© 2025 Author(s). Open access article published, except where otherwise noted, by Firenze University Press under CC-BY-4.0 License for content and CC0 1.0 Universal for metadata. Firenze University Press | www.fupress.com/bae Bio-based and Applied Economics BAE Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Citation: Macedo João, A., Freire, D., Rocha, H., & Dias, J. (2025). Rebuilding strategies for food self-sufficiency: Por- tugal’s past patterns and future ambi- tions. Bio-based and Applied Econom- ics 14(2): 51-65. doi: 10.36253/bae-17226 Received: January 29, 2025 Accepted: June 26, 2025 Published: September 30, 2025 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor in chief: Silvia Coderoni Associated editor: Laura Garcia Her- rero ORCID AMJ: 0000-0002-6239-6122 DF: 0000-0003-2969-4440 HR: 0000-0002-5981-4469 JD: 0000-0003-2517-7905 Rebuilding strategies for food self-sufficiency: Portugal’s past patterns and future ambitions Alexandre Macedo João1,*, Dulce Freire2, Humberto Rocha3, Joana Dias4 1 Center for Interdisciplinary Studies, University of Coimbra, Portugal 2 Center for Interdisciplinary Studies, Faculty of Economics, University of Coimbra, Por- tugal 3 Centre for Business and Economics Research, Faculty of Economics, University of Coim- bra, Portugal 4 Institute for Systems Engineering and Computers at Coimbra, Faculty of Economics, University of Coimbra, Portugal *Corresponding author. Email: j.alexjoao@gmail.com Abstract. This study analyses Portugal’s wheat productive capacity, exploring the reasons behind its recent steady decline despite achieving self-sufficiency in the 20th century. Using Multivariate Adaptive Regression Splines (MARS), which proved to be highly effective in the development of forecasting models, the research provides valuable insights for countries facing similar challenges in defining production strat- egies. By employing this approach, decision-makers can improve resource allocation, ensure food security, and foster a resilient agricultural sector. The findings highlight the importance of understanding wheat production dynamics within the European Union and aligning national strategies with the Union’s goals and policies. The anal- ysis indicates that achieving self-sufficiency is possible, supported by productivity improvements and increased cultivation areas. However, realizing significant produc- tion growth demands the adoption of sustainable strategies. This research contributes to shaping informed agricultural policies, enhancing decision-making processes, and promoting a more sustainable and efficient food production system to meet future challenges. Keywords: wheat production, self-sufficiency, productivity, forecasting models, policy making. JEL Codes: E21, E23, E27, Q10. ABBREVIATIONS EEC/EU: European Economic Community/European Union MARS: Multivariate Adaptive Regression Splines CAP: Common Agricultural Policy https://creativecommons.org/licenses/by/4.0/legalcode https://creativecommons.org/publicdomain/zero/1.0/legalcode https://www.fupress.com/bae https://doi.org/10.36253/bae-17226 https://doi.org/10.36253/bae-17226 https://orcid.org/0000-0002-6239-6122 https://orcid.org/0000-0003-2969-4440 https://orcid.org/0000-0002-5981-4469 https://orcid.org/0000-0003-2517-7905 mailto:j.alexjoao@gmail.com 52 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Alexandre Macedo João et al. INTRODUCTION Cereal production and consumption have been cen- tral issues for the world economy. The main similarity between the subsistence bases of ancient states was the fact that their economy was based on cereal produc- tion (Scott, 2017). During the second half of the 20th century wheat production and productivity per hectare increased steadily, reflecting its status as the main cereal for human consumption. However, in recent decades, the increases in production of several major cereal crops have slowed down, and in several countries, yields start- ed to stagnate (Michel & Makowski, 2013). To further aggravate this situation, the armed conflict affecting the Ukrainian territory caused international wheat prices to rise to all-time highs. In the case of European countries, this confrontation was especially harsh, given that a lot of countries were dependent on cereals imported from Russia and Ukraine (Zhang et al., 2024). Given the stra- tegic relevance that wheat has in societies, how can each State respond to these domestic supply threats? Depart- ing from the analysis of Portugal, this paper aims to answer a question that is arising in many countries. The main objective of this study is to analyse his- torical wheat production and consumption data and to project trends for 2022–2032, in order to find out if Por- tugal has the capacity to become self-sufficient in wheat production, something that was already achieved in the 20th century, particularly during the “Wheat Cam- paign”. We adopt the Multivariate Adaptive Regression Splines (MARS) machine learning model, validated in time-series forecasting (Nayana et al., 2022). By employ- ing the predictions resulting from MARS in scenarios with different cultivated areas, we assessed the implica- tions of land use adjustments on future wheat produc- tion. Although this study is focused on the Portuguese case, the method employed can be applied to other coun- tries. This scenario-based approach provides informed guidance to policymakers, identifying realistic and sus- tainable strategies to optimize production efficiency, strengthen national food security and support informed planning based on each country’s production potential. Portugal was the chosen country because it has been, throughout history, very dependent on the agri- cultural sector. Furthermore, the country has witnessed periods of self-sufficiency and also of very low levels of self-supply in wheat production. But in recent decades, production has dropped to alarming numbers. If in 1989 the degree of self-supply was around 60%, in 2021 this value had dropped to 6.1%. As a result, Portugal became a country very dependent on imports. In order to invert this trend, the Portuguese government created the National Strategy for the Promotion of Cereal Pro- duction (NSPCP), which set self-supply goals in cereal production for the period between 2018 and 2023. The degree of self-supply defined by the NSPCP was 38% for all cereals and 20% for wheat (Barreiros, 2018). Since the NSPCP has already ended, we can confirm that its objectives were not achieved. Regardless, and in order to have defined goals with political relevance, its targets will serve as a basis to build one of the three production scenarios discussed in this study. Two prediction models were developed, one for the per capita consumption and other for the production per hectare. This study is structured as follows. The next section presents the data collected, as well as the chosen meth- odology. Section 3 discusses the results, drawing three scenarios for production. Finally, section 4 presents the main conclusions of this study: Self-sufficiency, although difficult to achieve, would still be possible given the area that was once cultivated in Portugal. DATA SOURCES This study is based on quantitative data and quali- tative information obtained from scientific studies and relevant historical documents. Data is available for the whole Portuguese continental territory, but after a pre- liminary analysis, we observed that 8 districts (out of 18) were responsible for 92% of the national wheat pro- duction from 1920 to 2021. These districts are: Beja, Bragança, Évora, Faro, Lisbon, Portalegre, Santarém and Setúbal (Figure 1). Considering that these districts contributed the most to national production, it is plau- sible to assume that this trend will continue and con- sequently, the analysis focuses on them. Unfortunately, some of the data for the districts, like the cultivated area and the climatic variables, were only available from 1943 onwards. So, although at a national level the available data started in 1920, when looking at the district level the available data only considers the period from 1943. As shown by Figure 1, we can observe the discrepan- cies in production between the chosen districts and the rest of the country. The districts that completely make up the Alentejo region (Beja, Évora and Portalegre), are the three main contributors and together represent 64% of total production. We collected data on total consumption, per capita consumption, population, total production, productivity per hectare, cultivated area and climatic variables (tem- perature and precipitation). The data used in this study were produced by several certified national and interna- tional entities: National Statistics Institute (NSI), Portu- 53 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Rebuilding strategies for food self-sufficiency guese Institute of the Sea and Atmosphere (PISA), Unit- ed Nations (UN), Climate Portal (CP) and Pordata. Production values at district level were calculated by Viana et al. (2021). This data required extensive research, since the sources where it was published have different territorial units over the years. Therefore, it was neces- sary to transform the values so that they were all meas- ured using the same territorial unit. This transformation was done by applying an area-weighting technique that allows the transfer of data from a set of units in the ter- ritory to a second set of units, with which the first ones overlap. This method assumes a spatially homogeneous relationship between the source unit and the target unit, meaning that the variable is assumed to be equally dis- tributed across the source unit (Viana et al., 2021). The evolution of wheat production in Portugal has not been consistent over the last century, as can be seen in Figure 2. Although there have been periods in which produc- tion has come close to and, in some cases, even exceeded consumption, the insufficiency of national production has been a constant in Portuguese history (Pais et al., 1978; Freire & Lains, 2017). In recent decades, the situa- tion has worsened with production capacity declining at an alarming rate. The cultivated area was also calculated by apply- ing the same method used for production and can also be seen in Figure 2. The decrease in cultivated area is pointed out as one of the main reasons why Portugal has seen such large production losses in recent decades. The NSI estimated, for the year of 2022, an area of about 103 thousand hectares dedicated to cereal cultivation, the lowest value in the last 100 years. In comparison, 322 thousand hectares of wheat were cultivated in 1989, a year in which the degree of self-supply reached 60%. Wheat consumption in Portugal has increased over the last century but has stagnated since the beginning of the 21st century. While the consumption registered in 2021 is below its peak, it is still very high when com- pared to historical values. It’s important to note that the growth in consumption was achieved through an increase in wheat imports and not production (Freire & Lains, 2017). In our analysis, the consumption values are of total consumption, which include human, animal, and other uses. The evolution of total consumption can also be observed in Figure 2. The population of each country constitutes another essential variable in this analysis, as it directly affects the calculation of self-supply. Therefore, we collected data on the total Portuguese population at the end of each year between 1920 and 2021. For the period 2022 to 2032, we used the population forecasts available in the United Nations database (Gaigbe-Togbe et al., 2022). Productivity per hectare is more relevant at district level because it enables us to analyse which districts have the highest yields and where production can be more efficient and profitable. Historically, productivity levels in Portugal have been low, and experts argue that this is mainly due to poor soil and adverse climatic conditions suitable for increasing productive efficiency in a competi- tive market. In addition, the soils with productive poten- tial have been progressively occupied by more efficient and profitable crops, such as vineyards, olive groves and almond trees (Pais et al., 1978; Faisca 2019). The evolu- tion of productivity per hectare is shown in Figure 3. Climatic variables are essential for understanding the performance of agricultural production, as crops are vulnerable to inter-annual climatic variability (Gouveia & Trigo, 2008). Despite technological advances, modern agriculture is still heavily dependent on weather condi- tions, which poses a significant risk to production due to short-term variability and future climate changes. Jam- bekar et al. (2018) used data mining techniques to model Figure 1. Percentage of national production by district (1920-2021). Source: own processing based on data from the NSI using ArcGIS. 54 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Alexandre Macedo João et al. wheat production in India, showing that climatic varia- bles are one of the main predictive factors. Rocha & Dias (2019) observed that low precipitation and its seasonal- ity distribution can explain as much as 75% of the wheat yield variability. Garrido-Lestache et al. (2005) conclud- ed that the quality of certain varieties of wheat under Mediterranean conditions is mainly affected by precipi- tation and temperature. We have values for annual tem- peratures (average, maximum and minimum) and pre- cipitation. These figures cover the period from 1943 to 2021. As we are forecasting future yields, it is also neces- sary to obtain forecasts for the climatic variables, which are available in the CP database. The data is shown in Figures 4 and 5. Figure 2. Production, consumption and sown area in continental Portugal (1920-2021). Source: own processing based on data from the NSI. Figure 3. Productivity of the 8 major wheat producing districts (1943-2021). Source: own processing based on data from the NSI. 55 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Rebuilding strategies for food self-sufficiency METHODOLOGY Based on the available data, two different forecasting models were developed: one for production and the oth- er for consumption. It is assumed that future consump- tion will be influenced mainly by historical consumption and population’s trend. In the case of production, the forecast model includes variables with a direct impact on wheat yield, namely the cultivated area, temperature and precipitation. The method used in both models was MARS, devel- oped by Friedman, J. (1988). It is similar to linear regres- sion as it aims to model the relationship between the dependent and the independent variables. However, unlike linear regression, which uses a single coefficient to represent the relationship between the variables, MARS uses a series of spline functions, with their own set of parameters to model this relationship. Each spline func- tion represents a segment of the overall relationship, and the combination of these functions can approximate a Figure 4. Average, maximum and minimum annual temperature in Portugal with forecasts (1920-2032). Source: own processing based on data from the PISA and the CP. Figure 5. Total annual precipitation in Portugal with forecasts (1920-2032). Source: own processing based on data from the PISA and the CP. 56 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Alexandre Macedo João et al. wide range of non-linear relationships between the pre- dictor variables and the dependent variable. It automati- cally identifies important interactions in the data which it fits into a model to capture these interactions (Nayana et al., 2022). Its ability to automatically identify and capture complex relationships in the data, especially for situations where the functional form of the relationship is difficult to specify, as well as its ability to handle large data, make it a very useful tool for data modelling and analysis. The choice of MARS over more conventional meth- ods, such as linear regression or ARIMA, is justified by its demonstrated predictive accuracy and robustness in previous studies, as well as its growing momentum as a tool for developing predictive models in complex data mining applications and challenging scenarios (Nayana et al., 2022). Dias & Rocha (2019) tested different mod- elling approaches for wheat price forecast in the USA, one of which was MARS, using only past values of the time series. Their study concluded that the most con- sistent results were obtained with MARS. Nayana et al. (2022) used MARS to predict wheat yield in India. The results showed that MARS was an effective approach for predicting wheat yield, achieving high levels of accu- racy. Jambekar et al. (2018) conducted a study aimed at predicting crop production using different data mining techniques in India, exploring datasets containing infor- mation on the production of wheat, climatic variables, and area under cultivation. Their results demonstrated that MARS was the most effective technique for predict- ing crop production. Overall, these studies highlight the growing relevance of MARS in agricultural research, particularly in settings where the underlying data rela- tionships are difficult to specify. Its proven effectiveness reinforces its suitability for the present study, which deals with forecasting under structural and historical complexity. By supporting more accurate predictions, MARS contributes to better-informed decisions in crop management, resource allocation and policy design. FORECASTING MODELS Per capita consumption The consumption of a given product depends on many variables, such as price, alternatives, availabil- ity and per capita income. Based on the available data and given the lack of additional information allowing a more detailed analysis of the evolution of consumption, it is assumed that the historical values of the series rep- resent, in an integrated manner, the set of variables that influence consumption. Any structural change in the Portuguese economy that has occurred during the peri- od from 1920 to 2021, with a significant impact on the variation in consumption, is reflected in the historical records of total consumption. This approach is therefore considered the most suitable for generating viable and realistic forecasts, within the limitations of the available data. These values can therefore be used as a basis for forecasting values. In other words, MARS was employed to predict per capita consumption using an autoregres- sive model, which is a model that uses past values of the dependent variable as forecasting variables. As in any autoregressive model, it is necessary to decide which past values will be considered to estimate the values of the future series (the figure in the future period will be calculated based on known past values from t – n1 to t – n2). The choice of the past values is made automatically by an optimization algorithm that studies the different possibilities, choosing the one that leads to a smaller prediction error considering the data. The sliding window concept is used, in which it is con- sidered that not all the data of the series are known, pre- dicting these values based on the lag interval to be eval- uated. As the aim is to estimate the period from 2022 to 2032, while forecasting each year individually, multiple forecasting models were developed, one for each year under analysis. The lagged values that may be the best for predicting consumption figures for next year may not be the best for predicting two years into the future. To create these models, we resorted to existing libraries that implement these methods in the R studio software (Dias & Rocha, 2019). The predictions of the per capita con- sumption are represented in Figure 6. As expected, the predictions point to an increase in per capita consumption. Even with some setbacks and a somewhat uneven evolution, per capita consumption will end 2032 at 156.97 kilos consumed per inhabitant. These values were calculated in kilos per inhabitant, but the total figure is measured in tonnes so that a comparison can be made with production values. Obtaining these values for the per capita consumption forecast and com- bining them with the population forecast for Portugal, we calculated the value of national consumption, which is presented in Table 1. We can see that the population in Portugal will decrease over the period analysed, which is in line with the trend of recent decades. Despite this, as we can see from the figures presented, wheat consumption in Portu- gal is expected to continue to increase. In 2021, national wheat consumption was approximately 1 335 million tonnes. This figure, compared to the range of values forecasted up to 2032, shows that despite the decrease in population, total consumption in Portugal will increase by around 18% over this period. 57 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Rebuilding strategies for food self-sufficiency Productivity per hectare In the case of the model for productivity per hectare, the use of MARS was applied differently. As mentioned previously, 8 districts accounted for 92% of the produc- tion over the last century. Thus, we will estimate the productivity per hectare of these districts individually, providing a way for determining the districts where it is most advantageous to concentrate production. In our first attempt, we only considered climatic variables as independent variables: annual temperature (average, maximum and minimum) and precipitation. However, these climatic variables were found to have lit- tle explanatory power with regard to productivity trends. This might be caused by the fact that only annual values of these variables were included, which is not the most optimal level of aggregation, as it does not allow for cap- turing monthly values and variations that have a signifi- cant impact on output. Additionally, technological and biotechnological innovations in agriculture significantly influence yields and productivity per hectare, a factor that is not captured by climatic variables, highlighting the importance of considering technological change in analysing dynamics of productivity over time. Different categories of innovation, including mechanical, biological, chemical, agronomic, and com- puter technologies, have profoundly transformed the structure of the agricultural sector (Sunding & Zilber- man, 2001). After the Second World War, there were major advances in mechanization and biological innova- tions, and fertilizer consumption increased significantly around the world (Federico, 2010). At the beginning of the 21st century, the sector experienced a new techno- logical revolution, driven by advances in biotechnology and precision agriculture (Sunding & Zilberman, 2001). However, while innovation is rightly highlighted as a key driver of productivity growth, it is essential to recognize the practical challenges associated with adopting new technologies, particularly in regions with limited finan- cial resources, aging agricultural populations or under- developed infrastructures. This is particularly evident in Figure 6. Per capita consumption (1920-2032). Source: own processing based on data from the NSI. Table 1. Population, per capita consumption and total consumption (2022-2032). Year Total consumption (ton) Population Per capita consumption (kg/inh) 2022 1 411 132 10 282 222 137.24 2023 1 508 353 10 259 508 147.02 2024 1 498 097 10 235 702 146.36 2025 1 493 358 10 210 995 146.25 2026 1 593 684 10 185 237 156.47 2027 1 568 546 10 158 975 154.40 2028 1 606 847 10 132 084 158.59 2029 1 471 144 10 104 703 145.59 2030 1 637 633 10 076 502 162.52 2031 1 692 462 10 047 864 168.44 2032 1 572 636 10 018 702 156.97 Source: Based on Data from PorData and UN. 58 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Alexandre Macedo João et al. the Portuguese case, where several authors have shown the historical and structural difficulties in adopting agri- cultural innovations (Freire & Lains, 2017, Lains et al., 2024). Despite significant transformations in recent dec- ades, Portugal continues to face challenges, which make it more difficult to apply advanced solutions (Soares, 2005). As such, although innovation is a driver of agri- cultural productivity growth, it is important to always consider the geographical and infrastructural context in which it is implemented. Given that climatic variables alone could not accu- rately predict future productivity and assuming that, with the constant advances in innovation, average pro- ductivity will continue to increase, it was decided to include the year as an explanatory variable. The inclu- sion of this variable captures the progressive effect of time on productivity and therefore captures the impact of innovations. By including the year as an independent variable, models with acceptable explanatory power were obtained. Analysing the results presented in Table 2 reveals that climatic variables have good explanatory power over productivity per hectare in certain districts, such as Bragança, Faro, Lisbon and Santarém. The forecasts dis- played in Table 3 indicate a significant increase in pro- ductivity per hectare over the period analysed. Accord- ing to the trends observed, Beja, Évora, Portalegre, Santarém and Setúbal are the districts with the biggest increases. Not coincidentally, they are all neighbours and belong to the Ribatejo and Alentejo regions, located in the south of Portugal. On the other hand, the districts of Bragança, Faro and Lisbon show a slower evolution and seem to be entering a period of stagnation or even decline in productivity, as can be seen in the last three years of the series. PRODUCTION SCENARIOS This section examines whether it is possible for Por- tugal to achieve wheat self-sufficiency over the analysed period. To assess this possibility, three different scenari- os of cultivated area are considered: – First scenario: the area in 2021 remains constant; – Second scenario: the area corresponds to its maxi- mum value since 1943; – Third scenario: the necessary area to reach the 20% target defined in the NSPCP. By combining the predicted yields per hectare obtained from MARS with the proposed cultivated area in each of these three scenarios, we can determine the potential for wheat production in Portugal. Table 4 shows the values for the cultivated area for each scenar- io. In the case of the third scenario, since the sown area was calculated based on the productivity figures predict- ed for the 2022-2032 period, an average was calculated to provide a single approximate value that represents the Table 2. Explanatory power of climatic variables. Districts Explanatory power of climatic variables for predicting productivity Beja 74.2% climatic variables are not significant Bragança 55.8% maximum and average temperature are significant Évora 74.2% climatic variables are not significant Faro 76.1% all climatic variables are significant Lisbon 84.8% maximum temperature is significant Portalegre 64.1% climatic variables are not significant Santarém 86.6% maximum temperature is significant Setúbal 80.1% climatic variables are not significant Source: author’s calculation using Rstudio. Table 3. Prediction of productivity per hectare per district (T/Ha) (2021-2032). Year Beja Bragança Évora Faro Lisbon Portalegre Santarém Setúbal 2022 3.14 1.74 3.36 1.13 0.81 2.93 3.58 4.49 2023 3.32 1.81 3.55 1.42 0.53 3.09 3.63 4.84 2024 3.49 1.87 3.74 1.38 0.87 3.25 3.84 5.19 2025 3.67 1.93 3.92 1.58 0.90 3.42 3.92 5.53 2026 3.85 1.99 4.11 1.44 0.93 3.58 4.06 5.88 2027 4.02 2.06 4.30 1.58 0.97 3.74 4.15 6.23 2028 4.20 2.12 4.49 1.37 1.00 3.91 4.28 6.58 2029 4.38 2.18 4.67 1.63 1.03 4.07 4.37 6.93 2030 4.56 2.25 4.86 1.43 1.06 4.23 4.46 7.28 2031 4.73 2.31 5.05 1.50 1.09 4.39 4.48 7.62 2032 4.91 2.37 5.24 1.19 1.02 4.56 4.59 7.97 Source: author’s calculation using Rstudio. 59 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Rebuilding strategies for food self-sufficiency area needed to achieve 20% self-supply over the entire period. Table 4 clearly show that Beja stands out as the district with the largest cultivated area across all scenar- ios, followed by Évora and Portalegre. Lisbon, Setúbal, and Faro consistently show the smallest cultivated areas. The second scenario, which represents the maximum expansion, presents values significantly higher than the other two. First scenario Agricultural production dynamics have undergone significant changes over the past decades. In several countries, the area allocated to agriculture has declined and the workforce in the sector has also fallen signifi- cantly (Federico, 2010). Portugal has followed this trend, as evidenced by the data from the last few decades. Increasing production depends largely on expanding the agricultural land and as wheat is considered a very demanding crop in terms of natural resources, namely fertile soils, expanding the cultivated area is a huge chal- lenge. In 2021, the area allocated to wheat production in the 8 districts was 27 374 hectares, corresponding to 95% of the entire cultivated area. This number is one of the lowest of the last century, being only higher than the values recorded in 2018 and 2019. The figures for the cultivated area with wheat in 2021 were used to estimate the amount of output that can be achieved. The production numbers recorded in Table 5 show a continuous increase during the series, which can be attributed to productivity gains driven by innovations, as mentioned earlier. Despite having the third smallest cultivated area in the last century, the increase in pro- duction, even with the land remaining constant, dem- onstrates Portugal’s productive potential. Given that the highest self- supply figure achieved was 8% in 2032, the results show that Portugal does not have the capacity to guarantee 20% self-supply, which means that it can- not meet the target set by the NSPCP. This indicates that Portugal cannot rely exclusively on innovation advances to increase productivity. It is also necessary to look at the possibility of expanding the cultivated area. Second scenario In the second scenario, production was calcu- lated based on the largest sown area recorded since 1943. Once again, the districts of Beja, Évora and Por- talegre recorded the largest areas, while the districts with the smallest were Bragança and Setúbal. The sum of the figures presented in Table 4 corresponds to 814 734 hectares, a figure higher than that recorded at the peak of the Wheat Campaign, when self-sufficiency was achieved. The importance of mentioning the Wheat Table 5. Production with constant area in tons (2022-2032). Year Beja Bragança Évora Faro Lisbon Portalegre Santarém Setúbal Total Self-Supply 2022 30 730 5 058 23 048 811 625 11 250 4 717 5 271 81 510 5.8% 2023 32 492 5 261 24 351 1 019 409 11 865 4 783 5 682 85 861 5.7% 2024 34 155 5 436 25 655 990 672 12 479 5 059 6 093 90 539 6.0% 2025 35 917 5 610 26 889 1 133 695 13 132 5 165 6 492 95 033 6.4% 2026 37 679 5 785 28 193 1 033 718 13 746 5 349 6 903 99 405 6.2% 2027 39 342 5 988 29 496 1 133 749 14 361 5 468 7 313 103 851 6.6% 2028 41 104 6 163 30 799 983 772 15 013 5 639 7 724 108 197 6.7% 2029 42 866 6 337 32 034 1 169 795 15 628 5 758 8 135 112 722 7.7% 2030 44 627 6 540 33 337 1 026 818 16 242 5 876 8 546 117 013 7.1% 2031 46 291 6 715 34 640 1 076 842 16 857 5 902 8 945 121 268 7.2% 2032 48 054 6 890 35 941 853 787 17 510 6 050 9 357 125 442 8.0% Source: author’s calculation using Rstudio. Table 4. Area cultivated in each district for all three scenarios (Ha). District First Scenario Second Scenario Third Scenario Beja 9 787 275 363 28 967 Bragança 2 907 56 545 9 962 Évora 6 859 149 344 20 369 Faro 717 65 089 3 065 Lisbon 772 71 374 2 861 Portalegre 3 840 86 219 11 418 Santarém 1 318 64 649 4 872 Setúbal 1 174 46 151 3 202 Total 27 374 814 734 84 717 Source: based on data from the NSI and author’s calculation. 60 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Alexandre Macedo João et al. Campaign lies in the fact that it was the period in which the highest levels of self-sufficiency were achieved, dem- onstrating that it is possible to place Portugal in a com- fortable situation in terms of domestic supply. Following the public policies applied by fascist Italy, the Portuguese government launched the Wheat Cam- paign in 1929. It was established due to two main fac- tors: the insufficiency of national production to meet consumption needs and the implementation of pro- tectionist policies by the countries to which Portugal depended on for its supply (Freire, 2008). The measures implemented were successful in the sense that, between the years 1931 and 1935, wheat production exceeded domestic consumption and made it possible to create surpluses (Pais et al., 1978). The highest degree of self- sufficiency recorded was 161.29% in the year 1934. Both testimonies of time and historians recognise that wheat self-sufficiency was difficult to achieve in the following decades. One of the reasons that explains this fall in production in the decades following the Wheat Campaign is the fact that most of the incentives granted by the Portuguese state during the campaign was pri- marily aimed at increasing the cultivated area (Freire, 2008), rather than promoting investment in innova- tion and new cultivation techniques. However, with the implementation of new technologies that have altered the structure of the agricultural sector and increased pro- ductivity, it is relevant to compare the figures recorded during the Wheat Campaign with those of this second scenario, since self-sufficiency is achieved in both. According to the data presented in Table 6, produc- tion is significantly higher than consumption forecasts, which suggests that Portugal would be able to achieve self-sufficiency if it recuperated the area that was once allocated to growing wheat. In 2022, Portugal would reach an output corresponding to 162% of consump- tion and in 2032, this figure increased to 221%. How- ever, it is unlikely that this land can be repurposed for wheat production due to the occupation of much of it by urban uses, more profitable crops or because it no longer has the required fertility levels (van Vliet et al., 2017). During the period in which these maximum areas were recorded, it was clear that they were driven by strong state support, which encouraged producers to occupy their land with wheat. However, given the comparative disadvantages and the fact that the agricultural sector is no longer governed by national policies, but by the Com- mon Agricultural Policy (CAP), this type of guideline is no longer available and probably won’t be in the future. Third scenario In the third scenario, it was tested the situation in which Portugal reaches the 20% target of self-supply. In 2017, the Portuguese Ministry of Agriculture, For- estry and Rural Development announced the creation of the NSPCP, which defined several mains goals, like the increase of cereal production, the reduction of external dependence and the achievement of a stronger and more efficient agricultural sector in the period between 2018- 2023. The strategy was based on three pillars: strength- ening the role of producer associations, focusing on innovation and transfer of knowledge, and better organi- zation along the production chain (Barreiros, 2018). The production of the districts was calculated based on the percentage of total national production achieved by each district in 2021, and is shown in Table 7. According to the results, the total wheat production required to meet the self-supply target in 2032 is approx- imately 10% higher than in 2022, primarily due to the Table 6. Production with the maximum area in tons (2022-2032). Year Beja Bragança Évora Faro Lisbon Portalegre Santarém Setúbal Total Self-Supply 2022 864 640 98 388 501 796 73 551 57 813 252 622 231 443 207 218 2 287 471 162% 2023 914 205 102 346 530 171 92 426 37 828 266 417 234 676 223 371 2 401 441 159% 2024 961 017 105 739 558 547 89 823 62 095 280 212 248 252 239 524 2 545 208 170% 2025 1 010 582 109 132 585 428 102 841 64 237 294 869 253 424 255 215 2 675 728 179% 2026 1 060 148 112 525 613 804 93 728 66 378 308 664 262 475 271 368 2 789 089 175% 2027 1 106 959 116 483 642 179 102 841 69 233 322 459 268 293 287 521 2 915 968 186% 2028 1 156 525 119 875 670 555 89 172 71 374 337 116 276 698 303 674 3 024 988 188% 2029 1 206 090 123 268 697 436 106 095 73 515 350 911 282 516 319 826 3 159 659 215% 2030 1 255 655 127 226 725 812 93 077 75 656 364 706 288 335 335 979 3 266 447 199% 2031 1 302 467 130 619 754 187 97 634 77 798 378 501 289 628 351 671 3 382 504 200% 2032 1 352 032 134 012 782 563 77 456 72 801 393 159 296 739 367 823 3 476 585 221% Source: author’s calculation using Rstudio. 61 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Rebuilding strategies for food self-sufficiency projected increase in national consumption. This trend underscores the importance of continuing to invest in strategies to boost wheat production, as the target will continue to rise steadily. In contrast, the required cul- tivated area gradually decreases over the same period, reflecting the positive impact of innovation-driven gains in productivity. DISCUSSION Portuguese productive capacity As other European countries, from the second half of the 19th century onwards (Federico, 2010; Lains et al., 2024), Portugal experienced growing State intervention, particularly in the most important agricultural subsec- tors for human consumption, like cereals. The Wheat Campaign is part of this trend of national protectionist policies (Freire, 2008). In Portugal, this kind of policies lasted until the country’s entry into the European Eco- nomic Community (EEC), in 1986, where the CAP came into effect. The policy changes had a very particular impact on wheat, because in the 20th century it was the crop that deserved the most protection (Freire & Lains, 2017). The implementation of the CAP changed the structure of the Portuguese agricultural sector and has been considered detrimental to the sector’s evolution. Indeed, it can be observed that agriculture has struggled to generate new dynamics capable of counteracting the structural impacts of the CAP reforms, notably failing to devise development strategies, engage new stakeholders, and harness productive potential (Cunha, 2010). Follow- ing the decline in incentives for wheat cultivation, out- put sharply declined nationwide, since it was state inter- vention that rendered production economically attractive to farmers. Combining these changes in the orientation of incentives with the opening of international markets and the integration into the EEC, imports were favoured in order to make up for the national consumption deficit. The findings of the second scenario suggest that Por- tugal could become self-sufficient if it manages to recover a large part of the land once used to produce wheat. In fact, the lower degree of self-supply recorded in this sce- nario, 159% in 2023, allows us to understand that there is room for production gains through increases in the cultivated area. However, the assumption that the maxi- mum areas historically cultivated can be recovered does not fully consider current constraints, such as urbaniza- tion, land abandonment, soil degradation and shifts to more profitable crops (van Vliet et al., 2017). These struc- tural changes in land use reduced the feasibility of fully recovering former wheat production areas. This complex- ity must be acknowledged when interpreting the results. However, the analysis also shows that a consistent and stable increase in cultivated area, combined with produc- tivity gains driven by technological innovation, could sig- nificantly improve self-supply. Although self-sufficiency is unlikely to be achieved in the short term due to climatic, environmental and political constraints, the third sce- nario emerges as a more realistic benchmark. To be more specific, the year in which the area necessary to reach 20% is lowest is 2032, and to reach that point it would be necessary to increase the area by 44 300 hectares, an increase of 162% over the 2021 figure. Another significant takeaway from the analysis of the results is the identification of the most favourable districts for wheat production. It has already been men- tioned that most of the Portuguese territory does not have favourable conditions for wheat production and Table 7. Production required to achieve 20% self-supply in tons (2022-2032). Year Beja Bragança Évora Faro Lisbon Portalegre Santarém Setúbal Total 2022 104 573 18 546 78 573 3 929 2 340 38 346 18 221 17 698 282 226 2023 111 779 19 823 83 986 4 200 2 502 40 988 19 476 18 918 301 671 2024 111 019 19 689 83 415 4 171 2 485 40 709 19 343 18 789 299 619 2025 110 668 19 626 83 151 4 158 2 477 40 580 19 282 18 729 298 672 2026 118 102 20 945 88 738 4 437 2 643 43 306 20 578 19 988 318 737 2027 116 239 20 614 87 338 4 367 2 601 42 623 20 253 19 672 313 709 2028 119 078 21 118 89 471 4 474 2 665 43 664 20 748 20 153 321 369 2029 109 021 19 334 81 915 4 096 2 440 39 977 18 995 18 451 294 229 2030 121 359 21 522 91 185 4 559 2 716 44 501 21 145 20 539 327 527 2031 125 422 22 243 94 238 4 712 2 807 45 991 21 853 21 227 338 492 2032 116 543 20 668 87 566 4 379 2 608 42 735 20 306 19 724 314 527 Source: author’s calculation using Rstudio. 62 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Alexandre Macedo João et al. that only a few regions are capable of meeting the neces- sary conditions to produce it efficiently (Freire & Lains, 2017). The Alentejo and Ribatejo regions, composed by five districts (Beja, Évora, Portalegre, Santarém and Setúbal), emerge as the most productive zones. Setúbal, which is the district with the highest productivity, should have production concentrated mainly in munici- palities outside the Setúbal peninsula, due to the high residential density in this zone, which are a part of the Lisbon metropolitan area. The district of Santarém, with the second highest productivity, has historically concen- trated production in the municipalities located on the left bank of the Tagus river, which is close to the Alente- jo region. Although Beja, Évora and Portalegre have lower productivity levels than these two, they are still the districts with the highest concentration of produc- tion and should continue to be prioritised. During sev- eral centuries, wheat was the main crop of the Alentejo region (Faísca, 2019). In fact, “government policies were, since 1899, contributing for the region to consolidate the image of Portugal’s granary” (Freire, 2008, p. 31). As a result, wheat cultivation has emerged as an activity which exercised a substantial influence on soil degrada- tion and played a significant role in the state of desertifi- cation within the region. The scenario based approach and policy context The scenario-based approach used in this study can be easily adapted to other national contexts and crop types, as long as country-specific data on cultivated area, production figures and climatic variables are available. By defining alternative scenarios of land allocation and productivity improvements, the same framework can simulate the evolution of production potential of other cereals such as maize, barley or oats. For example, apply- ing this approach to maize in the southern countries of Eastern Europe is particularly relevant, as maize is one of the most important cereal crops for this region, occu- pying a significant share of cultivated land and playing a central role in consumption (Grčak et al., 2020). From a political point of view, integrating scenario results with existing agricultural strategies improves decision-making at various levels of governance. In the EU, for example, scenario modelling can serve as a basis for CAP interventions and support the “Farm to Fork” strategy, which aims to make food systems fair, healthy and environmentally friendly (Wesseler, 2022). Nation- al authorities can then prioritize incentive systems, to encourage farmers to expand cultivation in environmen- tally sustainable ways or to rotate crops to maintain soil quality. By linking this method directly to policy instru- ments, future research can provide tailored recommen- dations that support sustainable intensification, crop diversification and resilience building ensuring that sce- nario-based planning becomes a relevant indicator for agricultural policymaking. Although this study focuses mainly on consump- tion and production scenarios, it is important to recog- nize the broader economic context in which is inserted. Market dynamics, including price volatility and global imbalances between supply and demand, can have a significant impact on both domestic production incen- tives and the level of wheat imports. A clear example of this, as mentioned earlier, is the armed conflict on the Ukrainian territory. The disruptions in the produc- tion cycle and in the supply chain resulting from this conflict have made a significant impact on the market dynamics of wheat on the international stage (Zhang et al., 2024). Similarly, international trade policies, such as tariffs or subsidies, also play a critical role in shaping these dynamics. In the context of the EU, the CAP and the trade agreements with third parties have a profound influence on grain markets (Wieliczko, 2017). These fac- tors collectively shape the economic environment in which agricultural decisions are made and must be con- sidered when assessing the viability and sustainability of production strategies. Any strategy aimed at achieving national self-suffi- ciency in wheat must be conceived as a long-term, care- fully planned and environmentally sustainable effort. The Wheat Campaign, although it led to self-sufficiency, proved to be economically and ecologically unsustain- able and cannot be repeated. Intensive cultivation led to serious erosion and degradation of soil quality due to over-exploitation, particularly in the Alentejo, reflect- ing a lack of planning on the part of policymakers who ignored these factors (Freire, 2008). In addition, much of the soil in Portugal is unsuitable for growing wheat, resulting in chronically low yields and high production costs due to heavy dependence on fertilizers (Faísca, 2019). Consequently, any future efforts should prioritize the expansion of wheat into the most suitable areas. To ensure that any increase in production aligns with sus- tainability goals, expansion must be accompanied by environmental safeguards that mitigate soil degradation, conserve water resources and preserve biodiversity. This implies the adoption of soil conservation practices and the adoption of technologies to optimize the use of pro- duction factors. Integrating these measures into national policy frameworks will be crucial to balance productiv- ity objectives with long-term ecological resilience and to ensure that the quest for self-sufficiency adheres to the principles of sustainable agriculture. 63 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Rebuilding strategies for food self-sufficiency Limitations and further research The work carried out and described in this study has some limitations that should be acknowledged when interpreting the results. One fundamental limitation concerns the relatively weak explanatory power of the climatic variables in the model, particularly in relation to predicting productivity levels. However, this should not be interpreted as proof that climate variables are insignificant in determining agricultural results. Nev- ertheless, aggregating data over large temporal and spa- tial scales can hide more nuanced interactions between weather patterns and crop output (Srikanthan & McMa- hon, 2001). By relying on annual figures, the analysis fails to capture the timing and intensity of key climatic events, such as rainfall during the main growth phases or temperature extremes during sensitive periods of the crop cycle, which are known to have substantial effects on yields (Gouveia & Trigo, 2008). In this regard, the use of weekly, monthly, or even seasonal climatic data would allow for a much more precise understanding of when and how climate variables influence production (Gao et al., 2023). As such, the lack of significant explanatory power in the current model probably reflects a limita- tion in the availability of the data rather than the actu- al influence of weather on production. Future research could benefit significantly from incorporating more granular climate data, both spatially and temporally, to better understand the specific conditions under which climate variability affects agricultural productivity. To resolve this constraint, the model was adjusted to include the year as an explanatory variable. This choice allowed MARS to capture the temporal evolution exist- ing in our dataset, which gave it the ability to generate stable and reliable results. More importantly, this inclu- sion revealed that productivity trends are also signifi- cantly influenced by technological innovations, which were not previously considered. By introducing the year into the model, we were able to indirectly reflect the cumulative effect of these innovations over time, from mechanization and the use of fertilizers to the adoption of biotechnology and precision agriculture (Federico, 2010). Although this approach does not isolate specific technologies, it does incorporate their overall influence on output. The resulting models, which include year as a variable, achieved acceptable levels of explanatory power and improved predictive capacity. CONCLUSION In conclusion, an increase in wheat production not only depends on productivity improvement brought by innovations, but also on the increase of the cultivated area. If it remains constant, the maximum degree of self- supply that can be achieved is 8% in 2032, a figure not very different from the value of 6,1% recorded in 2021, which is very far from the 20% target. Considering the circumstances in which the periods of self-sufficiency occurred, it can be observed that an increase in produc- tion would require an extension of the cultivated area, which may become possible if incentives are created. The scenario-based approach used not only pro- vides policymakers with informed guidance on realis- tic and sustainable strategies for optimizing production efficiency but also provides a form of sensitivity analysis of the MARS method used for forecasting. While the MARS method has strengths, such as the ability to mod- el non-linear relationships and to select the most effi- cient variables automatically, it also has limitations, such as sensitivity to missing data and high computational requirements for large data sets. Despite these limita- tions, MARS remains a valuable tool due to its ability to produce understandable models and identify influential forecasting factors. However, to ensure reliable and accu- rate forecasts, it is advisable to complement it with other models and validation techniques. The use of MARS in this paper proved very efficient and it emerges as a powerful tool for modelling and pre- dicting wheat production and consumption. It allowed us to work with a very extensive database and, by using its ability to capture nonlinear relationships and inter- actions among multiple predictors, it provided a robust framework for forecasting. The fact that it identifies and captures complex relationships in data, which would not be possible to unfold if we had used a linear model, was fundamental to this research. For a strategy to be drawn up and implemented suc- cessfully in any sector of the economy, it is necessary to carry out studies and analyses of current conditions and past performance in order to establish reliable objectives. Based on historical data of variables that directly impact wheat production, our analysis was made to provide us with information on the productive capacity of the main agrarian regions. The fact that we limited ourselves to values that were previously recorded gave us the ability to state that this study is reliable and realistic, because these values have been achieved in the past. As such, this study is of great importance to researchers and policymakers to gain insights into the key factors influencing wheat production, enabling informed decision-making and resource allocation. Due to the lack of investment by the Portuguese government, which is still not paying enough attention to the problem of decreasing wheat production, it has not yet been possible to record the figures we had 64 Bio-based and Applied Economics 14(2): 51-65, 2025 | e-ISSN 2280-6172 | DOI: 10.36253/bae-17226 Alexandre Macedo João et al. predicted could be achieved. As agricultural systems face increasing challenges due to climate change and fluc- tuating market conditions, this paper offers a valuable approach for reliable and accurate wheat production pre- dictions, thereby aiding in the formulation of sustainable agricultural strategies. 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