Crop production in Türkiye: trends and driving variables

Climate change and a rapidly increasing population boost the pressure on Türkiye’s cropping systems to increase crop production in order to meet rising food demand. It is unknown whether and in which direction trends and variability in harvested area and yield separately affect crop production in Türkiye. The objective of this study was to (1) quantify the long-term (2004–2020) trends of planting/harvested areas, yield and crop production for the 16 vital annual crops in Türkiye, (2) quantify the separate contribution of harvested area and yield on crop-specific production variability and (3) the potential of water and temperature-based remote sensing variables on capturing the variability of harvested areas and yield. The harvested area of the most grown crops (10 out of 16) such as wheat and barley showed a declining trend. However, the yield trend was increased for all of the study crops, which in some cases overcompensated for the decline in the harvested area on crop production. The harvested area showed a more robust explanatory power for production variability than yield except for the crops with higher breeding investments and subsidized by authorities such as wheat and sugar beet. The water-related remote sensing variables and combination of water and temperature variables largely explained the variability of the harvested area in Türkiye. In order to stabilize crop production in Türkiye, better and more efficient water management plans are crucial

Are Supervised Learning Methods Suitable for Estimating Crop Water Consumption under Optimal and Deficit Irrigation?

This study examined the performance of random forest (RF), support vector machine (SVM) and adaptive boosting (AB) machine learning models used to estimate daily potato crop evapotranspiration adjusted (ETc-adj) under full irrigation (I100), 50% of full irrigation supply (I50) and rainfed cultivation (I0). Five scenarios of weather, crop and soil data availability were considered: (S1) reference evapotranspiration and precipitation, (S2) S1 and crop coefficient, (S3) S2, the fraction of total available water and root depth, (S4) S2 and total soil available water, and (S5) S3 and total soil available water. The performance of machine learning models was compared with the standard FAO56 calculation procedure. The most accurate ETc-adj estimates were observed with AB4 for I100, RF3 for I50 and AB5 for I0 with coefficients of determination (R2) of 0.992, 0.816 and 0.922, slopes of 1.004, 0.999 and 0.972, modelling efficiencies (EF) of 0.992, 0.815 and 0.917, mean absolute errors (MAE) of 0.125, 0.405 and 0.241 mm day−1, root mean square errors (RMSE) of 0.171, 0.579 and 0.359 mm day−1 and mean squared errors (MSE) of 0.029, 0.335 and 0.129 mm day−1, respectively. The AB model is suggested for ETc-adj prediction under I100 and I0 conditions, while the RF model is recommended under the I50 condition

Aquaponics in Saudi Arabia: Initial Steps towards Addressing Food Security in the Arid Region

Due to water scarcity and harsh climate, Saudi Arabia and its neighboring countries rely heavily on fresh food imports from distant lands and have higher per capita expenditures on vegetable imports compared with USA and China. Aquaponics can supply fresh food throughout the year and may complement conventional agriculture in Saudi Arabia to help the objectives and policies defined by the government for food and water security. In this spirit, an Aquaponics farm is being constructed in the desert-coast climate to study the feasibility. A detailed SWOT analysis is performed for a commercial farm which reveals that the advantages of Aquaponics in the Saudi market outweigh the weaknesses. Preliminary experiences show that such ventures require high capital costs and synergistic collaboration of engineering, agriculture, business, and geology

Saturated Hydraulic Conductivity Estimation Using Artificial Intelligence Techniques: A Case Study for Calcareous Alluvial Soils in a Semi-Arid Region

The direct estimation of soil hydraulic conductivity (Ks) requires expensive laboratory measurement to present adequately soil properties in an area of interest. Moreover, the estimation process is labor and time-intensive due to the difficulties of collecting the soil samples from the field. Hence, innovative methods, such as machine learning techniques, can be an alternative to estimate Ks. This might facilitate agricultural water and nutrient management which has an impact on food and water security. In this spirit, the study presents neural-network-based models (artificial neural network (ANN), deep learning (DL)), tree-based (decision tree (DT), and random forest (RF)) to estimate Ks using eight combinations of soil data under calcareous alluvial soils in a semi-arid region. The combinations consisted of soil data such as clay, silt, sand, porosity, effective porosity, field capacity, permanent wilting point, bulk density, and organic carbon contents. The results compared with the well-established model showed that all the models had satisfactory results for the estimation of Ks, where ANN7 with soil inputs of sand, silt, clay, permanent wilting point, field capacity, and bulk density values showed the best performance with mean absolute error (MAE) of 2.401 mm h−1, root means square error (RMSE) of 3.096 mm h−1, coefficient of determination (R2) of 0.940, and correlation coefficient (CC) of 0.970. Therefore, the ANN could be suggested among the neural-network-based models. Otherwise, RF could also be used for the estimation of Ks among the tree-based models

Aquaponics in Saudi Arabia: Initial Steps towards Addressing Food Security in the Arid Region

Due to water scarcity and harsh climate, Saudi Arabia and its neighboring countries rely heavily on fresh food imports from distant lands and have higher per capita expenditures on vegetable imports compared with USA and China. Aquaponics can supply fresh food throughout the year and may complement conventional agriculture in Saudi Arabia to help the objectives and policies defined by the government for food and water security. In this spirit, an Aquaponics farm is being constructed in the desert-coast climate to study the feasibility. A detailed SWOT analysis is performed for a commercial farm which reveals that the advantages of Aquaponics in the Saudi market outweigh the weaknesses. Preliminary experiences show that such ventures require high capital costs and synergistic collaboration of engineering, agriculture, business, and geology