Core Ideas Potassium use efficiency in cereals is unknown. World demand for potassium in agriculture is increasing. Potassium is a non‐renewable resource. Worldwide potassium (K) fertilizer use has grown, while the expected fertilizer use efficiency has decreased. The objective of this paper was to estimate potassium use efficiency (KUE) for cereal crops and report on methods that will most likely lead to improved KUE. World KUE was calculated using the total area under cereal production, total cereal grain production, percent K content in cereal grains and K fertilizer consumed from 1961 to 2015. All data was obtained from FAOSTAT except percent K grain content, which was acquired from the USDA. The reported KUE estimate included assumptions established in prior literature. The percent K coming from the soil was estimated at 71%, while previous year K fertilizer‐residual‐effects were offset by knowing that similar amounts of fertilizer K will be applied in following years. At current consumption rates, existing K reserves as K20 are estimated to last 100 yr meaning that mining operations will need to expand to meet expected market demands. Results showed that cereal production increased by a factor of 3.2 from 1961 to 2015 and that was accompanied by a threefold increase in fertilizer K consumed. Estimated KUE from 1961 to 2015 for world cereal crops using the difference method was 19%. Combined with findings from this paper, estimates of N, P, and K use efficiency for cereal production in the world stand at 33, 16, and 19%, respectively.

The second law of thermodynamics states that entropy or randomness in a given system will increase with time. This is shown in science, where more and more biological processes have been found to be independent. Contemporary work has delineated the independence of yield potential (YP0) and nitrogen (N) response in cereal crop production. Each year, residual N in the soil following crop harvest is different. Yield levels change radically from year to year, a product of an ever‐changing and unpredictable/random environment. The contribution of residual soil N for next years’ growing crop randomly influences N response or the response index (RI). Consistent with the second law of thermodynamics, where it is understood that entropy increases with time and is irreversible, biological systems are expected to become increasingly random with time. Consequently, a range of different biological parameters will influence YP0 and RI in an unrelated manner. The unpredictable nature that environment has on N demand, and the unpredictable nature that environment has on final grain yield, dictate the need for independent estimation of multiple random variables that will be used in mid‐season biological algorithms of the future. Core Ideas Randomness in biological systems is increasing. Many biological processes are independent. Yield levels change from one year to the next. Environments change over time and are random. Optimum fertilizer nitrogen rates change dramatically from year to year.

Biochar (B) has shown promise in improving crop productivity. However, its interaction with inorganic nitrogen (N) in temperate soils is not well-studied. The objective of this paper was to compare the effect of fertilizer N-biochar-combinations (NBC) and N fertilizer (NF) on maize (Zea mays L.) grain yield, N uptake, and N use efficiency (NUE). Trials were conducted in 2018 and 2019 at Efaw and Lake Carl Blackwell (LCB) in Oklahoma, USA. A randomized complete block design with three replications and ten treatments consisting of 50, 100, and 150 kg N ha−1 and 5, 10, and 15 Mg B ha−1 was used. At LCB, yield, N uptake, and NUE under NBC increased by 25%, 28%, and 46%, respectively compared to NF. At Efaw, yield, N uptake, and NUE decreased under NBC by 5%, 7%, and 19%, respectively, compared to NF. Generally, results showed a significant response to NBC at ≥10 Mg B ha−1. While results were inconsistent across locations, the significant response to NBC was evident at LCB with sandy loam soil but not Efaw with silty clay loam. Biochar application with inorganic N could improve N use and the yield of maize cultivated on sandy soils with poor physical and chemical properties.

Method of N application in winter wheat (Triticum aestivum L.) and its impact on estimated plant N loss has not been extensively evaluated. The effects of the pre‐plant N application method, topdress N application method, and their interactions on grain yield, grain protein concentration (GPC), nitrogen fertilizer recovery use efficiency (NFUE), and gaseous N loss was investigated. The trials were set up in an incomplete factorial within a randomized complete block design and replicated three times for 5 site‐years. Data collection included normalized difference vegetation index (NDVI), grain yield, and forage and grain N concentration. The NDVI before and after 90 growing degree days (GDD) were correlated with final grain yield, grain N uptake, GPC, and NFUE. At Efaw location, NDVI after 90 GDDs accounted for 58% of variation in grain yield and 51% variation in grain N uptake. However, NDVI was found to be a poor indicator of both GPC and NFUE. Grain yield was not affected by the method and timing of N application at Efaw. Alternatively, at Perkins, topdress applications resulted in higher yields. The GPC and NFUE were improved with the topdress applications. Generally, topdress application enhanced GPC and NFUE without decreasing the final grain yield. The difference method used in calculating gaseous N loss did not always reveal similar results, and estimated plant N loss was variable by site‐year, and depended on daily fluctuations in the environment.

Variable influence of the environment on early‐season plant growth leads to similarly variable yield levels from year to year. This study was conducted to determine the ideal point in the growing season when normalized difference vegetation index (NDVI) sensor readings were highly correlated with grain yield. For each site‐year, NDVI readings were collected at least seven times from December through April. Readings were collected from two long‐term experiments where an N response was expected in plots that historically received different N rates. The number of days from planting to sensing where growing degree days (GDD) were more than 0 (GDD > 0) was tabulated by site‐year for all dates when NDVI data were collected. The r2 was computed for NDVI versus final grain yield at all sensing dates and plotted against the respective GDD > 0 when readings were taken. Linear plateau models were used to determine the point when the r2 peaked. Averaged over 3 yr (2016–2018), the optimum GDD > 0 needed to predict grain yield using NDVI in both long‐term trials was between 97 and 112. Use of the GDD > 0 as a numeric metric to delineate the best time and date to collect NDVI readings and predict yield potential can then be used to formulate accurate midseason fertilizer N rates. Adhering to quantitative GDD > 0 data is much more reliable than using subjective morphological scales. These critical GDD values can be reported on a day‐to‐day, by‐location basis (http://mesonet.org) for in‐season producer use.