Context Interest in studying heat stress (HS) has increased significantly due to the problems associated with increasing global warming. Heat stress has very destructive effects on the health and performance of livestock. Aims Our objective was to investigate the effects of heat stress on in vivo and in vitro ruminal metabolism in fat-tailed Iranian sheep. Methods Fourteen intact non-lactating and non-pregnant mature fat-tailed Makoei ewes (67.5 ± 2.5 kg BW) were kept indoors for 24 h/day and randomly assigned to HS (33.0–41.0°C and a temperature–humidity index (THI) of ≥83 for 24 h/day) or thermoneutral (TN; 24.5 ± 2.3°C and a THI of 66.1 ± 2.5) condition in two consecutive experimental periods. At the end of first experimental period, the animals in each group were exchanged with another group. The ewes were fed a total mixed ration two times a day, composed of lucerne hay (33%) and corn silage (1:2) to meet their maintenance metabolisable energy and protein requirements. Key results HS ewes had lower dry-matter (DM) intake than did TN ewes (P < 0.05). HS increased the in vivo DM, organic matter (OM) and neutral detergent fiber digestibility (P < 0.05), but crude protein digestibility was not affected. Total volatile fatty acid concentration and pH were not affected by HS. However, propionate molar percentage was increased and N-NH3 concentration was decreased by HS. In vitro gas production of three different tested feeds was lower in rumen fluid collected from HS than that from TN group, but DM and OM digestibility and methane emission were decreased only in the case of Orchard grass (P < 0.05). Conclusions and implications In general, HS had detrimental effects on DM intake and in vitro nutrient digestibility but increased in vivo nutrient digestibility, and changed microbial population.

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Wheat plays a vital role in the food security of society, and early estimation of its yield will be a great help to macro-decisions. For this purpose, wheat yield and water productivity (WP) by considering soil data, irrigation, fertilizer, climate, and crop characteristics and using a novel hybrid approach called hazelnut tree search algorithm (HTS) and extreme machine learning method (ELM) was examined under the drip (tape) irrigation. A dataset including 125 wheat yield data, irrigation and meteorological data of Mahabad plain located southeast of Lake Urmia, Iran, was used as input parameters for crop year 2020–2021. Eighty percentage of the data were used for training, and the remaining 20% for model testing. Nine different input scenarios were presented to estimate yield and WP. The efficiency of the proposed model was calculated with the statistical indices coefficient of determination (R2), root-mean-square error (RMSE), normalized root-mean-square error, and efficiency criterion. Sensitivity analysis result showed that the parameters of irrigation, rainfall, soil moisture, and crop variety provide better results for modeling. There was good agreement between the practical values (field management data) and the estimated values with the HTS–ELM model. The results also showed that the HTS–ELM method is very efficient in selecting the best input combination with R2 = 0.985 and RMSE = 0.005. In general, intelligent hybrid methods can enable optimal and economical use of water and soil resources.