Researchers Develop Algorithm to Predict Harvest Potential from Climate Data

The publicly available algorithm was developed using 15 years of data from Italy to compare how combinations of climatic events impacted subsequent harvests.

Archanes, Crete, Greece
By Paolo DeAndreis
Jan. 3, 2024 18:01 UTC
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Archanes, Crete, Greece

Almost one hun­dred pro­duc­tion stake­hold­ers have down­loaded an algo­rithm that may pro­vide the abil­ity to fore­cast an olive grove’s behav­ior and pro­duc­tiv­ity.

The new tech­nol­ogy is based on a thor­ough analy­sis of sea­sonal weather pat­terns dur­ing the olive growth cycle over a long period in Italy.

By com­par­ing the rela­tion­ship between olive devel­op­ment and har­vests to the cli­mate impacts, researchers were able to iden­tify dozens of poten­tial cli­mate stres­sors and how they affect olive tree pro­duc­tiv­ity.

See Also:AI Tool for Olive Farmers Will Improve Yields, Reduce Costs, Researchers Say

Researchers believe this infor­ma­tion might sup­port national or regional admin­is­tra­tions, olive grow­ers, pro­duc­ers, and other inter­ested par­ties in pre­dict­ing how an upcom­ing sea­son may unfold and mak­ing any agro­nomic or busi­ness adjust­ments.

The new tech­nol­ogy results from a coor­di­nated project involv­ing sci­en­tists from the Italian National Research Council (CNR) and the Agency for New Technologies, Energy and Sustainable Development (ENEA) as well as American researchers from the University of California – Berkeley.

We are work­ing on under­stand­ing which [cli­matic] dri­vers can trig­ger unfa­vor­able con­di­tions and the asso­ci­ated prob­a­bil­ity of expe­ri­enc­ing detri­men­tal effects on olive pro­duc­tion,” Arianna Di Paola, a researcher at the Italian Institute for BioEconomy at the CNR, told Olive Oil Times.

Examples of trig­gers are con­di­tions which favor the spread of the olive fruit fly or high win­ter tem­per­a­tures which can alter the olive cycle and impact flow­er­ing and pol­li­na­tion,” she added.

The research ana­lyzed olive har­vests in 66 Italian provinces between 2006 and 2020 to iden­tify the stres­sors using a wide range of data. They were able to uncover how the worst olive har­vests came to be.

Understanding the ongo­ing sea­son­al­ity allows us to fore­see what we may expect in the near future,” Di Paola said.

These are not sea­sonal fore­casts, which are required to be reli­able and trans­lated into action­able infor­ma­tion to facil­i­tate the deci­sion-mak­ing process, a whole world of research in itself,” she added. They are short-term sce­nar­ios that might sup­port invest­ments, pre­ven­tive mea­sures, treat­ments or agro­nomic prac­tices.”

The research did not stop at iden­ti­fy­ing the dri­vers of unfa­vor­able con­di­tions.

While we can­not yet pre­dict the whole phe­no­log­i­cal cycle of the olive, as it is not pos­si­ble to pre­dict the veg­e­ta­tive onset in the sea­son on regional scales, what we can do is, using a cal­en­dar, sim­ply divide the life­cy­cle of the olive into two-month install­ments,” Di Paola said.

By ana­lyz­ing the vari­ables impact­ing olive pro­duc­tion through the years and aggre­gat­ing them every two months, researchers defined a list of the vari­ables and exam­ined how they inter­act over time.

The analy­sis pro­vides a short-term pre­ci­sion fore­cast, which researchers said is three times bet­ter than the analy­sis of a sin­gle vari­able.

For exam­ple, one thing is to say that we had a warmer win­ter, another is to say that fol­low­ing that warm win­ter, we also had a very wet sum­mer, fac­tors which can add up and fur­ther worsen the sce­nario,” Di Paola said.

Once the analy­sis was ready, the researchers looked at which sea­sonal cli­mate vari­ables were more often asso­ci­ated with extremely bad or high-yield sea­sons, dis­card­ing the mid­dle-range yields.

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This selec­tion aimed to iden­tify yields that, on a broad spa­tial scale, were most affected by cli­matic vari­abil­ity given the super­im­po­si­tion of other dri­vers.

In mid­dle-range sea­sons, the yields might depend on vari­ables such as deploy­ing spe­cific agro­nomic tech­niques by one grower com­pared to another, or to the time spent prun­ing the olives and many more vari­ables,” Di Paola said.

Therefore, researchers were more inter­ested in look­ing at both plen­ti­ful and scarce extreme sea­sons, as the asso­ci­ated con­di­tions had an impact inde­pen­dent of the actions of the sin­gle grower.

Most of us are used to focus­ing on sin­gle stress fac­tors, such as a freeze or heat­wave, but even if we should man­age to look at those sin­gle stress fac­tors cor­rectly, we would still not be able to asso­ciate them to a spe­cific phe­no­log­i­cal stage with­out proper field obser­va­tions or model sim­u­la­tions,” Di Paola said.

We tried to smooth out all of these effects to con­sider them together on a large scale and across whole sea­sons,” she added.

Interestingly, the researchers found a link between the cli­mate vari­ables iden­ti­fied by the algo­rithm and the olive fruit fly phe­nom­e­non.

The algo­rithm will not tell you why a spe­cific sce­nario is going to occur,” Di Paolo said. However, by apply­ing it, we note that the out­puts – worse years in terms of pro­duc­tiv­ity and emerg­ing cli­mate stres­sors – were plau­si­bly asso­ci­ated with olive fruit fly infes­ta­tions.”

What the algo­rithm tells us is some­thing like: should you have these array of con­di­tions, let’s say five dif­fer­ent vari­ables over a given time, then it is highly prob­a­ble that the olive yield will be excep­tion­ally low,” she added.

Once this warn­ing comes from the algo­rithm, an expert must look at the data to inter­pret it cor­rectly. Is it the olive fruit fly, or are there other fac­tors we should con­sider?” Di Paola noted.

We stan­dard­ized all the vari­ables to make them com­pa­ra­ble across time and space, and that allowed us to look at things from above,” she added. To make it clear, when the research says that a spe­cific trig­ger is a warmer period than aver­age, that was true across all the provinces in the coun­try.”

By explor­ing a wide range of the ter­ri­tory, the algo­rith­m’s gen­er­al­iza­tion increases, and bet­ter fore­casts for the whole sec­tor in the entire coun­try can be achieved.

This is a use­ful view of the whole sec­tor for all enti­ties inter­ested in look­ing at the full pic­ture,” Di Paola said.

The algo­rithm, which is pub­licly acces­si­ble and can be down­loaded and inte­grated into their sys­tems, might be help­ful not only for Italy but also for the olive sec­tor.

The method we applied can be exported to other coun­tries and sec­tors,” Di Paola con­cluded. Once fed with the needed data, the algo­rithm can eas­ily be adapted to make that kind of sea­sonal fore­cast.”



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