The power of citizen science to contribute to both science and society is gaining increased recognition, particularly in physics and biology. Although there is a long history of public engagement in agriculture and food science, the term ‘citizen science’ has rarely been applied to these efforts. Similarly, in the emerging field of citizen science, most new citizen science projects do not focus on food or agriculture. Here, we convened thought leaders from a broad range of fields related to citizen science, agriculture, and food science to highlight key opportunities for bridging these overlapping yet disconnected communities/fields and identify ways to leverage their respective strengths. Specifically, we show that (i) citizen science projects are addressing many grand challenges facing our food systems, as outlined by the United States National Institute of Food and Agriculture, as well as broader Sustainable Development Goals set by the United Nations Development Programme, (ii) there exist emerging opportunities and unique challenges for citizen science in agriculture/food research, and (iii) the greatest opportunities for the development of citizen science projects in agriculture and food science will be gained by using the existing infrastructure and tools of Extension programmes and through the engagement of urban communities. Further, we argue there is no better time to foster greater collaboration between these fields given the trend of shrinking Extension programmes, the increasing need to apply innovative solutions to address rising demands on agricultural systems, and the exponential growth of the field of citizen science.

Linear interpolation of irregularly spaced LIDAR elevation data sets is needed to develop realistic spatial models. We evaluated inverse distance weighting (IDW) and ordinary kriging (OK) interpolation techniques and the effects of LIDAR data density on the statistical validity of the linear interpolators. A series of 10 forested 1000‐ha LIDAR tiles on the Lower Coastal Plain of eastern North Carolina was used. An exploratory analysis of the spatial correlation structure of the LIDAR data set was performed. Weighted non‐linear least squares (WNLS) analysis was used to parameterize best‐fit theoretical semivariograms on the empirical data. Tile data were sequentially reduced through random selection of a predetermined percentage of the original LIDAR data set, resulting in data sets with 50%, 25%, 10%, 5% and 1% of their original densities. Cross‐validation and independent validation procedures were used to evaluate root mean square error (RMSE) and kriging standard error (SE) differences between interpolators and across density sequences. Review of errors indicated that LIDAR data sets could withstand substantial data reductions yet maintain adequate accuracy (30 cm RMSE; 50 cm SE) for elevation predictions. The results also indicated that simple interpolation approaches such as IDW could be sufficient for interpolating irregularly spaced LIDAR data sets.

Nitrogen decision making and the selection of the “right” N rate in wheat (Triticum aestivum L.) and maize (Zea mays L.) are difficult due to complex interactions in the N cycle with weather, management, and genetics. An adaptive management approach utilizing farmer networks and participatory learning was established to refine N rate decisions. On‐farm trials were established to reflect grower N rate with additional treatments of ±25% N. In 79 site‐years of wheat, N_−25%, N_std, and N_+25% rate treatments were best in 37, 35, and 28% of the trials, respectively. In 100 site‐years of maize, N_−25%, N_std, and N_+25% rate treatments were best in 58, 30, and 12% of the trials, respectively. Grower’s selected N rates in wheat were similar to recommendations from the North Carolina Realistic Yield Expectation (RYE) database while maize N rates were an average 48 kg ha⁻¹ higher; however, N_−25% rates, which were best 58% of the time, were similar to RYE N rate. Doppler‐based estimates of total precipitation from the National Weather Center explained 90% of the average maize yield variability. However, site‐yield was independent of location, N rate, and total precipitation. Measures of performance (N factor productivity and N balance) varied with achieved yields but indicate most growers apply N adequate to maintain organic N lost through mineralization. Results suggest that improved approaches to N rate selection and N efficiency will likely require in‐season adjustments to yield‐based N rates that incorporate local management and environmental conditions throughout the growing season.Core Ideas Yield level and response is independent of location, N rate, and total precipitation. Doppler‐based rainfall estimates help explain seasonal trends in yield. Growers often select N rates greater than recommended for maize but not wheat.

Core Ideas: Fertilizer additives to decrease N losses did not provide consistent yield advantages. Plots treated with N‐loss products did not increase N use efficiency or N uptake. Agronomic optimum N rates observed in the field aligned with North Carolina recommendations. To reduce environmental losses of N and increase crop use, it is critical to optimize N fertilization rates and determine if N‐loss prevention amendments increase yields. Research objectives were to: (i) determine N‐release patterns of three N‐loss amendments (urea ammonium nitrate [UAN] treated with NBPT+DCD, nitrapyrin, or an organo‐Ca) and UAN through a laboratory incubation; (ii) determine effectiveness of these four products for maize (Zea mays L.) and winter wheat (Triticum aestivum L.) produced in two to three regions of North Carolina; and (iii) determine agronomic optimum N rate for wheat and corn compared to state‐recommended rates. Nitrogen release was measured in three soils (coastal plain, piedmont, and mountains) during the incubation experiment. Field experiments were randomized complete block designs (four replications of six maize N rates and five wheat N rates), with each rate applied as one of four product treatments (UAN and UAN+ one of three N‐loss prevention amendments). In the incubation experiment, soils treated with UAN+nitrapyrin or UAN+NBPT+DCD delayed nitrification longer than soils treated with UAN or UAN+organo‐Ca. There was no significant effect of product on maize grain yield (coastal plain and mountains) and wheat yield (coastal plain and piedmont). A year × product interaction occurred for maize grain yield in the piedmont. Agronomic optimum N rates mostly aligned with current North Carolina N fertilizer recommendations. Despite positive laboratory results, N‐loss amendments did not have a significant effect on yield in 9 of 10 site‐years, indicating that proper N rates are a more effective nutrient management strategy.