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Article

Effectiveness of Options for the Adaptation of Crop Farming to Climate Change in a Country of the European South

by
Elena Georgopoulou
1,*,
Nikos Gakis
2,
Dimitris Voloudakis
3,
Markos Daskalakis
3,
Yannis Sarafidis
1,
Dimitris P. Lalas
2 and
Sevastianos Mirasgedis
1
1
Institute for Environmental Research & Sustainable Development, National Observatory of Athens, I. Metaxa & Vas. Pavlou, GR-15236 Palea Penteli, Greece
2
FACE3TS S.A., Agiou Isidorou 1, GR-11471 Athens, Greece
3
RethinkAg S.P., Eirinis 81, Agia Paraskevi, GR-15341 Attiki, Greece
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(10), 1814; https://doi.org/10.3390/agriculture14101814
Submission received: 9 September 2024 / Revised: 30 September 2024 / Accepted: 11 October 2024 / Published: 15 October 2024
(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)
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Abstract

:
This study quantitatively evaluates the effectiveness of three main options for the adaptation of crop farming to climate change (i.e., shift of planting dates, increase/addition of irrigation, and resilient hybrids/cultivars) in Greece, a country in southern Europe. The potential effect of each option on the yields of several crops in all Greek regions is estimated for 2021–2040 and 2041–2060 and compared with those under the historical local climate of 1986–2005, by using agronomic and statistical regression models, and data from different climatic simulations and climate change scenarios. Our results reveal that all the adaptation options examined have the potential to significantly reduce crop yield losses occurring under no adaptation, particularly during 2021–2040 when for many regions and crops more than half of the losses can be compensated for. Notably, in some cases during this period, the measures examined resulted in crop yields that are higher than those under the historical climate. However, the effectiveness of the measures diminished significantly in 2041–2060 under very adverse climate change conditions, highlighting the dynamic nature of adaptation. Assessing the effectiveness of combined adaptation options and evaluating additional criteria (e.g., feasibility) represent essential areas for future research.

1. Introduction

In December 2023, more than 130 world leaders attending COP28 in Dubai, United Arab Emirates, signed a declaration on the need to adapt agriculture and food systems to climate change. This declaration includes five key goals to link to agriculture, food systems, and climate planning; some are directly related to climate change adaptation [1,2].
The Dubai Declaration on agriculture did not come out of the blue. During the last decade, global policies have emphasized the need for agriculture to adjust to climate change, resulting in worldwide actions and important political initiatives, particularly in the European Union (EU) [3]. A significant opportunity to address climate change concerns in the agricultural sector came up in 2015, with the simultaneous adoption of the 21st Climate Conference of the Parties (COP 21) decisions in Paris, the Sendai Framework for Natural Disaster Risk and Reduction, and the United Nations Sustainable Development Goals (SDGs). Agriculture is a central part of the 2030 Agenda for Sustainable Development [4], with several SDGs directly related to food, agriculture, and climate change. The United Nations has also published a guide for national policymakers [5], with several actions supporting adaptation to climate change, such as water protection and water scarcity management through policies and measures for irrigation efficiency and crop diversification.
The latest (2022) Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) concluded that in Europe [6], irrigation and changing sowing dates are effective measures for protecting crops against heat stress and drought. However, it also emphasized that unwisely incentivizing and developing irrigation expansion actions in the Mediterranean can lead to unsustainable water use [7].
More recent studies also highlighted the importance of more efficient (hence water-saving) irrigation systems and irrigation water management measures (e.g., deficit irrigation) to address climate change concerns [8,9,10]. On-field experiments at cotton cultivations in Greece showed that an optimized 25% reduction in irrigation water had no effect on yields [11]. Quantitative assessments at the local scale have also shown that less water-demanding crops can be very effective in increasing resilience to climate change [9].
Farmers in major agricultural areas of Europe, such as the Po Valley in Italy, already provide supplementary irrigation and change planting dates [12]. Recent surveys among farmers in central and southern Europe also revealed that changes in soil and water management and adopting drought-tolerant cultivars are some of the main options for adapting to a changing climate [13].
Other adaptation options include measures such as soil management (e.g., mulching, zero tillage) [14,15,16,17], crop diversification/rotation [18,19,20,21], and innovative plant breeding [22,23,24].
However, despite the availability of adaptation solutions, there are still significant knowledge gaps regarding the quantification of their effectiveness, as in the case of irrigation [25]. In addition, most quantitative figures in the literature refer to small-scale studies at a specific location or region [26]. Besides these gaps, there are several other barriers to mainstreaming adaptation in crop farming, including policy frameworks, social and behavioral aspects, financial and institutional capacities, limited infrastructures, and regulatory provisions [12,22,27,28]. Farm characteristics and previous experience with extreme events also affect the adoption of adaptation solutions [29].
Agriculture is an essential economic sector in Greece, a Mediterranean country of the European south. According to the data from the Hellenic Statistical Authority (ELSTAT), agriculture generated 4.3% of the Greek Gross Value Added in 2023, while at the level of some prefectures, its contribution was much higher (16–25%)
Some administrative Greek regions have already compiled climate change adaptation plans, with measures also for crop farming. Also, the new Greek Strategic Plan for the Common Agricultural Policy 2023–2027 [30,31] aims to contribute further to climate change adaptation and mitigation. However, the implementation of adaptation is slow. Farmers are trying to adapt to the changing climate mainly by increasing or adding irrigation and using varieties that are more tolerant to heat and drought. These actions are mostly autonomous and poorly connected to the regional adaptation plans. In addition, integrating regional and local adaptation needs into national and regional policies addressed directly or indirectly to agriculture is often limited in scope and ambition.
These characteristics, combined with the rapidly increasing climate risks in southern Europe and the Mediterranean [6,7], highlight the importance of efficient adaptation in the Greek agricultural sector and make Greece a good case for studying the effectiveness of such measures.
Available studies on the expected benefits from adaptation options for crop productivity in Greece are scarce and often narrow in scope and coverage. The first national assessment on climate change impacts, which was carried out by the Bank of Greece [32], provided estimates on percentage changes in future yields of some cultivations in Greek regions based on some model simulations, findings from regional assessments for other countries, and old climate change scenarios. It also included an adaptation section with only a brief and literature-based analysis of some adaptation measures. Following that, Georgopoulou et al. (2017) [33] quantitatively assessed the effectiveness of main adaptation options for crop farming in Greek regions and for various Representative Concentration Pathway (RCPs) scenarios of climate change; however, this analysis did not include many essential cultivations (e.g., trees other than olive, fodder plants), while model inputs for soil qualities and agricultural practices were predominantly sourced from international literature. Other studies have provided even less input on the adaptation potential in Greece, limiting their analysis to specific crops such as vineyards [34] and olive trees [35] or regions such as Crete [36].
Our present study aims to fill these knowledge gaps and assess quantitatively the effectiveness of three main adaptation options for crop farming, i.e., earlier planting (for annual crops), increase/addition of irrigation, and more resilient hybrids/cultivars to summer heat. We focused on these options as they already form part of many regional adaptation plans in Greece, are considered by decision-makers as essential measures against climate change risks, and are already applied to some extent by farmers. Another reason was the debate in many Greek regions, where pressures on local water resources are high, about the magnitude of future water needs and the expected impact of crop farming in the era of climate change.
To explore the effectiveness of adaptation changes under different levels of climate change, we assessed adaptation options for 2021–2040 and 2041–2060, three for climate change scenarios utilized in contemporary assessments, and for all essential crops in Greece. Our present study is the continuation of a recent research work that we carried out on climate change risks for crop farming in Greece [37], covering several crops and all regions and exploiting local input data. The modeling techniques, databases, and insights from that work have been exploited in the present study to evaluate the effectiveness of adaptation.
The following sections present the methodology for assessing the effectiveness of adaptation options, the results obtained, a discussion, and conclusions drawn.

2. Materials and Methods

2.1. Study Area

This study assesses the effectiveness (in terms of crop yields) of the three adaptation options for crop farming in all 13 administrative Greek regions (Figure 1).
As we assessed adaptation options through modeling at the regional level and for each crop, the numerical effort to simulate the annual growth of 35 crops in 13 regions and for years up to 2060 would be too high and not necessary in all cases as the present share of some regions to the national production of some crops is very low. Therefore, we adopted the following approach to select the cases to model (hereafter, the ‘85% rule’). First, we ranked the regions in descending order for each crop based on their contribution to the national crop production. Then, we selected the top-ranked region, and if its contribution to the national total was less than 85%, we extended the selection to include the second-ranked region. We then selected the third-ranked region if the cumulative contribution to the national total of the former two ones was still under 85%. We repeated this process until the cumulative share of the selected regions reached at least 85%. In the case of grapevines, the analysis covered all 13 regions as the modeling approach does not differ much from region to region. The selected combinations of crops–regions modeled in our present study are indicated with green shading in Figure 2, and the last column presents the cumulative share of the national production per crop covered by our ‘85% rule’.
Official statistical data on the regional distribution of crop production in Greece are available only for years up to 2021. Given the dramatic effect of the COVID-19 pandemic on all economic activities, including the production and demand of agricultural products, to apply the ‘85% rule’, we used the Hellenic Statistical Authority’s (ELSTAT) annual production data for the last year before the pandemic started, i.e., for 2019.
Figure 3 presents the contribution of the regions to the Crop Output (in economic terms) according to statistical data from the Economic Accounts for Greece published annually by the ELSTAT. The combinations of crops–regions selected by applying the ‘85% rule’ correspond to 92% of the total Crop Output in Greece, meaning they cover all essential production sites and crops. Almost 70% of the economic output of crop farming is concentrated in five out of 13 regions (Eastern Macedonia and Thrace, Central Macedonia, Thessaly, Western Greece, and Peloponnese) and is derived mainly from fruits (including grapes, olives, and their products), fresh vegetables, and industrial crops (e.g., cotton).

2.2. Climate Simulations

Adaptation options for crop farming were assessed for 2021–2040 and 2041–2060. The period 1986–2005 was selected as the historical climate reference period to consider the climate change that has already taken place. We explored three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5) to investigate the impact of different levels of future climate change on the effectiveness of adaptation.
To assess adaptation options, we used simulation approaches linking crop growth, crop yields, and climatic conditions (described in Section 2.3). These approaches require input climatic data with sufficiently high spatial and temporal resolution. Daily values of climatic parameters (i.e., maximum, minimum, and mean, daily air temperature, total daily precipitation, and average daily solar radiation) were collected from climate simulations carried out in the context of the ‘Coordinated Downscaling Experiment-European Domain’ (EURO-CORDEX) research program [38]. Five climate simulations that combined three global climate models and three regional climate models with horizontal analysis of 0.11° (i.e., approximately 10 km) were utilized (Table 1) to explore different future climatic conditions, keeping in mind that climate projections represent a significant source of uncertainty in climate change impact assessments for crop farming [39].
As data from climate models are available in grid format, we selected representative locations within each region to collect climate data for future periods. Each Greek administrative region comprises many prefectures. As the ELSTAT provides statistical crop production data at the prefecture level, we identified, for each crop and region, the prefecture with the highest production in 2019, and its capital was considered representative (in terms of climatic conditions) of the whole region.
Climate parameters’ values from each climate simulation for each year of the three periods examined (i.e., 1986–2005, 2021–2040, and 2041–2060) and each of the three climate change scenarios explored (RCP2.6, RCP4.5, and RCP8.5) were collected for all these representative Greek locations and were subsequently used in the simulations to assess the three options for adaptation.

2.3. Methodology for the Assessment of Adaptation Options

The methodology for quantitatively assessing adaptation options in this study originated from and is consistent with the approach for estimating climate change risks for crop farming in Greece, which was developed and applied in the context of our previous research work [37]. In the present study, we applied the following methods to assess the potential effects on crop yields from the application of adaptation measures:
  • For most annual crops (e.g., cereals, rice, cotton, vegetables), we used the Decision Support System for Agrotechnology Transfer (DSSAT, Ver 4.8.0.027, DSSAT Foundation, Gainesville, FL, USA) [40] to simulate adaptation options. DSSAT includes many crop growth simulation (agronomic) models; in the context of our previous work mentioned above, we adjusted those models to Greek conditions as much as possible and calibrated them using regional historical data on crop yields. The models’ calibration process is described in detail in the relevant paper [37]. The accuracy of these calibrated models, which were used in our present study to assess the different adaptation options, is considered satisfactory as the deviations between the observed crop yields of the 2011–2019 period and those simulated by the calibrated DSSAT models for the 20-year historical climate reference period do not exceed ±15% across all crops-regions and are even below the ±10% level in almost all of them. In the present study, we used these calibrated models without any changes under the historical climate reference period and the ‘No adaptation’ case. To assess specific adaptation measures, we appropriately modified their input data on planting dates, irrigation management schemes, and hybrids/cultivars to reflect the changes to be introduced by these measures (Section 2.3.1).
  • For vineyards, we assessed adaptation options by using the Agricultural Production Systems Simulator software tool (APSIM, Ver. 7.10, APSIM initiative, Queensland, Australia) [41,42]. In the context of our previous research, we calibrated the grape growth model of APSIM for a high-value-added foreign variety widely cultivated in Greece (i.e., Sauvignon Blanc) and for representative wine-producing locations in the northern and southern regions of the country. The model’s calibration process is described in detail in the relevant paper [37]. The accuracy of the calibrated model, which was used in our present study to assess the different adaptation options, is considered satisfactory as the deviations between the observed average crop yields during 2011–2019 and those simulated by the calibrated APSIM model do not exceed ±8% across grape and wine producing regions in northern and southern Greece. In the present study, the model’s simulations under the historical climate reference period and the ‘No adaptation’ case remained unchanged. Again, we appropriately modified the model’s input data on crop management practices and cultivar characteristics to assess adaptation options (Section 2.3.1).
  • For crops that are not yet covered by the DSSAT crop growth models (mainly perennial and arboreal crops), in the context of our previous work, we developed statistical regression models (based on regional historical data on crop production, cultivated areas, and climatic conditions) for all major crops cultivated in various Greek regions, linking crop yields and climatic parameters at the regional level. The accuracy of these models is satisfactory as most (77%) of them have an R2 value equal to or greater than 0.7, and all of them have a significance F value less than 0.05 (with most having a significance F value less than 0.001). More details are provided in the relevant paper [37]. In the present study, we utilized these statistical regression models without any changes under the historical climate reference period and the ‘No adaptation’ case, and we modified their inputs on precipitation to assess the effectiveness of increased/added irrigation (Section 2.3.2).
Figure 4 summarizes the simulation approaches followed for the various crops. Figure 5 presents the estimated future changes in crop yields in the absence of adaptation (‘No adaptation’ case), as derived from our previous research work [37]. These estimates represent the basis for assessing the effectiveness of adaptation measures by comparing the effects of climate change on crop yields under an adaptation option with those under no adaptation. Models for calculating crop yields under the historical climate of 1986–2005 were developed and calibrated based on the ELSTAT statistical data on regional crop production and agricultural areas. These data inevitably integrate different agricultural practices at the farm level. Thus, the resulting crop yields, which form the basis for quantitatively assessing the effects of climate change on crop yields without and with adaptation, represent ‘average’ farming practices followed at present and not optimal (e.g., some farmers may use minimal irrigation due to local technical or other constraints).

2.3.1. Simulation of Adaptation Options Using Agronomic Models

For most annual crops, we assessed the effects of adaptation options with the DSSAT Ver 4.8.0.027 [43], which simulates the crop growth cycle and links crop development and, hence, crop yields at maturity with daily values of climate parameters (i.e., minimum and maximum and temperatures, solar radiation, and precipitation) and agricultural practices (e.g., sowing, irrigation, fertilization, etc.).
In the context of our previous research, we adjusted the model inputs to integrate (to the extent possible) Greek conditions into the simulations, namely soil types, cultivars/varieties used, and agricultural practices applied in the various regions.
In this study, the input data of the calibrated DSSAT models were modified as follows to assess the effects of adaptation options:
(a)
For earlier planting, the sowing dates of the various crops in the DSSAT simulation files were shifted one month earlier, complemented where necessary by changes in the scheduling (but not in the total annual quantity) of irrigation/fertilization. Given the large number of crops and regions, the two future periods, and the three climate change scenarios, and to reduce computational effort, we applied a one-month shift to all regions and crops. For each crop, shifting its sowing date alters its growth cycle due to changes in climatic conditions compared to the ‘No adaptation’ case, and hence, has the potential to affect the maturity date and crop yield at maturity. For example, for crops where maturity in the absence of adaptation typically occurs in summer, an earlier planting can reduce exposure to adverse summer conditions (e.g., extreme heat, drought) and consequently limit the adverse effects on yields.
(b)
For crops already irrigated under the ‘No adaptation’ case, we modified appropriately the DSSAT simulation files to include a 15–20% increase in irrigation volume. This percentage increase was applied uniformly to the existing irrigation scheduling except in cases where early simulation results revealed that a more time-targeted increase in irrigation was necessary for the adaptation measure to lead to an improvement over ‘No adaptation’. For crops grown mostly in drylands in Greece, such as barley, wheat, and a small portion of cotton, irrigation was added to the DSSAT simulation files, with an irrigation schedule based on the relevant one for irrigated crops. An increase/addition of irrigation can significantly improve crop yields by providing consistent moisture levels to crops that are fundamental for plant growth.
(c)
The effects on crop yields from introducing hybrids/cultivars resilient to climate change are challenging to simulate, as data on their physiological behavior are rarely available in the literature. Thus, we decided to focus on two crops, barley and maize, for which there is already significant progress in developing new hybrids/cultivars that are more tolerant to climate change, and which are of particular importance in Greece. Of special importance are hybrids/cultivars with a short biological cycle, which have the advantage of completing their development before the very hot and dry summer days (which will be even hotter and drier in the future). Thus, through a trial-and-error process, the DSSAT input files were modified so that the crop cycle resulting under this adaptation option is shorter than the one under ‘No adaptation’.
For each adaptation option, the simulation process was as follows: We fed the DSSAT tool with the daily values of climate parameters (minimum and maximum temperature, solar radiation, and precipitation) for each of the climate simulations in Table 1 and year of the periods 2021–2040 and 2041–2060. The simulations were as in the ‘No adaptation’ case, that is, for the same set of cultivars (except for adaptation option (c)) and soil types. Thus, we performed more than 600 runs of the model (i.e., N combinations of soil type-hybrid/variety × 2 × 20 years × 5 climate simulations × 3 RCP scenarios) per crop and region. Next, for each crop and region, each RCP scenario, and each climate simulation, we calculated the average crop yield during each 20-year period and then the percentage change in each future period from the historical climatic conditions. Finally, we averaged these percentage changes across the different climate simulations. Thus, we came up with a final estimate for the crop yield change per crop and region under each RCP scenario, future period, and adaptation option.
Regarding vineyards, we assessed adaptation options (b) and (c) using the calibrated APSIM model (APSIM v2023.5.7224.0). We followed a simulation process like that of the DSSAT for estimating the regional grape yields in the 20-year future periods under each adaptation option and then calculating the relevant changes from the historical climate reference period.

2.3.2. Assessment of Adaptation Options Using Statistical Models

As stated above, we quantitatively assessed the effects of increased/added irrigation on crop yields for crops not yet covered by the DSSAT through the same statistical models developed for the ‘No adaptation’ case. The assessment of the effectiveness of adaptation in the present study using these models was performed by introducing into the equations of the statistical models linking regional crop yields with the monthly values of statistically significant climatic parameters, a ‘pseudo’ precipitation to reflect the adaptation measure. Specifically, we increased the amount of monthly precipitation (as projected by the climate simulations for future periods) in these equations by the amount of additional irrigation whose effects on yields were to be quantitatively assessed. This process was applicable only for those crops/regions for which (a) precipitation for one or more months was found to be statistically significant and (b) precipitation for at least one month was found to have a positive correlation (i.e., an increase in precipitation will lead to an increase in annual crop yield). In addition, out of these combinations, we retained only those with a medium/high reduction in crop yield under the ‘No adaptation’ case (Figure 5). Figure 6 presents the final set of crops/regions for which we assessed the effects of the increase/addition of irrigation. Because of the form of the equations of statistical regression models (i.e., crop yields are linked to climatic parameters and, for some crops/regions, to dummy variables reflecting the contribution of non-climatic parameters and extreme events), we were not able to assess quantitatively the effectiveness of adaptation measures other than irrigation.
Furthermore, for this set of crops, we conducted sensitivity analyses on the effectiveness of irrigation by exploring the effects of different percentage increases of irrigation on crop yields. Such analyses were much easier for these crops than for those where we used agronomic models to assess adaptation options, as the utilized regression models require monthly and not daily values of irrigation supply. Any percentage increase in irrigation needed to be expressed in absolute figures (i.e., in mm of ‘additional rainfall’) to be entered into the regression model equation. Therefore, we first calculated the required irrigation for each crop and region under the historical climate reference period 1986–2005 by considering the typical water needs of each crop (according to the current agricultural practices in Greece and the literature) and the rainfall in the historical period, with the assumption that irrigation fully covers any resulting deficit. In future periods where precipitation is expected to be lower than the historical climate reference period 1986–2005, an increase in the historical irrigation for a crop in a region would be able to cover part (or the total) of this future water deficit due to climate change.

3. Results

3.1. Effectiveness of Earlier Planting

Figure 7 presents the estimated changes in crop yields in 2021–2040 and 2041–2060 from those under the historical climate of 1986–2005, when earlier planting was applied.
Figure 7 does not include estimates for cotton in two northern regions (i.e., Eastern Macedonia and Thrace, and Central Macedonia) as the temperatures in those regions at the beginning of March (when sowing should occur under the shift of planting by one month earlier) are low, even under the future climate, and therefore are not suitable for the smooth growth of plants and the achievement of satisfactory crop yields.
Our results reveal that earlier sowing, coupled with irrigation/fertilization timing modifications where necessary, is a very efficient adaptation measure in the period 2021–2040, as it can reduce crop yield losses by more than 50% in 60% of total cases (crops/regions/RCP scenarios), with a median reduction in yield losses across all crops, regions, and RCP scenarios by 58% during this period. In addition, in some cases of wheat and maize, earlier planting can lead to yield increases (up to +10%) compared with yields under the historical climate. In 2041–2060, the effectiveness of the measure decreases considerably, with only 37% of the total cases showing reductions in yield losses by more than 50% and a median decrease in yield losses by 42% across all crops, regions, and RCPs. These findings confirm those of other relevant studies on locations in Europe [44,45,46,47] and other areas in the Mediterranean [48,49].

3.2. Effectiveness of Increase/Addition of Irrigation

As mentioned in Section 2, we examined this adaptation option for crops simulated with statistical regression models and the DSSAT and APSIM agronomic models. The expected effects on crop yields were estimated as follows:

3.2.1. Crops Simulated by Statistical Regression Models

Figure 8 presents the estimated effectiveness of the measure, with green shading indicating the cases where increased/added irrigation not only limits the reductions in crop yield in the absence of adaptation but also fully compensates for yield losses or even leads to crop yields higher than those under the historical climatic conditions of 1986–2005. Figure S1 (Supplementary Material) shows the percentage of the corresponding cumulative future precipitation during the months in which precipitation is statistically significant—to which each increase in irrigation corresponds. For lentils now grown on drylands, the percentage increase in irrigation in Figure 8 corresponds to the corresponding cumulative future precipitation.
The results show that an increase in irrigation by 15–20% from present levels is quite effective as a climate change adaptation measure as it significantly reduces (on average, by 51% in RCP2.6/2021–2040 to 31% in RCP8.5/2041–2060) the crop yield losses occurring under the ‘No adaptation’ case.
On the other hand, Figure 8 and Figure S1 reveal that to compensate for yield losses through irrigation, the quantities of irrigation water supplied to crop farming should increase substantially. Specifically, in 2021–2040, the necessary average increase ranges from 45% in RCP2.6 to 65% in RCP8.5, while in the 2041–2060 period, from 62% in RCP2.6 to 83% in RCP8.5. Furthermore, these increases correspond to very high percentages of future precipitation, i.e., 47–59% in 2021–2040 and 58–79% in 2041–2060. Although irrigation during some specific months does not use precipitation water only during these months but also from the remaining months of the year, the high percentages of future equivalent precipitation mentioned above indicate that it will be tough (if not impossible) to fully compensate for the future losses of crop yields through increased irrigation as the necessary amounts of irrigation water will likely not be available, especially under moderate or severe climate change (RCP4.5 and RCP8.5).

3.2.2. Crops Simulated by the DSSAT and the APSIM Tools

Figure 9 shows, for crops simulated with the DSSAT tool, the estimated changes in crop yields in 2021–2040 and 2041–2060 from those under the historical climate of 1986–2005, when irrigation was increased or added.
For 2041–2060, we assessed the measure using the DSSAT tool only for RCP8.5 due to the heavy numerical effort required, as the irrigation enhancement applies to all crops and crop simulations for each region and year of the two 20-year future periods.
As shown in Figure 9, the increase/addition of irrigation is very effective in the period 2021–2040 as it has the potential to reduce yield losses that occur in the ‘No adaptation’ case by more than 50% in 57% of total cases examined (crops/regions/RCP scenarios), with a median reduction in yield losses across all crops, regions, and RCP scenarios of the order of 57% during this period. In addition, for some rainfed crops such as barley, additional irrigation can fully offset crop yield losses under no adaptation, which agrees with the findings of other studies for locations in southern Europe [14]. However, whether this additional irrigation water will be available under future climatic conditions remains questionable. In the period 2041–2060, however, the effectiveness of the adaptation measure decreased significantly, reducing yield losses by more than 50% in only 22% of the cases (crops-regions) under the very adverse RCP8.5 scenario of climate change and with a median reduction in yield losses by 27% across all crops and regions.
For vineyards simulated using the APSIM tool, increasing irrigation by 20% in 2021–2040 reduced yield losses by 5–10% across most regions and under all three RCP scenarios. However, in this period and under scenario RCP8.5, the required percentage increase in irrigation in the regions of Peloponnese and South Aegean was estimated at 50%, while in the region of Crete, it was found to be even higher (i.e., 70%). Overall, during this first 20-year period, yield losses were reduced through increased irrigation in all regions and RCP scenarios, but by no more than 10%. Under the RCP2.6 scenario, an increase in irrigation by 20% reduced yield losses’ by up to 5% in all regions from 2041 to 2060. Under RCP4.5, the regions of Western Macedonia, Thessaly, Central Greece, and South Aegean required an increase in irrigation by 50% and the region of Crete by 70% to achieve the highest reduction in yield losses (which remained lower than 10% in any case). Finally, in the second 20-year period and under the RCP8.5 scenario, an additional irrigation of approximately 50% was found to be needed in five regions (i.e., Western Macedonia, Thessaly, Central Greece, Attica, and Peloponnese) and by 100% in two regions (i.e., South Aegean and Crete) to achieve the highest reduction in yield losses, which did not exceed 10%.
The significant increases in water supply for many crops to effectively adapt to climate change estimated in the present study, combined with the unavoidable limitations of local water resources in southern European regions such as Greece due to climatic and non-climatic factors (e.g., competitive water uses and over-exploitation of water aquifers), highlight the importance of increasing the efficiency of irrigation systems to make better use of water resources for crop farming. Such improvements can cover, to some extent, the increasing future water demand because of climate change and population growth [50].

3.3. Effectiveness of More Resilient Hybrids/Cultivars

A quantitative assessment of the effectiveness of this measure was possible only for the crops simulated using the DSSAT and APSIM tools.
Regarding crops simulated by the DSSAT, we assessed this adaptation option for two of them, namely barley and maize, where there is significant progress in developing new hybrids/cultivars that are more resilient to climate change. Hybrids with a short biological cycle are particularly important, as they can complete their development before the very hot and dry summer, which in the future climate will be even hotter and drier in southern Europe. Figure 10 shows the estimated effectiveness of the use of such hybrids/cultivars.
Results show that this adaptation measure reduced yield losses by 50% or more in most of the cases considered (crops-regions-RCP scenarios-time periods), with a median reduction in yield losses across both crops and all regions and RCP scenarios of 65% and 51% in 2021–2040 and 2041–2060, respectively. Using short-cycle hybrids/cultivars to adapt to climate change looks particularly effective for maize. However, in the region of Thessaly, where a significant increase in temperature and a decrease in precipitation are expected under the future climate, the reductions in maize yield losses are much lower than in the other regions and do not exceed 40% in 2041–2060. Notably, our results derive from assessing some short-cycle cultivars currently in the market. Therefore, they only indicate the potential of new climate-tolerant and region-specific crop varieties to limit negative climate change impacts on crop yields, particularly under very adverse climate change scenarios such as RCP8.5 [51].
We also examined this adaptation measure for vineyards using the APSIM tool, simulating grapevine varieties with a growing season shorter than one month compared to the ‘No adaptation’ case. The results showed that in most cases, the measure resulted in modest yield increases of up to 5%. However, in 2041–2060 and under the RCP8.5, the use of varieties with a one-month shorter growing season increased yields by 5–10% in the regions of Peloponnese, South Aegean and Crete, while a similar effect emerged under the RCP4.5 for the regions of Western Macedonia, South Aegean and Crete. While our present study focused on international varieties with a high added value widely cultivated in Greece, other studies found that indigenous late-ripening varieties are less affected by future temperature increases [34] and thus, their cultivation could be further explored as an adaptation measure in some Greek regions.

3.4. Overall Picture of the Effectiveness of Adaptation Measures

Figure 11 provides an overall picture of the effectiveness of the three adaptation measures assessed in this study. It shows the potential percentage reductions in crop yield losses (occurring in the absence of adaptation) for each adaptation option, each RCP scenario, each future 20-year period, and all crops considered in our study. Gray shading marks the area of reductions up to 100%, which corresponds to the total compensation of yield losses. Reductions exceeding 100% correspond to cases where an adaptation measure increased crop yields under future climatic conditions compared to yields under the historical climate of 1986–2005.
Figure 11 reveals that all three adaptation options can significantly reduce crop yield losses occurring without adaptation, particularly during 2021–2040 (where reductions in many cases exceed 50%). In this period, though, temperature increases, and precipitation reductions are much smaller than in 2041–2060, and increasing or adding irrigation can even improve crop yields occurring under the historical climate of 1986–2005 and with no adaptation. This improvement is primarily because, at present, many small farmers do not fully exploit the potential for crop irrigation due to financial constraints, limited local water resources, and relevant infrastructure for the distribution of irrigation water. Under the modeling of this adaptation option, such constraints were assumed to be lifted. However, these positive adaptation effects diminish significantly under severe climate change, as in 2041–2060 under the RCP8.5 scenario. In general, as we move from the first to the second 20-year period and from RCP2.6 to RCP8.5, crop yield reductions achieved from adaptation measures tend to ‘concentrate’ within the gray area and indeed towards its upper part, i.e., reductions in yield losses often do not exceed 25–30%. These findings are consistent with recent research on the benefits of adaptation for crop farming in other locations in southern Europe and the Mediterranean (e.g., see [47,52,53,54,55,56]). In conclusion, the effectiveness of adaptation measures depends significantly on the severity of climate change and decreases considerably as this change becomes more intense. Therefore, we should consider this dynamic nature of adaptation when designing a set of adaptation measures and prioritize and combine measures according to the expected changes in the local climate.

4. Discussion

Several factors affected our estimates of the effectiveness of the three adaptation options examined for crop farming in Greece.
First, the magnitude of the potential reductions in crop yield losses to be achieved by adaptation measures directly depends on the quantitative estimates of those losses under no adaptation, as these form the baseline with which to compare the performance of each adaptation measure. The figures for the losses used in the present study came from our previous research [37], which discussed in detail the assumptions, uncertainties, and limitations associated with these quantitative estimates. Furthermore, in our present study, we followed a methodological approach consistent with that in our previous work to properly compare regional crop yields with and without adaptation; those assumptions, uncertainties, and limitations apply to our present estimates of the effects of adaptation measures.
Furthermore, our estimates on the effectiveness of the three adaptation options examined are affected by limitations that arise from our modeling simulations together with the large scope of our study (i.e., many crops, regions, future periods, climate change scenarios, climatic simulations):
(a)
Regarding annual crops, our quantitative assessment of the effects of adaptation options on crop yields mainly relied on crop simulations through the DSSAT tool, carried out for each of the 13 Greek administrative regions, five climate simulations, three RCP scenarios, two 20-year periods, several cultivars for each crop, and many agricultural soil types. In addition, those simulations were first performed yearly (i.e., the model estimated crop growth per day of each year, from sowing to harvest); then, the calculated crop yields were averaged per period and RCP scenario. This modeling process, although it allows for a very detailed simulation of crops’ growth under specific management and climatic conditions, requires a significant numerical effort, particularly for the adaptation option of irrigation, which was examined for all crops simulated through the DSSAT (11 crops in total) and all Greek regions. To reduce computational time, we applied specific software programming scripts that allowed us to automate, to some extent, the utilization of input data and the processing of output data, thus significantly reducing the computational time. However, the number of simulations needed for irrigation remained high; therefore, we limited the assessment of this adaptation measure for the 2041–2060 period to the RCP8.5 scenario, the most extreme climate change projection. Consequently, our assessment of the effectiveness of irrigation as an adaptation option in this second 20-year period represents a conservative estimate. Additional climate change scenarios should be examined in future research, particularly for crops and regions where the measure’s effectiveness drops significantly from 2021–2040 to 2041–2060.
(b)
The significant computational effort due to the several crops, regions, climate simulations, RCP scenarios, hybrids/cultivars, and types of agricultural soils considered during simulations carried out by agronomic models limited the elaboration of sensitivity analyses regarding the amount and schedule of the additional irrigation water, the shifting of planting dates by less or more than one month and in a non-uniform manner for all crops, and the assessment of several short-cycle hybrids/cultivars. We performed such analyses for irrigation applied to the crops simulated by statistical regression models and only to a small extent for crops simulated by the DSSAT and APSIM tools. Further sensitivity analyses regarding irrigation, shift of planting dates, and other crop management practices represent another area for future research, particularly for regions with significant climate variability and/or already limited water resources and crops showing significant crop yield reductions under no adaptation.
(c)
Although statistical regression models allowed us to assess the effects of additional irrigation on the regional yields of several crops not yet covered by the specific agronomic models used in this study, the form and content of the equations of these models prevented the examination of the other two adaptation options, namely shifting planting dates and using short-cycle hybrids/cultivars. Thus, in the context of future research, agronomic models for crop growth simulations that would allow the assessment of additional adaptation options should be developed, particularly for tree cultivations with a high added value in Greece, such as olive trees and citrus fruit trees.
In addition, although the application of our ‘85% rule’ ensures that the quantitative assessment of the effectiveness of adaptation per crop covers all regions that cumulatively produce most (i.e., ≥85%) of the crop production at the national level, it still leaves out some regions where the production of a particular crop may have a marginal contribution to the national total but is very important in terms of regional/local income and employment. These cases need to be considered in the context of region-specific assessments, which fall beyond the context of our present study.
Furthermore, as the primary goal of our study was to assess the effects on crop yields of three main adaptation options across all Greek regions and for all main crops cultivated in Greece, we did not undertake cost-benefit analyses of these options, and we did not examine the effects on future crop yields from combinations of these options. However, available research highlights the economic, environmental, and other benefits of such combinations and scheduling of different adaptation options in the context of adaptation pathways that consider the evolution of future climate change [6,57,58]. In addition, we did not explore the interactions between climatic and non-climatic drivers at the local and regional scale, which are complex and create synergies and trade-offs in each adaptation option [59]. Furthermore, besides effectiveness, we did not explore other criteria when evaluating adaptation options, namely affordability, feasibility (e.g., technical, institutional, social, geospatial/ecological), flexibility, and environmental side effects, which are important determinants of successful adaptation [60,61,62]. All these topics represent major areas for further research.
Also, although our assessment focused on three measures that farmers in Greece and elsewhere consider as main options to adapt to climate change, our study did not examine many others with the potential to increase resilience to climate change. These represent areas for further research. For example, mulching, which improves moisture retention in soil, can maintain or even increase crop yields under future climatic conditions and, at the same time, significantly reduce irrigation needs [14]. Crop diversification/switching is another measure to increase the resilience of crop farming to climate change [63,64,65,66]. Our estimates on crop yield losses under no adaptation (Figure 5) confirm this as these losses largely differ between crops within the same region.
Finally, although our quantitative assessment brings new knowledge on the benefits of specific adaptation options and has shown that they can be very effective in reducing crop yield losses because of climate change, there are several other determinants for the adoption of these measures by farmers [29]. This is particularly the case for measures that require radical or large changes in infrastructures and operations of crop farming at the regional/local level (e.g., significant expansion of irrigation network, introduction of modern irrigation technologies, shift to new/diversification of crops, large-scale changes in cultivation practices). Such adaptation measures represent regional transformations with potentially high benefits but also significant constraints and implications (e.g., financial, social, institutional) that should be considered in decision-making [67,68]. Socio-economic barriers to adaptation are significant and should be considered and addressed when preparing adaptation strategies and selecting adaptation measures. For example, district irrigation managers and farmers in locations of southern Europe are often reluctant to replace existing water-intensive crops (e.g., maize, vegetables, rice, cotton) with less water-intensive ones despite the already high pressures on local water resources, as the former cultivations form part of the regional/local food tradition and economic activity [12]. Also, as the economic feasibility of enhancing existing irrigation systems is often low [6], recent surveys among farmers have shown that financial support from the central administration is key for mainstreaming this type of adaptation [69].

5. Conclusions

In this study, we quantitatively assessed the effectiveness of three main options for adapting Greek crop farming to climate change: shift of planting dates, increase/addition of irrigation, and use of more resilient hybrids/cultivars. We examined the performance of these options in terms of their effect on crop yields for 35 crops in 13 Greek regions utilizing agronomic and statistical regression models, using climatic input data from five climatic simulations, three climate change scenarios comprehensively covering the range of potential evolution of climatic parameters, and two 20-year periods by 2060.
Our results indicate that all three adaptation options examined have the potential to significantly reduce crop yield losses occurring under no adaptation, particularly during 2021–2040, when in many regions and crops, adaptation can compensate more than half of the losses. In addition, during this period, the measures examined can lead to higher crop yields than those under the historic climate conditions. However, the effectiveness of adaptation measures significantly diminished under very adverse climate change conditions such as those expected in 2041–2060 under scenario RCP8.5. This situation indicates that beyond certain thresholds of climate change, adaptation measures undertaken at the farm level will have to be complemented with changes of a more systemic/transformative nature, such as diversification of cultivations and shifts of some crop cultivations to more climate-favorable regions. Furthermore, our results show that effective adaptation through irrigation will often require significant increases in water supply for crop farming, which may be very difficult, if not impossible, to provide due to increasing drought under future climatic conditions. Thus, using more efficient water supply technologies and systems, less water-demanding crop varieties, and soil management practices to enhance soil water availability is a priority in southern European countries [70]. The optimization of irrigation supply schemes based on regional and crop-specific water needs and the exploitation of measurement tools to monitor soil moisture can contribute to further savings in irrigation water under climate change [71].
This study represents a systematic attempt to quantitatively assess the expected direct benefits of main adaptation options on crop productivity in Greece. Under appropriate input data and model adjustments, our methodology can be applied to other southern European regions and countries and beyond to enhance knowledge of the regional and local potential for climate change adaptation and to assist decision-makers in designing effective adaptation strategies.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture14101814/s1. Figure S1: Percentage of cumulative future precipitation—during the months in which precipitation is statistically significant in the statistical regression models linking crop yields with climatic parameters—to which each level of irrigation increase corresponds.

Author Contributions

Conceptualization: E.G. and D.V.; methodology: E.G., N.G., and D.V.; software: N.G. and M.D.; validation: E.G. and D.V.; data curation: Y.S., N.G., and M.D.; writing—original draft preparation: E.G., D.V., M.D., Y.S., and N.G.; writing—review and editing: E.G., D.V., D.P.L., and S.M.; visualization: E.G. and N.G.; supervision: E.G. and N.G.; project administration: N.G.; funding acquisition: S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was fully funded by Piraeus Financial Holdings S.A. (the contract number is CT_01112022).

Institutional Review Board Statement

Not applicable as the study does not involve humans or animals.

Data Availability Statement

Data are mostly contained within the article. Specifically, this study used publicly available climate data from the EURO-CORDEX program which are available for free download at the Earth System Grid Federation (ESGF) Federated ESGF Nodes (https://esgf.llnl.gov/nodes.html, accessed on 15 January 2023). The study also used publicly available annual data on cultivated areas and production per crop and region from the ELSTAT Annual Agricultural Statistics reports (http://dlib.statistics.gr/portal/page/portal/ESYE/categoryyears?p_cat=10007963&p_topic=10007963, accessed on 15 June 2023) and the ELSTAT database for agriculture, livestock, and fisheries (https://www.statistics.gr/en/statistics/-/publication/SPG06/2019, accessed on 15 June 2023). Data on the qualities of soils in Greece were derived from the open-access European Soil Database v2.0 (https://esdac.jrc.ec.europa.eu/resource-type/european-soil-database-soil-properties, accessed on 20 April 2023). The rest of the data used for model simulations can be accessed upon request from the manuscript’s authors. The sources of models used in the present study are as follows: the statistical regression models used in the present study are available in the Supplementary Material of our previous study [16]; the DSSAT tool is available upon request from the DSSAT Foundation (https://dssat.net/, accessed on 23 February 2023); and the APSIM tool is publicly available from the APSIM Initiative (https://www.apsim.info/download-apsim/, accessed on 5 February 2023).

Acknowledgments

Acknowledgment is made to the APSIM Initiative which takes responsibility for quality assurance and a structured innovation program for APSIM’s modeling software, which is provided free for research and development use (https://www.apsim.info/download-apsim/, accessed on 5 February 2023).

Conflicts of Interest

Author Nikos Gakis and Dimitris P. Lalas were employed by the company FACE3TS S.A. Author Dimitris Voloudakis and Markos Daskalakis were employed by the company RethinkAg S.P. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that this study received funding from Piraeus Financial Holdings S.A. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

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Figure 1. Greek regions considered in the assessment of adaptation options for crop farming.
Figure 1. Greek regions considered in the assessment of adaptation options for crop farming.
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Figure 2. Share of Greek regions to the national total of crop production in 2019 (note: the cases indicated by green shading are those modeled in the context of this study).
Figure 2. Share of Greek regions to the national total of crop production in 2019 (note: the cases indicated by green shading are those modeled in the context of this study).
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Figure 3. Crop Output (in EUR) per agricultural product and region in Greece in 2019 (note: the cases indicated by green shading are those modeled in this study).
Figure 3. Crop Output (in EUR) per agricultural product and region in Greece in 2019 (note: the cases indicated by green shading are those modeled in this study).
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Figure 4. Simulation approaches linking crop yields and climatic parameters that we used to assess the effectiveness of adaptation options. A: agronomic model (Decision Support System for Agrotechnology Transfer-DSSAT), B: grape model (Agricultural Production Systems Simulator software tool-APSIM), C: statistical regression model.
Figure 4. Simulation approaches linking crop yields and climatic parameters that we used to assess the effectiveness of adaptation options. A: agronomic model (Decision Support System for Agrotechnology Transfer-DSSAT), B: grape model (Agricultural Production Systems Simulator software tool-APSIM), C: statistical regression model.
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Figure 5. Estimated crop yield changes (average of climate simulations) from those under historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060 due to climate change, as estimated in our previous research [37], which correspond to the ‘No adaptation’ case in the present study.
Figure 5. Estimated crop yield changes (average of climate simulations) from those under historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060 due to climate change, as estimated in our previous research [37], which correspond to the ‘No adaptation’ case in the present study.
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Figure 6. Combinations of crops-regions whose climate change risks under ‘No adaptation’ had been assessed through statistical regression models in our previous study [37], and those for which we assessed the effects on crop yields from an increase/addition of irrigation in the context of the present study.
Figure 6. Combinations of crops-regions whose climate change risks under ‘No adaptation’ had been assessed through statistical regression models in our previous study [37], and those for which we assessed the effects on crop yields from an increase/addition of irrigation in the context of the present study.
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Figure 7. Estimated crop yield changes from those under historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under earlier planting (‘EP’) and no adaptation (‘NA’).
Figure 7. Estimated crop yield changes from those under historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under earlier planting (‘EP’) and no adaptation (‘NA’).
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Figure 8. Estimated crop yield changes (%) from those under the historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under increased or added irrigation for crops simulated by statistical regression models. A 0% increase/added irrigation corresponds to the ‘No adaptation’ case.
Figure 8. Estimated crop yield changes (%) from those under the historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under increased or added irrigation for crops simulated by statistical regression models. A 0% increase/added irrigation corresponds to the ‘No adaptation’ case.
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Figure 9. Estimated crop yield changes (%) from those under the historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under added irrigation for rainfed crops (i.e., barley, wheat, and dry cotton) or increased irrigation for the rest crops (i.e., tomatoes, rice, potatoes, maize, beans, cotton irrigated, and cabbage). All crop simulations were carried out through the DSSAT tool.
Figure 9. Estimated crop yield changes (%) from those under the historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under added irrigation for rainfed crops (i.e., barley, wheat, and dry cotton) or increased irrigation for the rest crops (i.e., tomatoes, rice, potatoes, maize, beans, cotton irrigated, and cabbage). All crop simulations were carried out through the DSSAT tool.
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Figure 10. Estimated crop yield changes (%) from those under historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under the use of short-cycle hybrids/cultivars (‘SCHC’) and no adaptation (‘NA’), for crops simulated with the DSSAT tool.
Figure 10. Estimated crop yield changes (%) from those under historical climate conditions (1986–2005) for the periods 2021–2040 and 2041–2060, under the use of short-cycle hybrids/cultivars (‘SCHC’) and no adaptation (‘NA’), for crops simulated with the DSSAT tool.
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Figure 11. Overall picture (i.e., all crops and regions examined in this study) of the estimated reductions (%) in crop yield losses occurring in the ‘No adaptation’ case from each adaptation option (i.e., ‘EP’: early planting, ‘IoI-AoI’: increase/addition of irrigation, ‘SCHC’: short-cycle hybrids/cultivars). For 2041–2060 (the last part of the figure, after the thick vertical line), the figure shows the effectiveness of options only for RCP8.5, as the assessment by using the DSSAT tool was limited to this scenario.
Figure 11. Overall picture (i.e., all crops and regions examined in this study) of the estimated reductions (%) in crop yield losses occurring in the ‘No adaptation’ case from each adaptation option (i.e., ‘EP’: early planting, ‘IoI-AoI’: increase/addition of irrigation, ‘SCHC’: short-cycle hybrids/cultivars). For 2041–2060 (the last part of the figure, after the thick vertical line), the figure shows the effectiveness of options only for RCP8.5, as the assessment by using the DSSAT tool was limited to this scenario.
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Table 1. Climate simulations utilized in our assessment.
Table 1. Climate simulations utilized in our assessment.
Regional Climate Models (RCMs) 1Global Climate Models (GCMs)
ICHEC-EC-EARTHMOHC-HadGEM2-ESMPI-M-MPI-ESM-LR
KNMI-RACMO22E☑☑
SMHI-RCA4 ☑☑
DMI-HIRHAM5☑
1 KNMI-RACMO22E: Regional Atmospheric Climate Model (RACMO), developed by the Koninklijk Nederlands Meteorologisch Instituut (KNMI). SMHI-RCA4: 4th version of the Rossby Center Regional Atmospheric Climate Model (RCA), developed by the Swedish Meteorological and Hydrological Institute (SMHI). DMI-HIRHAM5: 5th version of the climate model HIRHAM, developed in a collaboration between the Danish Climate Center at the Danish Meteorological Institute (DMI) and the Potsdam Research Unit of the Alfred Wegener Institute Foundation for Polar and Marine Research.
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Georgopoulou, E.; Gakis, N.; Voloudakis, D.; Daskalakis, M.; Sarafidis, Y.; Lalas, D.P.; Mirasgedis, S. Effectiveness of Options for the Adaptation of Crop Farming to Climate Change in a Country of the European South. Agriculture 2024, 14, 1814. https://doi.org/10.3390/agriculture14101814

AMA Style

Georgopoulou E, Gakis N, Voloudakis D, Daskalakis M, Sarafidis Y, Lalas DP, Mirasgedis S. Effectiveness of Options for the Adaptation of Crop Farming to Climate Change in a Country of the European South. Agriculture. 2024; 14(10):1814. https://doi.org/10.3390/agriculture14101814

Chicago/Turabian Style

Georgopoulou, Elena, Nikos Gakis, Dimitris Voloudakis, Markos Daskalakis, Yannis Sarafidis, Dimitris P. Lalas, and Sevastianos Mirasgedis. 2024. "Effectiveness of Options for the Adaptation of Crop Farming to Climate Change in a Country of the European South" Agriculture 14, no. 10: 1814. https://doi.org/10.3390/agriculture14101814

APA Style

Georgopoulou, E., Gakis, N., Voloudakis, D., Daskalakis, M., Sarafidis, Y., Lalas, D. P., & Mirasgedis, S. (2024). Effectiveness of Options for the Adaptation of Crop Farming to Climate Change in a Country of the European South. Agriculture, 14(10), 1814. https://doi.org/10.3390/agriculture14101814

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