Context or problem: Available phosphorus (P) management is a continuous task in wheat-based systems of the UK, primarily to balance applying enough P to support high yields while avoiding unnecessary costs and damaging losses to the environment by applying too much. Objective or research question: Grain P concentration with a corresponding threshold value of 0.32 % has been proposed as a new method for P management, supporting or replacing soil test-based evaluations. The objective of this study was to investigate if this approach was a reliable option. Methods: We used data from the long-term Exhaustion Land experiment on the Rothamsted Farm in southeast England, to investigate the relations between winter wheat grain yield, grain P concentration, and Olsen P values in winter wheat over the last 32 years. Results: Our results show that maximum grain P concentrations in high yielding years are much lower than in low yielding years, indicating a dilution effect through high assimilate transfer to grains. We could not confirm a lower threshold of 0.32 % grain P as an indicator of crop P deficiency at high yields, in our trial the value was closer to 0.24 % grain P. The Olsen P test at our site was a good indicator of P response, and the Olsen P threshold value of 20 mg P kg-1 was sufficient to support the highest yields of winter wheat. Implications or significance: We conclude that P recommendations for cereals should continue to be based on soil Olsen P values, possibly supported by better estimations of P exports using grain analysis. Evaluation of the suitability of grain P concentration as a tool for P fertilizer management in cereal based systems would require more research. In the future, the existing Olsen P Index classes in the current UK Nutrient Management Guide ('RB209') should be reviewed to possibly increase P fertilizer use efficiency and reduce P losses to the environment whilst maintaining current production levels.

Prolonged and excessive use of chemical fertilizers has resulted in serious harm to soil health and ecosystems. This study aimed to reduce the cultivation costs for apricot trees, nearly 1/3(rd) of which are spent on fertilizers. The research was conducted on fully grown apricot trees of the cultivar New Castle, in the Solan district of Himachal Pradesh, India. The experiment consisted of fourteen treatment combinations evaluated in triplicate and statistically analyzed using a randomized block design (RBD). Results revealed that treatment T-12 [50% Nitrogen (Calcium Nitrate) + 50% Nitrogen (Urea) + Azotobacter + Phosphate Solubilizing Bacteria + Vermicompost] resulted in the highest percent increase in tree trunk girth (6.82%), highest leaf chlorophyll content (3.00 mg g(-1) fresh weight), leaf area (58.29 cm), fruit set (61.00%) and total yield (61.9 kg tree(-1)). In terms of nutrient status, T-12 had the highest leaf N (2.95%), leaf K (2.60%), soil N (386.33 kg ha(-1)), soil P (51.00 kg ha(-1)) and soil organic carbon (1.81%). The highest net return and profit over recommended dose of fertilizers (RDF) was also recorded in treatment T-12. The results of this study show that judicious fertilizer use along with integrated organic manure and bio-fertilizers can reduce cultivation costs, improve soil health, and increase fruit production with minimum ecosystem damage.

The traditional method of detecting crop nutrients is based on the direct chemical detection method in the laboratory, which causes great damage to crops. In order to solve the above problems, the main goal of this study is to design a precise fertilization method for greenhouse vegetables based on the improved back-propagation neural network (IM-BPNN) algorithm to increase fertilizer utilization efficiency, reduce production costs, and improve the economic viability of agriculture. First, soil samples from the farm in china are selected. With the laboratory treatment, available phosphorus, available potassium, and alkaline nitrogen are extracted. These data are preprocessed by the z-score (zero-mean normalization) standardization method. Then, the BPNN (backpropagation neural network) algorithm is improved by being trained and combined with the characteristics of the dual particle swarm optimization algorithm. After that, the soil sample data are divided into training and test sets, and the model is established by setting parameters, weights, and network hierarchy. Finally, the NBTY (nutrient balance target yield),BPNN (backpropagation neural network) and IM-BPNN algorithm are used to calculate the amount of fertilizer. Compared with the BPNN and NBTY algorithm, it shows that the IM-BPNN algorithm can more accurately determine the amount of fertilizer required by vegetables and avoid over-application, which can improve fertilizer utilization efficiency, reduce production costs, and improve the economic feasibility of agriculture.