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Rice is the major staple crop worldwide, whereas fertilization practices include mainly the application of synthetic fertilizers. A novel compost was developed using 74% of rice industrial by-products (rice bran and husks) and tested in rice cultivation in Greece’s main rice producing area. Field experimentation was conducted in two consecutive growing seasons (2017 and 2018) and comprised six fertilization treatments, including four compost rates (C1: 80, C2: 160, C3: 320 kg ha−1 of nitrogen all in split application, C4: 160 kg ha−1 of nitrogen in single application), a conventional treatment, as well as an untreated control. A total of 21 morpho-physiological and quality traits were evaluated during the experimentation. The results indicated that rice plants in all compost treatments had greater height (8%–64%) and biomass (32%–113%) compared to the untreated control. In most cases, chlorophyll content index (CCI) and quantum yield (QY) were similar or higher in C3 compared to the conventional treatment. C2 and C3 exhibited similar or greater yields, 7.5–8.7 Mg ha−1 in 2017 and 6.3–6.9 Mg ha−1 in 2018, whereas the conventional treatment resulted in 7.3 Mg ha−1 and 6.8 Mg ha−1 in the two years, respectively. No differences were observed in most quality traits that affect the rice commodity. The current study reveals that in sustainable farming systems based on circular economy, such as organic ones, the application of the proposed compost at the rate of 6 Mg ha−1 can be considered sufficient for the rice crop nutrient requirements.

The goal of this study is to assess the strength and mode of price dependence by time scale, among the extra virgin olive oil markets of Italy, Spain, and Greece. These three Mediterranean countries are responsible for 95% of olive oil production within the European Union and they account for more than 50% of the olive oil exports worldwide. For the empirical analysis, monthly prices from the aforementioned countries are utilized along with the tools of discrete wavelets and non-parametric copulas. Results indicate that (a) price linkages in the short run are significantly different from those in the longer run, with price dependence being stronger in the longer run, and (b) in the very long run, price shocks of the same sign but of different magnitude are transmitted from Italy to Spain with a higher probability than they are transmitted from Italy to Greece. Accordingly, the time scale affects the intensity as well as the pattern of dependence, pointing this way to asymmetric price co-movement. Regarding the integration of the three markets, the finding of asymmetric co-movement is not consistent with well-integrated markets.

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

The present study estimates the degree of market power in the major US beef and pork export destinations. The recently developed stochastic frontier (SF) estimator is used. Estimations of market and time-specific Lerner indices are provided. Balanced panel data between 1980 and 2011 were employed. The average Lerner index is 39% for the US beef exports and is the highest in the markets of ASEAN, Hong Kong/China, Japan, South Korea, and Taiwan. For the US pork exports, the average Lerner index is 16% and is the highest in the markets of Mexico and Taiwan.

Land use transformation, the longest-standing human-driven environmental alteration, is a pressing and complex issue that significantly impacts European landscapes and contributes to global environmental change. The urgency to act is reinforced by the European Environment Agency (EEA), which identifies industrial, commercial, and residential development—particularly near major urban centers—as key contributors to land take. As the EU sets a vision for achieving zero net land take by 2050, assessing the readiness and coherence of national legislation becomes critical. This comprehensive study employs a comparative legal analysis across five European countries—Italy, Greece, Poland, France, and Ukraine—examining their laws, strategies, and commitments related to land degradation neutrality. Using a review of national legislation and policy documents, the research identifies systemic patterns, barriers, and opportunities within current legal frameworks. The present study aims to provide valuable insights for policymakers, planners, and academic institutions, fostering a comprehensive understanding of existing gaps, implementation, and inconsistencies in national land use legislation. Among the results, it has become evident that a typical “pathway” between the examined states in terms of the legislative framework on land use–land take is probably a utopia for the time being. The legislations in force, in several cases, are labyrinthine and multifaceted, highlighting the urgent and immediate need for simplification and standardization. The need for this action is further underscored by the fact that, in most cases, land use frameworks are characterized by complementary legislation and ongoing amendments. Ultimately, the research underscores the critical need for harmonized governance and transparent, enforceable policies, particularly in regions where deregulated land use planning persists. The diversity in legislative layers and the decentralized role of the authorities further compounds the complexity, reinforcing the importance of cross-country dialogue and EU-wide coordination in advancing sustainable land use development.

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Results showed high consistency between estimates and ground measurements, revealing high correlations (R2 > 0.93) and good accuracies (RMSE < 0.83, rRMSEm < 23.6% and rRMSEr < 16.6%) in all cases. Sentinel-2A estimates were compared with Landsat-8 showing high spatial consistency between estimates over the three areas. The possibility to exploit seasonally-updated crop mask exploiting Sentinel-1A data and the temporal consistency between Sentinel-2A and Landsat-7/8 LAI time series demonstrates the feasibility of deriving operationally high spatial-temporal decametric multi-sensor LAI time series useful for crop monitoring.

The objective of this study is to estimate the degree of oligopsony power in the U.S. cattle industry with the use of the recently developed stochastic frontier estimator of market power. Unlike the seminal paper where estimation of the mark-up in an output market at firm level was the main objective, this work proposes a stochastic production frontier estimator in order to estimate the mark-down in an input market at aggregate level. Furthermore, with the help of the new estimator we derive and estimate the Lerner index of oligospony power for the U.S. cattle market. For the empirical part of the study we employed annual time series data from the U.S. cattle/beef industry for the time period 1970-2009. Our results suggest that beef packers exert market power when purchasing live cattle for slaughter.

The objective of this study is to measure the aggregate degree of market power exercised by the US meat packing industry with the employment of the recently developed stochastic frontier estimator (SFA) of market power. Furthermore, the present work shows that the SFA estimation technique can be used in order to measure the sum of oligopsonistic and oligopolistic power along a food supply chain. Annual time series data for the period 1970–2011 were employed. The empirical results reveal that, in the US meat packing industry, the farm-to-wholesale price spread is 3.74% above the marginal processing cost. These findings indicate that rather a small percentage of the farm-to-wholesale price spread can be attributed to market power in the US meat packing sector.

The objective of this study is to assess the degree and the structure of price dependence between different cuts in the US pork industry at the retail level. To this end, it utilizes monthly retail data of pork cuts and the statistical tool of copulas. The empirical results suggest that for all pairs, retail prices are not likely neither to boom nor to crash together, even though overall dependence is quite considerable for two of the three pairs considered in this study. No evidence of asymmetric price co-movements was found.

The objective of this paper is to assess the degree and the structure of price dependence between four different coffee varieties of the Arabicas and Robustas qualities. This is pursued using the statistical tool of copulas and monthly price data for the period 2006-2015. The empirical results indicate: (i) price booms and price crashes are transmitted with different probabilities in five out of six pairs of different coffee varieties, indicating asymmetric price dependence during extreme market upswings/downswings, and (ii) price increases are transmitted faster and more fully than price decreases, since in every pair examined in this study, the upper tail dependence coefficient is higher than the lower tail dependence coefficient. According to the results, coffee producers are more likely to see coffee prices of different varieties of the same and different quality to boom rather than crash together.