plateaus, Maharashtra, India

AbstractChange in land‐use patterns affects a landscape by changing the composition of local flora and fauna. The lateritic plateaus in Konkan are one such example of this rapid land‐use change. Although these plateaus are rich in endemic flora and fauna, they are considered as “wastelands” due to lack of woody vegetation and thus, are subjected to various land‐uses. Mango cultivation in this region has become a major source of income due to its Alphonso variety of mangoes. Hence, over the last few decades, many plateaus have been converted into mango orchards. However, how the conversion of plateaus to mango orchards has affected species composition of different groups of organisms remains unclear. In the present study, we sampled the lateritic plateaus and plateaus converted into mango orchards in Ratnagiri, Maharashtra, to investigate the response of the reptile communities. We observed more species heterogeneity in the orchards compared to the unaltered sites. Moreover, it appeared that there was a species turnover when the plateaus were converted into orchards. The generalist and widespread species such as agamids and skinks and very few specialist species were comparatively more abundant in the orchards. On the contrary, the plateaus without orchards harbored more specialist species such as Hemidactylus albofasciatus (n = 62, in unaltered sites vs. n = 5 in the orchards), Echis carinatus (n = 15 in unaltered sites and none in the orchards) and Ophisops jerdonii (n = 45 in unaltered site vs. n = 12 in the orchards) compared to the generalist species.

Factors influencing adoption of agro-ecological pest management options for mango fruit fly under information constraints: a two-part fractional regression approach

The catalytic effect of climate change on the emergence and prevalence of invasive alien pests along with weak pesticide regulatory frameworks in developing countries calls for a transition towards sustainable pest management. Agro-ecological pest management (APM) offers a nature-based, cost-effective alternative for addressing systemic pest challenges, such as mango fruit fly invasion. We applied a two-part fractional regression to sequentially model APM adoption and intensity decisions among 423 smallholder mango orchard managers from Makueni County, Kenya. Despite APM’s potential, we observed moderate adoption rates (56.7%), with the average adopter implementing only 25% of the APM practices concurrently. Farmers’ socio-psychological attributes significantly influenced both adoption and intensity decisions. While perceptions of technology attributes and institutional and social factors primarily influenced both the adoption and intensity decisions, information constraints, resource endowment, gender and inter-generational factors significantly influenced only the intensity decision. To support the transition from synthetic insecticides to APM measures, policymakers should create more opportunities for awareness creation, training and knowledge co-creation and co-production, particularly through social networks and gender-disaggregated participatory group approaches.

Local and landscape constraints of adult population of Anastrepha obliqua (Diptera: Tephritidae) in mango orchards

Introduction: The West Indian fruit fly, Anastrepha obliqua (Macquart), is one of the most economically important pests of mangoes in Mexico and the Neotropics. Objective: To analyze adult population patterns of A. obliqua and their relation to local and landscape factors. Methods: We selected 11 “Manila” mango orchards along the middle part of La Antigua River, which were characterized to determine their biophysical structure and landscape configuration considering their distance to six different land uses/land covers. Anastrepha obliqua population was estimated by collecting adult flies in colorless polyethylene plastic bottle traps baited with a protein for three weeks in June 2022. Results: A total of 1 869 adults of A. obliqua were trapped, of which 75 % were females and 25 % were males. Abundance varied from 68 to 490 adult flies per orchard, while capture frequency from 0.36 to 1.8 flies/trap/day. Abundance increased in highly shaded orchards, and capture frequency decreased in orchards with highly soil compaction. Abundance and the frequency of capture increased in orchards near isolated trees of Spondias spp. and decreased in orchards near sugarcane plantations. Shade management in mango orchards may be used as an effective strategy to promote biotic interactions to naturally regulate A. obliqua populations, meanwhile, soil compaction represents a limiting ecological condition, which might severely impact fruit fly larvae survival. Conclusions: Adult population of A. obliqua depends on several local and landscape factors because they indicate resource availability and ecological conditions. These findings could be considered for control strategies in the integrated management of this pest to promote protection and improve the fruit quality of mango crop.

Classification of infection grade for anthracnose in mango leaves under complex background based on CBAM-DBIRNet

Anthracnose is one of the most harmful fungal diseases in mango orchards. Accurate and rapid detection of anthracnose infection grade for mango leaves in complex natural environments is a prerequisite of precise variable pesticide spraying by robots in intelligent orchards. In this paper, A lightweight double branch inverted residual attention network (CBAM-DBIRNet) model was proposed by combining the inverted residual network and the attention mechanism, which was used to detect the infection grade of anthracnose for mango leaves in natural environment. The model designed two parallel residual branch structures to extract multi-scale features of mango leaves with different infection grades through different dilation coefficients. The inverted residual structure and depthwise separable convolution were used to reduce the complexity of the model. The CBAM attention module was integrated into the model to suppress the interference of complex background information. The mango leaves collected in the natural orchard were used for experiments. The results showed that the accuracy, recall and F1 of the CBAM-DBIRNet model for the classification of anthracnose infection grade were 98.42 %, 97.80 % and 97.89 %, respectively. The model size and parameter were 0.64 MB and 0.15 M, respectively. This study provides technical support for smart orchard management and robotic precision variable application operations.

Current status of white mango scale (Aulacaspis Tubercularis Newstead) and associated factors in Assosa district western Ethiopia

White mango scale (Aulacaspis tubercularis Newstead; Hemiptera: Diaspididae) is a major insect pest of mango production threatening production and marketing of mango in Ethiopia. The present study was conducted to determine the current status of the white mango scale (WMS) and to identify the factors contributing to the damage level of the pest. A cross-sectional survey was conducted in ten selected kebeles in Assosa district, western Ethiopia, during 2021/2022 cropping season. Data on infestation level and WMS life-stage abundance were recorded from fifty randomly selected mango orchards. Data on the prevalence of WMS and factors influencing the distribution and damage levels of white mango scale infestations were analyzed using SAS and SPSS software respectively. The results of this study indicate that WMS infestation was prevalent in all assessed mango growing areas of the district. There was a significant (p < 0.05) difference between kebeles and mango orchards in infestation level and WMS life-stage population densities. Poor cultivation, crop husbandry practices, and pest management strategies have been identified as the major contributing factors for high pest infestation. The study indicates that WMS infestations are increasing in the study area; thus, there has to be strong efforts towards the implementation of safe and effective management strategies.

The Effects of Planting Density, Training System and Cultivar on Vegetative Growth and Fruit Production in Young Mango (Mangifera indica L.) Trees

Increasing the planting density of mango orchards appears promising for obtaining higher yields, particularly during the first productive years. However, the challenge is to maintain a good balance between vegetative growth and fruit production in the longer term. The objective of this study was to decipher the effects of planting density, training system and cultivar on young mango trees’ growth and production. The experiment, conducted in North Queensland, consisted of five combinations of planting density and training system applied to the cultivars Keitt, Calypso and NMBP-1243. The planting densities were low (208 tree ha−1), medium (416 tree ha−1) and high (1250 tree ha−1). The closed vase conventional training system was applied at each density. Single leader and espalier on trellis training systems were applied at medium and high densities, respectively. The tree canopy dimensions were measured every 6 months from planting, and tree production was recorded from the third to the fifth years after planting. Vegetative growth and fruit production were the results of complex interactions between planting density, training system, cultivar and/or time. The expected increase in orchard yield with higher planting density was observed from the first productive year, despite lower individual tree production at high planting density. Lower vegetative growth and fruit production at high planting density were probably caused by competition between trees. NMBP-1243 and Keitt showed more rapid vegetative growth. Keitt was the most productive cultivar during the first three productive years. The detailed results of this study provide avenues to further explore the behaviour of mango trees at high planting densities.

Estimation of terrestrial carbon stock as influenced by mango orchard at Dhule region

Estimation of terrestrial carbon stock as influenced by mango orchard at Dhule region

PREVALANCE AND MANAGEMENT OF SOME MAJOR DISEASES OF MANGO AT AFGOYE AND BALAD DISTRICT IN SOMALIA

The experiments were conducted in different mango orchards of Afgoye and Balad districts in Somalia under the faculty of Agriculture, Zamzam Universityof Science and Technology during February to April, 2020 on prevalence and management of mango diseases. Altogether, seven different diseases viz. Anthracnose, die-back, powdery mildew, sooty mold, red rust, bacterial leaf blight and bacterial leaf spot were recorded. All the diseases were common in both regions but these varied from orchard to orchard. Among the diseases, severity of Anthracnose (35.3%), die-(20.67%), powdery mildew (45.17%), red rust (13.75%) and bacterial leaf sight (54.33%) were found major problem in Balad districts. In Afgoye , Anthracnose (16.033%), sooty mold (18.90%), bacterial leaf blight( 19.833%) and bacterial leaf spot (8.927%) were found the highest severity. fungicides Caroozate 45, Carbazim, respectively, were evaluated and found that Carbazim were good effect in controlling Anthracnose, sooty mold, red rust and bacterial leaf spot. Caroozate 45 was also effective in controlling Anthracnose, die-back, powdery mildew, red rust al leaf spot disease. like similarly weused botanicals fungcide, Such as Neemand Garlic well cake.respectively,were evaluated and found that Neem with Garlic were good effect in controlling Anthracnose, sooty mold, red rust and bacterial leaf spot. Neem was also effective in controlling Anthracnose, die-back, powdery mildew, red rust al leaf spot disease.

Standardization of Mass Rearing of Mango Leafhoppers

This study involved three predominant mango hopper species viz.: Idioscopus clypealis (Lethierry), Idioscopus nitidulus (Walker), and Amritodus atkinsoni (Lethierry). The nymphs and adults were collected from a mango orchard in Bhubaneswar, Odisha, India. To rear the leafhoppers, plastic containers with moist sand and mango twigs as food sources were used in different environments, including room temperature, an open environment, and a B.O.D incubator with controlled temperature (25°C) and relative humidity (75%). The survival and longevity of nymphs and adults were recorded. The results showed that rearing in the B.O.D incubator consistently resulted in the highest longevity rates for both nymphs and adults. Rearing in the open environment had the lowest longevity rates, while room temperature rearing fell in between. These findings highlight the importance of maintaining consistent temperature and humidity conditions for improved longevity in mango leafhopper populations.

Developing Machine Vision in Tree-Fruit Applications-Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting.

Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need for the release of training and test datasets with any work reporting model development is emphasized to enable the re-evaluation of published work. An additional reporting need is the documentation of the performance of the re-training of a given model, quantifying the impact of stochastic processes in training. Three mango orchard applications were considered: the (i) fruit count, (ii) fruit size and (iii) branch avoidance in automated harvesting. All training and test datasets used in this work are available publicly. The mAP 'coefficient of variation' (Standard Deviation, SD, divided by mean of predictions using models of repeated trainings × 100) was approximately 0.2% for the fruit detection model and 1 and 2% for the fruit and branch segmentation models, respectively. A YOLOv8m model achieved a mAP50 of 99.3%, outperforming the previous benchmark, the purpose-designed 'MangoYOLO', for the application of the real-time detection of mango fruit on images of tree canopies using an edge computing device as a viable use case. YOLOv8 and v9 models outperformed the benchmark MaskR-CNN model in terms of their accuracy and inference time, achieving up to a 98.8% mAP50 on fruit predictions and 66.2% on branches in a leafy canopy. For fruit sizing, the accuracy of YOLOv8m-seg was like that achieved using Mask R-CNN, but the inference time was much shorter, again an enabler for the field adoption of this technology. A branch avoidance algorithm was proposed, where the implementation of this algorithm in real-time on an edge computing device was enabled by the short inference time of a YOLOv8-seg model for branches and fruit. This capability contributes to the development of automated fruit harvesting.

Hydrological Changes in a Fruit Growing Area Under the Influence of Climate Change: on the Amazon Coast

Objective: The objective of this study is to investigate the water consumption of the mango orchard using the method proposed by Thornthwaite and Mather (1955), called Climatological Water Balance, with the aim of assisting in agricultural planning and decision making. Theoretical Framework: The Climatological Water Balance was initially developed with the aim of characterizing the climate of a region, in order to be used in the climate classification developed by Thornthwaite in the 1940s. Later, this method began to be used for agronomic purposes given the great interrelationship of agriculture with climatic conditions. Method: The methodology adopted for this research comprises the information used in the preparation of the water balance and was acquired by instrumentation implemented at the experimental hose site in the village of Cuiarana, district of Salinópolis in the northeast of the state of Pará. The meteorological data were obtained in the Project tower LBA where are measured, by automatic instrumentation, such as: average air temperature (Tar) the relative air humidity (RH) through the HMP45 probe, the Rain acquired by the CSI MODEL TB4-L RAIN GAGE ​​from Campbell Scientific, the collection of data was carried out using a datalogger with recording every 10s and storage of the average every 10 min. Results and Discussion: The results obtained revealed an increase in air temperature mainly in El-niño years and with a strong reduction in rainfall in these years of approximately 30 to 40% in the annual accumulation. These results imply a reduction in productivity and the regional and local impact of the hydrological cycle. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of fruit growing. These implications may include the importance of the vegetation cover of the mango orchard on the socio-environmental quality of the village of Cuiarana. Originality/Value: This study contributes to the literature on mango crops that are not irrigated, it also makes it possible to optimize sowing times, taking into account the average water availability in the studied region, resulting in a more efficient use of rainfall for agricultural production. The relevance and value of this research are evidenced by the fact that the study in the northeast of Pará is still precarious in terms of water consumption by the plant in a region with water deficiency.

Responses of Nutritional Status and Productivity of Timor Mango Trees to Foliar Spray of Conventional and/or Nano Zinc

The management of mango orchards is beset with a number of issues, including micronutrient deficits and significant fruit drop, which both contribute to decreases in mango tree output. Among these micronutrients, zinc is vital for increasing agricultural productivity, ensuring crop sustainability, and improving plant nutritional status during the growing season. To overcome zinc (Zn) deficiencies, this study was carried out during two successive seasons in an expected “Off” year (2022) and an expected “On” year (2023) on mature mango trees cv. Timor. To ameliorate this Zn deficiency, the effect of zinc in three forms—zinc oxide nanoparticles (ZnO NPs), sulfate (ZnSO4), and chelated (Zn-chelated)—as a foliar sprayon leaves’ mineral, chlorophyll, total carotenoids, and total carbohydrate contents and productivity were studied. Ten spray treatments were used in this study, including nano zinc (100 ppm), zinc sulfate (0.1%), and chelated zinc (0.2%) on two occasions, 7 January and 4 weeks after the first application, either alone or in combination with each other as compared to the control. In both study seasons, the results showed that all the zinc forms in mineral, chelated, or nano form had a positive effect on the number of flowers per panicle, the percentage of fruit set, the number of fruits per panicle, and the number of fruits per tree, and it decreased the percentage of fruit drop. Furthermore, all zinc forms significantly increased the leaf N, P, K, Ca, Mg, Cu, Fe, Mn, B, and Zn contents (%), and all the treatments improved the chlorophyll, total carotenoid, and total carbohydrate contents compared with the other treatments. The most effective treatment was two applications of nano zinc at 100 ppm in terms of the nutritional status and productivity of Timor mango trees.

Wild native insects are efficient pollinators of mangoes in the Northern Territory of Australia

Mango is an economically important fruit crop in tropical regions, including parts of Australia. However, the contribution of native flower visitors to mango pollination and fruit set is currently understudied. We studied cv. Kensington Pride (KP) in the Northern Territory (NT) of Australia to assess the overall contribution of insect pollinators to fruit set through pollinator exclusion treatments – open pollination, total pollinator exclusion and large pollinator exclusion (>5 mm). We also compared diurnal patterns in insect visitation with mango phenology and determined the pollination effectiveness and efficiency of flower visitors based on visitation rates, foraging behaviour and pollen deposition. At 45 days after the first pollinator survey, the number of fruits per panicle in open and large pollinator exclusion treatments was not significantly different from one another, despite large pollinator exclusion panicles receiving less than half of the number of insect visitations. Furthermore, open and large pollinator exclusion treatments had 28- and 30-times higher numbers of fruits per panicle than the total pollinator exclusion treatment, respectively, showing the importance of insect pollination. The native stingless bee Tetragonula mellipes was the most frequent visitor (7906 visits, 62.7 %), followed by a hover fly Mesembrius bengalensis (2404 visits, 19.1 %). Tetragonula mellipes had the highest mango pollen loads on their bodies and deposited more pollen grains on stigmas per visit than other flower visitors, and contributed 75.5 % to the mango pollination services, about 4.5 times more than the next most important pollinator (M. bengalensis: 16.8 %). Our results suggest that T. mellipes efficiently pollinate mango flowers in the NT when wild colonies are abundant in the native vegetation adjacent to the crop. Placing T. mellipes hives in the centre of larger mango orchards may increase pollination services and potentially farm productivity by ensuring good pollination towards the centre of orchard blocks.

Estimating crop coefficients and water use of a full-bearing mango orchard in north-eastern South Africa using the fraction of vegetation cover and a dual source evapotranspiration model

In semi-arid countries like South Africa, commercially produced mango (Mangifera Indica L.) are grown entirely under irrigation. However, there is little accurate quantitative information on the water use of mango orchards, and few accurate water use models currently exist. In this study, we evaluated the performance of the FAO 56 method of estimating crop evapotranspiration (ETc) and a dual source water use model against measurements of actual consumptive water use of a commercial mango orchard. The FAO approach calculates ETc as the product of a crop coefficient (Kc) and the reference evapotranspiration (ETo). We derived Kc for well-watered orchards from readily available data including the fraction of vegetation cover, average tree height, and a stomatal control coefficient. The dual-source model calculated orchard evapotranspiration as the sum of tree transpiration and orchard floor evaporation derived from the modified Shuttleworth and Wallace model. Actual evapotranspiration was measured using an open path eddy covariance system, while tree transpiration was measured using the heat ratio method of monitoring sap flow. Data were collected in a mature ‘Tommy Atkins’ mango orchard grown with micro-sprinkler irrigation in north-eastern South Africa. Results show that the reference evapotranspiration explained most of the variation in orchard transpiration (R2 ∼ 0.78) compared to the solar radiation (R2 ∼ 0.66) and the vapour pressure deficit of the air (R2 ∼ 0.66). Compared with field measured water use data, the FAO method gave relatively accurate estimates of transpiration (R2 = 0.74, NRMSE = ±18.00 %, NMAE = ±15.00 %, NSE = 0.70) and evapotranspiration (R2 = 0.58, NRMSE = ±19.00 %, NMAE = ±16.00 %, NSE = 0.55). NSE is the Nash-Sutcliffe Efficiency. The dual-source model yielded slightly less accurate estimates of transpiration (R2 = 0.61, NRMSE = ±22.00 %, NMAE = ±20.00 %, NSE = 0.39) and evapotranspiration (R2 = 0.52, NRMSE = ±23.00 %, NMAE = ±25.00 %, NSE = 0.51). The basal crop coefficient (Kcb) showed small seasonal fluctuations, varying between 0.40 and 0.60 throughout the year. In contrast, Kc showed clear seasonality varying between 0.59–0.64 at flowering and fruit set and rising to 0.81–0.90 during the fruit growing phase. Estimates of the annual total transpiration (686 mm) from the FAO method were within 5 % of the actual measured values (677 mm) while the dual source model estimated 612 mm of transpiration, which was almost 10 % lower than the measured values. The results highlight that even though the FAO method requires fewer input parameters, it is potentially more accurate in estimating water use in mango orchards than the dual-source model.