Bat foraging strategies and pollination of Madhuca latifolia (Sapotaceae) in southern India

The sympatrically occurring Indian short-nosed fruit bat Cynopterus sphinx and Indian flying fox Pteropus giganteus visit Madhuca latifolia (Sapotaceae), which offers fleshy corollas (approximate to 300 mg) to pollinating bats. The flowers are white, tiny and in dense fascicles The foraging activities of the two bat species were segregated in space and time. Cynopterus sphinx fed on resources at lower heights in the trees than P giganteus and its peak foraging activity occurred at 19 30 h, before that of P giganteus Foraging activities involved short searching flights followed by landing and removal of the corolla by mouth Cynopterus sphinx detached single corollas from fascicles and carried them to nearby feeding roosts, where it sucked the juice and spat out the Fibrous remains Pteropus giganteus landed on top of the trees and fed on the corollas in situ, its peak activity occurred at 20 30 11 This species glided and crawled between the branches and held the branches with claws and forearms when removing fleshy corollas with Its Mouth Both C sphinx and P giganteus consumed fleshy corollas with attached stamens and left the gynoecium intact Bagging experiments showed that fruit-set in bat-visited flowers was significantly higher (P < 0.001) than in self-pollinated flowers.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Context-dependency of a complex fruit-frugivore mutualism: temporal variation in crop size and neighborhood effects

The quantity of fruit consumed by dispersers is highly variable among individuals within plant populations. The outcome Of Such selection operated by firugivores has been examined mostly with respect to changing spatial contexts. The influence of varying temporal contexts on frugivore choice, and their possible demographic and evolutionary consequences is poorly understood. We examined if temporal variation in fruit availability across a hierarchy of nested temporal levels (interannual, intraseasonal, 120 h, 24 h) altered frugivore choice for a complex seed dispersal system in dry tropical forests of southern India. The interactions between Phyllanthus emblica and its primary disperser (ruminants) was mediated by another frugivore (a primate),which made large quantities of fruit available on the ground to ruminants. The direction and strength of crop size and neighborhood effects on this interaction varied with changing temporal contexts.Fruit availability was higher in the first of the two study years, and at the start of the season in both years. Fruit persistence on trees,determined by primate foraging, was influenced by crop size andconspecific neighborhood densities only in the high fruit availability year. Fruit removal by ruminants was influenced by crop size in both years and neighborhood densities only in the high availability year. In both years, these effects were stronger at the start of the season.Intraseasonal reduction in fruit availability diminished inequalities in fruit removal by ruminants and the influence of crop size and fruiting neighborhoods. All trees were not equally attractive to frugivores in a P. emblica population at all points of time. Temporal asymmetry in frugivore-mediated selection could reduce potential for co-evolution between firugivores and plants by diluting selective pressures. Inter-dependencies; formed between disparate animal consumers can add additional levels of complexity to plant-frugivore mutualistic networks and have potential reproductive consequences for specific individuals within populations.

Mehiläisten (Apis mellifera) kukkavierailut mansikalla (Fragaria x ananassa) ja vadelmalla (Rubus idaeus) sekä kukkavierailuihin vaikuttavat tekijät

Hyönteispölytys lisää monien ristipölytteisten viljelykasvien siemensatoa sekä parantaa sadon laatua. Marjakasveilla, kuten mansikalla ja vadelmalla marjojen koko suurenee sekä niiden laatu paranee onnistuneen pölytyksen seurauksena. Aiempien havaintojen mukaan mansikan kukat eivät pääsääntöisesti houkuttele mehiläisiä, kun taas vadelma on yksi mehiläisten pääsatokasveista. Tutkimuksen tarkoituksena oli selvittää, miten tehokkaasti mehiläiset vierailevat mansikalla sekä vadelmalla, keskittyen kukkakohtaisiin käynteihin tuntia kohti. Mehiläisiä voidaan käyttää Gliocladium catenulatum-vektoreina torjuttaessa mansikan ja vadelman harmaahometta (Botrytis cinerea). Kukkavierailujen perusteella arvioidaan, onko vektorilevitys riittävän tehokas torjumaan harmaahometta ja miten hyvin mehiläisiä voidaan käyttää pölytyspalveluihin, etenkin mansikalla. Havainnot kerättiin kuudelta eri tilalta Sisä-Savosta kesällä 2007. Kukkavierailuja laskettiin mansikan ja vadelman kukinnan aikana erilaisissa sääolosuhteissa, eri kellonaikoina ja eri etäisyyksillä mehiläispesistä. Kukat valittiin satunnaisesti, ja valintaperusteena oli kukan avonaisuus. Tarkkailuaika riippui mehiläisten lentoaktiivisuudesta. Mansikan koko havaintojakson keskiarvoksi tuli 1,75 käyntiä kukkaa kohti tunnissa. Vadelmalla vastaava luku oli 4,27, joten keskiarvojen perusteella vadelma oli houkuttelevampi kuin mansikka. Kasvukauden vaiheella ei ollut eroja vierailuihin kummallakaan kasvilla, mutta vuorokaudenajan suhteen vierailuja oli enemmän aamupäivällä kuin iltapäivällä. Lämpötila korreloi positiivisesti vierailutiheyden kanssa kummallakin kasvilla. Sääolosuhteet rajoittivat havaintojen keräämistä ja kesä oli erittäin sateinen. Mehiläiset vierailivat kukissa riittävästi haastavissakin sääolosuhteissa niin, että harmaahometorjunta onnistui. Vektorilevitystä suunnitellessa, etenkin mansikalla, tulee ottaa huomioon pesien sijoittelu sekä riittävä lukumäärä. Pesien ravinnontarpeen tulee olla suuri, jotta mehiläiset keräisivät ravintoa kukista mahdollisimman tehokkaasti. Pesiin voidaan lisätä tarvittaessa avosikiöitä tai poistaa siitepölyvarastoja ravinnonkeruuaktiivisuuden lisäämiseksi. Lisätutkimusta tarvitaan pesien sijoittelun, kilpailevien kasvien sekä mansikkalajikkeiden houkuttelevuuden vaikutuksesta vierailutiheyteen. Suomalaisten mansikkalajikkeiden meden sekä siitepölyneritystä olisi myös hyvä selvittää.

Testing the efficacy of a chilli-tobacco rope fence as a deterrent against crop-raiding elephants

Chilli-based repellents have shown promise as deterrents against crop-raiding elephants in Africa. We experimented with ropes coated with chilli-based repellent as a cheap alternative to existing elephant cropraid deterrent methods in India. Three locations (Buxa Tiger Reserve, Wyanad Wildlife Sanctuary and Hosur Forest Division) representing varying rainfall regimes from high to low, and with histories of intense elephant-agriculture conflict, were selected for the experiments that were conducted over 2-3 months during the pre-harvest period of the kharif season in late 2006. Chilli and tobacco powder mixed with waste oil was applied to ropes strung around agricultural fields of 1.4-5.5 km perimeter and elephant approaches were monitored. Elephants breached the rope fences a few times at all three study sites. Female-led herds were far more deterred (practically 100% reduction) than were solitary males (c. 50%) by the chilli-tobacco rope. Efficacy of this method as a deterrent was significantly better in the low-rainfall regime relative to medium and high-rainfall regimes. The initial promising results present a case for more rigorous experimentation; these would help determine if the elephants avoiding the rope are responding physiologically to the chilli-tobacco smell or merely reacting cautiously to a novel substance in their environment.

Ecology of the Asian Elephant in Southern India. II. Feeding Habits and Crop Raiding Patterns

The Asian elephant's foraging strategy in its natural habitat and in cultivation was studied in southern India during 1981-83. Though elephants consumed at least 112 plant species in the study area, about 85% of their diet consisted of only 25 species from the order Malvales and the families Leguminosae, Palmae, Cyperaceae and Gramineae. Alteration between a predominantly browse diet during the dry season with a grass diet during the early wet season was related to the seasonally changing protein content of grasses. Crop raiding, which was sporadic during the dry season, gradually increased with more area being cultivated with the onset of rains. Raiding frequency reached a peak during October-December, with some villages being raided almost every night, when finger millet (Eleusine coracana) was cultivated by most farmers. The monthly frequency of raiding was related to the seasonal movement of elephant herds and to the size of the enclave. Of their total annual food requirement, adult bull elephants derived an estimated 9.3% and family herds 1.7% in quantity from cultivated land. Cultivated cereal and millet crops provided significantly more protein, calcium and sodium than the wild grasses. Ultimately, crop raiding can be thought of as an extension of the elephant's optimal foraging strategy.

Optimal crop planning and conjunctive use of water resources in a coastal river basin

Due to increasing trend of intensive rice cultivation in a coastal river basin, crop planning and groundwater management are imperative for the sustainable agriculture. For effective management, two models have been developed viz. groundwater balance model and optimum cropping and groundwater management model to determine optimum cropping pattern and groundwater allocation from private and government tubewells according to different soil types (saline and non-saline), type of agriculture (rainfed and irrigated) and seasons (monsoon and winter). A groundwater balance model has been developed considering mass balance approach. The components of the groundwater balance considered are recharge from rainfall, irrigated rice and non-rice fields, base flow from rivers and seepage flow from surface drains. In the second phase, a linear programming optimization model is developed for optimal cropping and groundwater management for maximizing the economic returns. The models developed were applied to a portion of coastal river basin in Orissa State, India and optimal cropping pattern for various scenarios of river flow and groundwater availability was obtained.

Climate change and crop yields in the Indian Cardamom Hills, 1978-2007 CE

In this study we analyzed climate and crop yields data from Indian cardamom hills for the period 1978-2007 to investigate whether there were significant changes in weather elements, and if such changes have had significant impact on the production of spices and plantation crops. Spatial and temporal variations in air temperatures (maximum and minimum), rainfall and relative humidity are evident across stations. The mean air temperature increased significantly during the last 30 years; the greatest increase and the largest significant upward trend was observed in the daily temperature. The highest increase in minimum temperature was registered for June (0.37A degrees C/18 years) at the Myladumpara station. December and January showed greater warming across the stations. Rainfall during the main monsoon months (June-September) showed a downward trend. Relative humidity showed increasing and decreasing trends, respectively, at the cardamom and tea growing tracts. The warming trend coupled with frequent wet and dry spells during the summer is likely to have a favorable effect on insect pests and disease causing organisms thereby pesticide consumption can go up both during excess rainfall and drought years. The incidence of many minor pest insects and disease pathogens has increased in the recent years of our study along with warming. Significant and slight increases in the yield of small cardamom (Elettaria cardamomum M.) and coffee (Coffea arabica), respectively, were noticed in the recent years.; however the improvement of yield in tea (Thea sinensis) and black pepper (Piper nigrum L.) has not been seen in our analysis.

Parameter estimation of a two-horizon soil profile by combining crop canopy and surface soil moisture observations using GLUE

Estimation of soil parameters by inverse modeling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise when heterogeneous layered soils are considered. The objective of this study was to explore the potential of combining observations on surface soil moisture and crop variables - leaf area index (LAI) and above-ground biomass for estimating soil parameters (water holding capacity and soil depth) in a two-layered soil system using inversion of the crop model STICS. This was performed using GLUE method on a synthetic data set on varying soil types and on a data set from a field experiment carried out in two maize plots in South India. The main results were (i) combination of surface soil moisture and above-ground biomass provided consistently good estimates with small uncertainity of soil properties for the two soil layers, for a wide range of soil paramater values, both in the synthetic and the field experiment, (ii) above-ground biomass was found to give relatively better estimates and lower uncertainty than LAI when combined with surface soil moisture, especially for estimation of soil depth, (iii) surface soil moisture data, either alone or combined with crop variables, provided a very good estimate of the water holding capacity of the upper soil layer with very small uncertainty whereas using the surface soil moisture alone gave very poor estimates of the soil properties of the deeper layer, and (iv) using crop variables alone (else above-ground biomass or LAI) provided reasonable estimates of the deeper layer properties depending on the soil type but provided poor estimates of the first layer properties. The robustness of combining observations of the surface soil moisture and the above-ground biomass for estimating two layer soil properties, which was demonstrated using both synthetic and field experiments in this study, needs now to be tested for a broader range of climatic conditions and crop types, to assess its potential for spatial applications. (C) 2012 Elsevier B.V. All rights reserved.

Hierarchical artificial immune system for crop stage classification

This paper presents a new hierarchical clustering algorithm for crop stage classification using hyperspectral satellite image. Amongst the multiple benefits and uses of remote sensing, one of the important application is to solve the problem of crop stage classification. Modern commercial imaging satellites, owing to their large volume of satellite imagery, offer greater opportunities for automated image analysis. Hence, we propose a unsupervised algorithm namely Hierarchical Artificial Immune System (HAIS) of two steps: splitting the cluster centers and merging them. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The classification results have been compared with K-means and Artificial Immune System algorithms. From the results obtained, we conclude that the proposed hierarchical clustering algorithm is accurate.

Crop stage classification of hyperspectral data using unsupervised techniques

The presence of a large number of spectral bands in the hyperspectral images increases the capability to distinguish between various physical structures. However, they suffer from the high dimensionality of the data. Hence, the processing of hyperspectral images is applied in two stages: dimensionality reduction and unsupervised classification techniques. The high dimensionality of the data has been reduced with the help of Principal Component Analysis (PCA). The selected dimensions are classified using Niche Hierarchical Artificial Immune System (NHAIS). The NHAIS combines the splitting method to search for the optimal cluster centers using niching procedure and the merging method is used to group the data points based on majority voting. Results are presented for two hyperspectral images namely EO-1 Hyperion image and Indian pines image. A performance comparison of this proposed hierarchical clustering algorithm with the earlier three unsupervised algorithms is presented. From the results obtained, we deduce that the NHAIS is efficient.

Gene expression programming-fuzzy logic method for crop type classification

Crop type classification using remote sensing data plays a vital role in planning cultivation activities and for optimal usage of the available fertile land. Thus a reliable and precise classification of agricultural crops can help improve agricultural productivity. Hence in this paper a gene expression programming based fuzzy logic approach for multiclass crop classification using Multispectral satellite image is proposed. The purpose of this work is to utilize the optimization capabilities of GEP for tuning the fuzzy membership functions. The capabilities of GEP as a classifier is also studied. The proposed method is compared to Bayesian and Maximum likelihood classifier in terms of performance evaluation. From the results we can conclude that the proposed method is effective for classification.