To make potato production more sustainable for smallholder farmers, product diversification through processing is critical. On the other hand, the processing sector mandated some stringent standards in order to maintain product quality, hence potato quality evaluations are required for chip processing industries. Specific gravity, starch, reducing sugars, tuber dry matter, and chip color are all important characteristics to consider for processing. This study was designed to find suitable potato varieties with satisfactory attributes for processing chips, in comparison with other processing potato varieties. Keeping this in mind, the study was performed during the winter season of 2019–2020 and 2020–2021 in Bangladesh. Six processing types of potato varieties viz. BARI Alu-25 (Asterix), BARI Alu-28 (Lady Rosetta), BARI Alu-29 (Courage), BARI Alu-68 (Atlantic), BARI Alu-70 (Destiny), and BARI Alu-71 (Dolly) were evaluated for different quality parameters (specific gravity, dry matter, starch content, reducing sugar, chip color, and crispiness). The result of the study showed an ample variation in the studied parameters among the potato varieties. Among the varieties, BARI Alu-28 (Lady Rosetta) and BARI Alu-29 (Courage) were found better for all the quality parameters, such as dry matter, starch, reducing sugar, chip color, and crispiness, and could be recommended for the processing industries.

Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods.

Potassium (K) is an essential macronutrient, affecting numerous physiological, biochemical, and metabolic functions in plants. In protected cropping systems (PCS), which are controlled environments for intensive agriculture, optimizing K management is essential for attaining sustainable productivity and resilience under stressful growth conditions. Understanding plant responses to these conditions requires advanced analytical approaches, and metabolomics is emerging as a key tool, though there is still a lack of PCS‐focused metabolomic studies. This review synthesizes the recent knowledge on plant metabolomic responses to K as a nutrient, emphasizing the central role of metabolomics in uncovering intricate biochemical pathways associated with K uptake, transport, and utilization. We investigate essential metabolic alterations in response to K deficiency, encompassing modifications in glucose metabolism, antioxidant synthesis, osmolyte accumulation, and hormonal regulation. The review also explores the relationships between potassium and other nutrients, specifically nitrogen, phosphorus, and essential micronutrients, and their impact on total plant metabolic networks. Furthermore, we discuss cutting‐edge omics integration, precision fertigation, real‐time sensor technologies, and machine learning applications that together promise to transform K fertilizer management in PCS. Future directions highlight the advancement of K‐efficient cultivars, integrating metabolomic biomarkers in breeding, and overcoming challenges in data interpretation, scalability, and the high cost of metabolomic analyses and phenotyping technologies. Collectively, these insights provide a framework for improving crop health, production, and nutrient utilization efficiency for a more sustainable future in protected cropping.

Aims: The main aim of this study was to find out the salinity tolerant varieties of potato and to evaluate the growth and yield performance of potato varieties under different salinity level. Study Design: The experiment was laid out in the two factor completely randomized design (CRD) with the three replications. Place and Duration of Study: A pot experiment was conducted at the Horticulture Research field and Horticulture Laboratory of Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh during December 2011 to April 2012. Methodology: The possible combination of the ten potato varieties (Diamant, Lady Rosetta, Provento, Felsina, Granola, Asterix, Cardinal, Sagita, Shilbilati and LalPakri) and the four levels of salt (NaCl) (S_o= 0.50 dS/m, S₁= 3.25 dS/m, S₂= 6.95 dS/m and S₃= 8.90 dS/m) were considered as the treatment variables. There were 120 earthen pots and the tubers of selected genotypes were planted in each pot. After 20 days of planting treatment was applied and irrigated every two days interval. 2 ds/m and 6 ds/m saline water was used to raise 0.5 ds/m and 3.25 ds/m respectively. To reach 6.95 ds/m first fifteen days pots were irrigated at 6 dS/m and then 8 ds/m at 2 days interval. Similarly, pots were irrigated at 6 ds/m (first 7 days) and 8 ds/m (next 7 days) and then 12 ds/m to reach 8.90 ds/m at the same interval to avoid osmotic shock. Finally, the electrical conductivity of the soil against treatment reached and measured by Electrical Conductivity meter. It was taken 30 days to raise the soil salinity up to the treatment. These levels of salinity were maintained up to final harvest. After harvesting some important physiological, and yield contributing attributes such as chlorophyll content, membrane leakage, ion uptake and tuber weight were measured. Results: In respect of leaf chlorophyll content, membrane leakage, per plant tuber weight, and ion uptake were better in Sagita and Felsina. The variety Sagita followed by Felsina had less membrane injury upto 32.14% where as Shilbilati and Lalpakri exhibited upto 69.45% membrane injury at 6.95 dS/m of salinity. At the same time, Sagita gave the highest yield followed by Felsina, Lady Rosetta and Provento. Conclusion: Finally, the present results revealed that variety Sagita and Felsina responded well under salinity stress condition. Lady Rosetta and Provento performed moderately at 6.95 dS/m. The varieties Shilbilati and Lalpakri were salt sensitive among the selected genotypes.

A cropping system that is based on three or four crops is currently a widely favored option for augmenting crop productivity to address the escalating global food demand. However, the improper fertilizer management and undue tillage adversely impacts both the productivity of crops and the fertility of the soil. A research investigation was conducted on tillage and nutrient management within the mustard-mungbean-Transplanting aus (T.aus)-Transplanting aman (T.aman) cropping system to examine the impact of fertilizer packages and tillage techniques on the overall productivity of cropping systems, as well as the condition of the soil in grey terrace soil. The research included tillage techniques viz; minimum tillage (MT), conventional tillage (CT) and deep tillage (DT); while nutrient management; NM₁: 100 % STB (Soil test based) following FRG (Fertilizer Recommendation Guide-2018), all from chemical fertilizer, NM₂: 125 % of STB following FRG- 2018, all from chemical fertilizer, NM₃: 100 % STB (80 % from chemical fertilizers and 20 % from cowdung), and NM₄: Native fertility (no fertilization). A total of twelve treatments replicated three times following the factorial completely randomized design for three consecutive seasonal years (2018–19, 2019–20, and 2020–21). MT outperformed DT and CT in terms of crop yield, rice equivalent yield (REY), system productivity (SP), and production efficiency (PE). Moreover, NM₃ exhibited enhanced performance in terms of agricultural productivity measures. Field capacity (FC), soil organic matter (OM), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), and soil nutrients (N, P, K, S, Zn and B) observed an enhancement as a result of the implementation of tillage MT and nutrition package NM₃. The investigation indicates that implementing minimum tillage (MT) coupled with an integrated plant nutrition system package (NM3) can assist in the improvement of soil and the enhancement of crop productivity.