Month: May 2019

The Biochar challenge in viticulture; enhance grape production and mitigate the effect of climate change in Mediterranean area

By Silvia Baronti, Francesco Primo Vaccari, Lorenzo Genesio, Anita Maienza

Vineyards management is aimed to the maximization of grape yields and quality, includes crop residues removal, strict weed and pest control treatments. These conventional practices have a strong environmental impact over long time periods and also on the fundamental soil physio-chemical and biological parameters (texture, pH, soil organic matter, biodiversity). In the climate change chalange, the choice of appropriate soil strategies can prevent loss of soil fertility and contribute to create a sustainable vineyard management on long term.
The use of biochar is a potential practice that allows restoration of degraded soils together with a significant contribute to mitigation of effect of climate change in agriculture. Biochar is a carbon rich material obtained by the pyrolysis/gasification of biomass and is a promising negative emission technology. Recently, the adoption of a biochar-based strategy management was encouraged by the special report of Intergovernmental Panel on Climate Change in 2018 to contribute to climate change adaptation and mitigation.
Vineyard managers’ interest in biochar has increased because of its potential to improve soil water-holding capacity to aid grape production during drought. Moreover vineyard biomass from vine removal and pruned wood can be converted to biochar in a circular economy perspective.

The Biochar Lab group of the Institute of Biometeorology of Florence (CNR IBIMET) starts in 2007 some field experiments to verify if the biochar could be a viable option also for the agriculture.
Ten years ago, the Biochar Lab of IBIMET started the first field trial on a vineyard of Marchesi Antinori – La Braccesca Estate, close to Montepulciano, Tuscany, Central. The Merlot vineyard was planted in 1995 and on 2009 was amended with 22t/ha of Biochar and with other 22t/ha on 2010. The trial is still in progress and also after 10 years the biochar effect on soil it can see directly (Figure 1).



Figure 1. Biochar amendment on vineyard in Central Italy.



Table 1. Grape yield per plant during 4 years of experiment in control plot (C) biochar 22t/ha plot (B) and biochar 44t/ha plot (BB). Different letters denote significant differences data by Genesio et al., 2015 .



The results collected during these years are exciting: we published an increase in grape production (up to 66%) of the plots treated with biochar without a decrease of the grape quality (Genesio et al. 2015) (Table 1), an increase in plant-soil water relations (Baronti et al 2014) (Figure 2) no concentrations of soil PAHs (Rombolà et al., 2019; Rombolà et al., 2015) and no eco-toxicity effect (Maienza et al., 2017) over long term.
In the latest paper, published on the journal Geoderma, were showed the long term effect on soil quality, demonstrating how the biochar incorporation to soil, not only has potential contribute to carbon sequestration, but also has a positive effect on soil chemical and biological parameters, even many years from a single application (Giagnoni et al., 2019).


Figure 2. Increase of soil water content during day of the years (DOY) in control plot (C) biochar 22t/ha (B) and biochar 44t/ha (BB). Different letters denote significant differences. Data from Baronti et al., 2014.



Figure 3. PHAs content in soil during the experiment in control plot (C) biochar 22t/ha (B) and biochar 44t/ha (BB). Different letters denote significant differences. Data from Rombolà et al., 2018.



These results encouraging other vineyard managers’ to adopt the biochar strategy with the start of other field long experiments this year in Tuscany:

  • Poggio Torselli Estate, San Casciano Val di Pesa (Firenze)
  • Experimental Estate of the Tuscany Region Cesa (Arezzo) at planting phase (Figure 4).


Figure 4. Experiment start at CESA. 2019



The Italian research on the effect of biochar in viticulture is now in the position to provide an effective contribution in mitigating climate change and we hope that this will be an example for other Mediterranean countries.

Biochar could be more extensively used in the future if it were produced more efficiently and in greater volume to make it more affordable, and/or if regulators mandate requirements for carbon sequestration and greenhouse gas (GHG) emissions for agriculture, or increase requirements related to air pollution and nitrogen management.


Silvia Baronti

Research interest focus on plant physiology and ecophysiology, investigating the physiological and biochemical parameters in the correlation between plant resistance and tolerance to biotic and abiotic factors. Relevant experience in environmental physiology, carbon cycle research and Climate Change Mitigation in the agricultural sector. Experience in ecophysiological studies and the measurement of N2O and CO2 fluxes from soils, on the effects of the increasing CO2 in natural and cultivated plants, on modelling and monitoring fluxes and biomass /Net Primary Productivity measurement in agroecosystems using the eddy covariance techniques.

Francesco Primo Vaccari

PhD in Ecology and Environmental Systems, (2010).
Permanent Research Scientist at Institute of Biometeorology - National Research Council (IBIMET CNR) (since (2002- present). Research Interests : Plant Ecology; Climate Change Impact & Mitigation; Effects of high CO2 on crops and natural ecosystems; Biosphere atmosphere interactions; Biochar.

Lorenzo Genesio

Senior Researcher at Institute for Biometeorology of CNR, Florence his Scientific interests and highlights concerns Biosphere-atmosphere interactions and carbon balance; Agronomic research on biochar and feedbacks on the earth system; Early warning systems for food security;Remote-sensing and micrometeorological applications for precision agriculture and precision viticulture; Ecophysiological studies on temperate and sub-tropical agro-ecosystems. Desertification sensitivity assessment.

Anita Maienza

Biologist, PhD in Forestry Ecology. I'm leading experience on the propose of soil practices that, preserve biodiversity, increase soil carbon content and mitigate the effect of climate change, first of all the study of biochar in soils.


Baronti S, Vaccari FP, Miglietta F, Calzolari C, Lugato E, Orlandini S, Pini R, Zulian C, Genesio L (2014) Impact of biochar application on plant water relations in Vitis vinifera (L.) Eur J Agron 53:38–44.

Genesio L, Miglietta F, Baronti S, Vaccari FP (2015) Biochar increases vineyard productivity without affecting grape quality: results from a four years field experiment in Tuscany. Agric Ecosys Environ 201: 20–25.

Giagnoni, L., Maienza, A., Baronti, S., Vaccari, F. P., Genesio, L., Taiti, C., ... & Mancuso, S. (2019). Long-term soil biological fertility, volatile organic compounds and chemical properties in a vineyard soil after biochar amendment. Geoderma, 344, 127-136.

Maienza, A., Baronti, S., Cincinelli, A., Martellini, T., Grisolia, A., Miglietta, F., ... & Genesio, L. (2017). Biochar improves the fertility of a Mediterranean vineyard without toxic impact on the microbial community. Agronomy for Sustainable Development, 37(5), 47.

Rombolà, A. G., Meredith, W., Snape, C. E., Baronti, S., Genesio, L., Vaccari, F. P., ... & Fabbri, D. (2015). Fate of soil organic carbon and polycyclic aromatic hydrocarbons in a vineyard soil treated with biochar. Environmental science & technology, 49(18), 11037-11044.

Posted by in Viticulture

Aromatic evolution of blanc de noirs sparkling wines made by traditional method during twelve months of aging on their lees

By Cristina Úbeda and Mariona Gil i Cortiella

The production of sparkling wine following the traditional method (also named Champenoise) involves a winemaking of the grapes to obtain a base wine, and afterwards a second fermentation inside the bottle, followed by a period of contact on their lees during several months.
Thus, one of the main factors affecting the volatile composition of sparkling wines is the grape variety employed to elaborate the base wine. The most employed grape varieties to produce sparkling wine are white and have been well-studied (Ibern-Gómez et al., 2000; Francioli et al., 2003; Bosch-Fusté et al., 2007; Gallardo-Chacón et al., 2010; Ganss et al., 2011; Kemp et al., 2015), however, less is known about sparkling wine produced from red grape varieties. The grape variety used for this study corresponds to País cv. (Vitis vinifera), which is the most ancient vinifera grown in South America (since the mid-16th-century), originates from Tenerife, Spain (where is still cultivated and named Listán Negro). Despite that cultivation of this grape has been gradually decreasing since the 19th-century because of the introduction of French varieties (Lacoste et al. 2010), it is nowadays the second most grown red variety in Chile (12.520 ha), and currently it is used for sparkling wine production in Chile showing a great potential for the elaboration of this kind of wines.
Along the ageing period, a process of yeasts autolysis takes place, influencing the characteristics of the wine and its sensorial quality (Alexandre & Guilloux-Benatier, 2006; Martínez-Rodríguez et al., 2001), being the aroma one of the most affected features in this process. During autolysis, several volatile compounds are released to the wine, as well as some enzymes interact with aromatic precursors releasing their corresponding volatile molecules to the wine. Moreover, some of the volatile compounds might be adsorbed on lees, decreasing their concentration in the sparkling wines during aging (Comuzzo et al., 2006; Ganss et al., 2011). Therefore, the aging time determines the type and the quantity of volatile compounds present in sparkling wines (Riu-Aumatell et al., 2006).
In this context we planned to study the impact aroma compounds during the production of sparkling wine from grape juice (since base wine was made by white winemaking, obtaining a blanc de noirs wine), to obtain a base wine that undergoes a second fermentation into the bottle employing a Saccharomyces cerevisiae (ex r.f. bayanus) yeast for the second fermentation, obtaining a sparkling wine that going through aging on their lees. These results could be found in a recent published article (Ubeda et al., 2018).
Hence, the evolution of aromatic compounds from grape juice to sparkling wine with 12 months of ageing on their lees was monitored by gas chromatography coupled to mass spectrometry (GC/MS). Besides, the impact aroma compounds were evaluated during the second fermentation and ageing (it is from base wine to 12 months of ageing) by carrying out olfactometry techniques (GC/MS/O) and a sensory analysis with a trained panel. The combination of different analyses allowed having a fairly broad view of the changes that take place in the sparkling wine aromatic features during its production (Figure 1).


Figure 1. Scheme of the production of sparkling wines and scientific approach.



As can be observed in Figure 2, there was an important loss of esters during the second fermentation and throughout ageing, mainly due to the decrease of β-phenethyl acetate and isoamyl acetate. This loss might be due to adsorption onto lees, but also due to the chemical hydrolysis due to their thermodinamical unstability. Despite of this, it was observed that diethyl succinate, ethyl lactate, and methyl-2-oxo-nonanoate increased during aging and could be proper aging markers.
Several compounds increased their concentration during aging (e.g., norisoprenoids). These compounds appear glycosylated in the grape and are released by yeast enzymes during the fermentation or from these non-volatile precursors by hydrolysis under acidic conditions at wine pH (Williams et al., 1982; Riu-Aumatell et al., 2006). Based on our findings, we propose that, in the case of young sparkling wines (12 months in contact with lees), vitispiranes might be better aging markers than the typically used TDN. However, terpenes which also appear glycosidated in the grape, increase after alcoholic fermentation but decrease during aging on lees (Figure 2).


Figure 2. Evolution of the different chemical families of volatile compounds during the production of sparkling wine. M (Must); BW (Base wine); 0M, 3M, 6M, 9M, 12M (Sparkling wine of different months in contact with lees).Scheme of the production of sparkling wines and scientific approach.



Figure 3. The contribution of each aroma category as percentage of the number of odor active compounds in Base wine (BW), sparkling wine of 0 (0M) and 12 months in contact with lees (12M).



Olfactometry analyses exposed that 48, 47 and 38 odor-active zones were detected in the BW, 0M, 12M samples, respectively. Among these, 23 were perceived in all the stages of the production process. Figure 3 shows the contribution of each aroma category as a percentage of the total modified frequency (MF) of odor zones. In contrast to the GC-MS analysis results, the zones described as fruity aromas were similar when comparing the BW (39% of the total MF) and sparkling wine after the second fermentation (40%). However, a decrease of the total MF was observed after the 12 months of contact with lees (36%), supporting the loss of fruity nuances after aging, which is linked to the decrease of esters. Joint to the loss of fruity character, there was a decrease in the floral odor zones, however, these were not perceived in the sensorial trials (Figures 4). This suggests that the responsibility for fruity and floral nuances in sparkling wine resides in a few high-impact aromatic, such as ethyl isobutyrate, isoamyl acetate, ethyl hexanoate, and β-phenylethanol.
Finally, multivariate analysis performed with all the volatile compounds determined showing that Component 1 (PC1) seems to explain the variance among wines with different aging time. This pointed to diethyl succinate, ethyl lactate, and vitispirane as the best aging markers for this young sparkling wine since their loadings in PC1 were the greatest among all the volatile compounds, being in agreement with the results obtained by chemical analyses (Figure 5).

Those interested in a longer length report can download the working paper at:


Figure 4. Spider graphics of the sensory descriptive analysis of the evaluated attributes.



Figure 5. Data scores and loadings (absolute eigenvalue ˃0.800) biplot on the plane made up of the first two principal components (PC1 against PC2).



Dr. Cristina Úbeda Aguilera is an assistant professor at the Universidad Autónoma de Chile, located in Santiago. She has graduate degree in Pharmacy and master’s degrees in Microbiology applied to industrial biotechnology, Nutrition and Dietetics and Food Safety. During her PhD, carried out at the University of Seville (Spain) (2012), she acquired knowledge in techniques for extracting bioactive and volatile compounds and also, performing sensory analysis from different food matrices, mainly working with fermented beverages and seasonings. She has developed her research activity in University of Seville and Zaragoza (Spain), IFAPA Córdoba, CSIC (Spain), University of Lisbon (Portugal), University of Chile (Chile). Currently, her research is focused into two main lines: the study of certain technologies to improve Chilean sparkling wine quality and the search of new yeast strains of interest for the food industry.

Dr. Mariona Gil i Cortiella is currently assistant professor in the Universidad Autónoma de Chile (Santiago de Chile, Chile), performing her research related with wine chemistry and wine technology in the Instituto de Ciencias Químicas Aplicadas, Inorganic Chemistry and Molecular Materials Center (ICQA), in collaboration with the Grupo de Investigación Enológica (GIE) of the Department of Enology and Agroindustry (University of Chile). She trained in the Universitat Rovira i Virgili (Tarragona, Spain), where she obtained a Bachelor of Science in Chemistry (2010), and a Bachelor of Science in Enology (2008), besides a Master of Science in Enology (2010), and a PhD in Enology and Biotechnology (2013). Her topics of research nowadays are related with the effect of the tanks used during winemaking on the chemical composition and sensory properties of Sauvignon blanc wines, and the characterization of cold and warm climate Syrah cv. wines from South America.


Alexandre, H., & Guilloux-Benatier, M. (2006). Yeast autolysis in sparkling wine–a review. Australian Journal of Grape and Wine Research, 12, 119-127.

Bosch-Fusté, J., Riu-Aumatell, M., Guadayol, J. M., Caixach, J., López-Tamames, E., & Buxaderas, S. (2007). Volatile profiles of sparkling wines obtained by three extraction methods and gas chromatography–mass spectrometry (GC–MS) analysis. Food Chemistry105, 428-435.

Comuzzo, P., Tat, L., Tonizzo, A., & Battistutta, F. (2006). Yeast derivatives (extracts and autolysates) in winemaking: Release of volatile compounds and effects on wine aroma volatility. Food Chemistry, 99, 217-230.

Francioli, S., Torrens, J., Riu-Aumatell, M., López-Tamames, E., & Buxaderas, S. (2003). Volatile compounds by SPME-GC as age markers of sparkling wines. American Journal of Enology and Viticulture, 54, 158-162

Gallardo-Chacón, J., Vichi, S., López-Tamames, E., & Buxaderas, S. (2009). Analysis of sparkling wine lees surface volatiles by optimized headspace solid-phase microextraction. Journal of Agricultural and Food Chemistry, 57, 3279-3285.

Ganss, S., Kirsch, F., Winterhalter, P., Fischer, U., & Schmarr, H. G. (2011). Aroma changes due to second fermentation and glycosylated precursors in Chardonnay and Riesling sparkling wines. Journal of Agricultural and Food Chemistry, 59, 2524-2533.

Ibern-Gómez, M., Andrés-Lacueva, C., Lamuela-Raventós, R. M., Buxaderas, S., Singleton, V. L., & De La Torre-Boronat, M. C. (2000). Browning of cava (sparkling wine) during aging in contact with lees due to the phenolic composition. American Journal of Enology and Viticulture, 51, 29-36.

Kemp, B., Alexandre, H., Robillard, B., & Marchal, R. (2015). Effect of production phase on bottle-fermented sparkling wine quality. Journal of Agricultural and Food Chemistry, 63, 19-38.

Lacoste, P., Yuri, J. A., Aranda, M., Castro, A., Quinteros, K., Solar, M. et al. (2010). Variedades de uva en Chile y Argentina (1550-1850): genealogía del torrontés. Mundo Agrario, 10, 7-8.

Martínez-Rodríguez, A. J., Polo, M. C., & Carrascosa, A. V. (2001). Structural and ultrastructural changes in yeast cells during autolysis in a model wine system and in sparkling wines. International Journal of Food Microbiology, 71, 45-51

OIV. The International Organisation of Vine and Wine. (2017). 2017 World Vitiviniculture Situation OIV Statistical Report on World Vitiviniculture.

Ubeda, C., Kania, I., Del Barrio-Galán, R., Medel, M., Gil, M., Peña-Neira, A. (2019). Study of the changes in volatile compounds, aroma and sensory attributes during the production process of sparkling wine by traditional method. Food Research International. doi:10.1016/j.foodres.2018.10.032

Williams, P. J., Strauss, C. R., Wilson, B., & Massy-Westropp, R. A. (1982). Studies on the hydrolysis of Vitis vinifera monoterpene precursor compounds and model monoterpene. beta.-D glucosides rationalizing the monoterpene composition of grapes. Journal of Agricultural and Food Chemistry, 30, 1219-1223.

Posted by in Chemistry

Psychological warfare in vineyards: Using drones and bird psychology to control bird damages to wine grapes

By Zihao Wang, Andrea S. Griffin, Andrew Lucas, KC Wong

Bird damage to commercial corps is a significant and long-standing global problem, and wine grapes are one the most vulnerable. In some regions of Australia, bird damage to a single vineyard can be as high as 83% (Tracey & Saunders, 2003). There are many methods developed to control bird damage in vineyards. However, there are not methods that are both efficient and cost-effective.
One of the emerging approaches to the problem is to use Unmanned Aerial Vehicles (UAVs), more commonly known as drones. As the autonomous technologies continuously improve, drones become much easier to operate. Many researchers and grape growers have attempted to use the drones to scare birds away from vineyards (Wang et al., 2017). However, the drones suffer the same limitation as other scaring methods. The birds eventually habituate to the drones after a period of time, as they have learned the drones possess no real threat. To make drones truly effective predators, we need to incorporate considerations of bird psychology into the design.
Decades of research in bird psychology suggest that birds learn about new predators by listening to the distress calls of their conspecifics (Conover & Perito, 1981; Griffin, 2008). Distress call is typically produced by a bird that has been captured by a predator. When birds experience new threats together with distress calls, they learn to associated the new threats with danger (Griffin, 2004). Furthermore, a large body of research indicates that the effectiveness of such learning can be increased in amplitude and duration by adding fear-inducing stimuli to the calls themselves (Griffin et al., 2010).
In Wang et al., 2019, we took this theory to the test by equipping a hexacopter multirotor drone with a piezo horn tweeter and a crow taxidermy, as shown in figure 1, and test the response of the pest birds to the multirotor in different vineyards. The horn tweeter and the crow taxidermy were responsible for generating the perceived predation risk. The horn tweeter broadcasted bird distress calls. The crow taxidermy was installed upside down, wings open, in a vertical pose on the undercarriage of the drone. The intention of this pose was to strike the impression that the UAV has just caught the crow, and the distress call was coming from the crow in apparent danger.


Figure 1. UAV equipped with horn tweeter and crow taxidermy.



The drone was tested across 4 different vineyards in 2018 in New South Wales, Australia. The main avian pest species encountered during the experiments were the Australian Raven (Corvus coronoides), Common Starling (Sturnus vulgaris), Sulphur-crested Cockatoo (Cacatua galerita) and Silvereye (Zosterops lateralis). The drone was flown manually to deter pest birds found in the vineyards. For flocking birds (ravens, starlings and cockatoos in the experiments), we observed and recorded the distance at which birds fled from the drone, and the time taken for the birds to return to their initial position. For the silvereyes, because of their very different behaviours, we counted the bird activities 15 min before and 15 min after drone flights as a measure of effectiveness.
In 9 successful trials targeted at flocking birds, all birds fled from the drone with no birds left behind (Table 1). On average, the birds responded to the drone when it was around 100 m away from them. The ravens did not leave the vineyard after the drone flights, they settled on average 450 m away from the position which they fled from. On the other hand, the cockatoo and starling flocks left the vineyard after the drone flight. Only the starling flock in trial 9 returned to the vineyard after 5 min, however they stayed perching on powerlines briefly before they flew away.


Table 1. Influence of UAV on large birds.


The silvereye activities recorded in the experiments can be seen in figure 2 and figure 3. A total of 3 experiments were performed. Figure 2 shows that the number of birds moving into and out of the vines after the drone flight dropped significantly compared to before the drone flight. The relative reduction of bird activity 15 min after the UAV flight were 66%, 95% and 42% (figure 3).


Figure 2. Influence of drone flights on silvereye activities.



Figure 3. Reduction in silvereye activities in 15 min after drone flight compared to 15 min before drone flight.



The results indicated strongly that the drone was an effective bird deterrent for the target pest birds. The drone had a minimum 50 m radius of influence on large birds. More importantly, this radius of influence has a moving centre, which effectively increased the radius of influence to the drone’s radius of action plus 50 m. In addition, 100% of the birds left the initial location after the drone flight during the 9 trials. Only one flock of starling returned but they did not return to forage in the vines. The effectiveness of drone against silvereyes is evident in the reduction of silvereye activities after the flights.
In conclusion, incorporating bird psychology into the design of a drone opens new avenues for the development of bird control methods. Drone can potentially eliminate bird damage, provided the drone can target the birds and take off as soon as the birds have arrived at the vineyard. Therefore, autonomous technologies are keys to make drones efficient bird management tools in the future. We are aiming to develop path planning algorithms and sensor technologies in the future to achieve this goal.

Those interested in a longer length report can download the working paper at:




Zihao Wang is a PhD candidate at The University of Sydney, researching Unmanned Aerial Vehicles (UAVs) in the School of Aerospace, Mechanical and Mechatronic Engineering. He graduated from The University of Sydney in 2014 with a Bachelor of Engineering (Honours Class 1). He is interested in autonomous systems and their applications. His current focus is to develop an autonomous UAV system for bird damage management in agriculture.

Dr Andrea Griffin is a zoologist and expert in Animal Cognition. She is interested in how animals adapt to changing environments via learning and evolution. She has developed methods for training endangered animals to recognise predators and has studied how invasive birds learn about predators, humans, places and traps. Her research makes important contributions to conservation and management of wild animal populations.

Andrew Lucas is the founder and Managing Director of AOS. AOS was established in Melbourne in 1997 as an offshoot from the Australian Artificial Intelligence Institute (affiliated with the AI Center at SRI International, Menlo Park, CA), AOS specialises in AI, autonomous and robotic systems, with major contracts for the RAAF, SASR (Special Air Services Regiment of the Australian Army), RAN, Australian Defence Department and UK Ministry of Defence.
AOS has developed its own technology base of AI software, focused on intelligent software agents, combining this with other AI technologies, such as machine learning and machine vision, and constraint programming, to address a wide range of applications.
Andrew holds a Ph.D. in Aeronautical Engineering from Cambridge University, United Kingdom and a Bachelor of Engineering (1st Hons) from the University of Melbourne, Australia. He has over forty years of experience in various engineering roles in aerospace & defence, management consulting, robotic systems, agricultural technology, artificial intelligence software, and telecommunications.
Andrew is a board member of the Australian Association of Unmanned Systems and is a member of the Royal Aeronautical Society’s Unmanned Air Systems Specialist Group.

Dr KC Wong is an Associate Professor at The University of Sydney, where he is the Deputy Head of School (Education), Director of Undergraduate Coursework, and the Director for Aeronautical Engineering within the School of Aerospace, Mechanical and Mechatronic Engineering. He is a pioneering UAS (Unmanned Aircraft Systems) researcher in Australia, having been working on multidisciplinary airframe design and flight testing since 1988. He leads a small UAS research team, and has international R&D collaborations. UAS designed and developed in his group have been utilised in several industry collaborative research projects. Dr Wong was the founding President of the industry-focussed Australian Association for Unmanned Systems (AAUS), and served in that role for seven years until 2015. He has a strong passion for enhancing skills for the next generation of aerospace engineers through his development of the Aeronautical Engineering curriculum with unique experiential learning opportunities.


Conover, M. R., & Perito, J. J. (1981). Response of Starlings to Distress Calls and Predator Models Holding Conspecific Prey. Zeitschrift Für Tierpsychologie, 57(2), 163–172.

Griffin, A. S. (2004). Social learning about predators: a review and prospectus. Animal Learning & Behavior, 32(1), 131–140.

Griffin, A. S. (2008). Social learning in Indian mynahs, Acridotheres tristis: the role of distress calls. Animal Behaviour, 75(1), 79–89.

Griffin, A. S., Boyce, H. M., & MacFarlane, G. R. (2010). Social learning about places: observers may need to detect both social alarm and its cause to learn. Animal Behaviour, 79(2), 459–465.

Tracey, J., & Saunders, G. (2003). Bird Damage to the Wine Grape Industry. New South Wales, Australia: Australian Government Bureau of Rural Sciences.

Wang, Zihao; Lucas, Andrew; Wong, KC and Charmitoff, G. (2017). Biomimetic design for pest bird control UAVs: A survey. In 17th Australian International Aerospace Congress : AIAC 2017. Melbourne, Vic.: Engineers Australia, Royal Aeronautical Society, 2017: 469-476. Availability: <;dn=739410642199086;res=IELENG> ISBN: 9781922107855. (pp. 26–28).

Wang, Z., Griffin, A. S., Lucas, A., & Wong, K. C. (2019). Psychological warfare in vineyard: Using drones and bird psychology to control bird damage to wine grapes. Crop Protection, 120.

Posted by in Viticulture