Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R² = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.

Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R² = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods.

The dynamic interactions between soil, weather and crop management have considerable influences on crop yield within a region, and should be considered in optimizing nitrogen (N) management. The objectives of this study were to determine the influence of soil type, weather conditions and planting density on economic optimal N rate (EONR), and to evaluate the potential benefits of site-specific N management strategies for maize production. The experiments were conducted in two soil types (black and aeolian sandy soils) from 2015 to 2017, involving different N rates (0 to 300 kg ha⁻¹) with three planting densities (55,000, 70,000, and 85,000 plant ha⁻¹) in Northeast China. The results showed that the average EONR was higher in black soil (265 kg ha⁻¹) than in aeolian sandy soil (186 kg ha⁻¹). Conversely, EONR showed higher variability in aeolian sandy soil (coefficient of variation (CV) = 30%) than in black soil (CV = 10%) across different weather conditions and planting densities. Compared with farmer N rate (FNR), applying soil-specific EONR (SS-EONR), soil- and year-specific EONR (SYS-EONR) and soil-, year-, and planting density-specific EONR (SYDS-EONR) would significantly reduce N rate by 25%, 30% and 38%, increase net return (NR) by 155 $ ha⁻¹, 176 $ ha⁻¹, and 163 $ ha⁻¹, and improve N use efficiency (NUE) by 37–42%, 52%, and 67–71% across site-years, respectively. Compared with regional optimal N rate (RONR), applying SS-EONR, SYS-EONR and SYDS-EONR would significantly reduce N application rate by 6%, 12%, and 22%, while increasing NUE by 7–8%, 16–19% and 28–34% without significantly affecting yield or NR, respectively. It is concluded that soil-specific N management has the potential to improve maize NUE compared with both farmer practice and regional optimal N management in Northeast China, especially when each year’s weather condition and planting density information is also considered. More studies are needed to develop practical in-season soil (site)-specific N management strategies using crop sensing and modeling technologies to better account for soil, weather and planting density variation under diverse on-farm conditions.

RapidSCAN is a portable active canopy sensor with red, red-edge, and near infrared spectral bands. The objective of this study is to develop and evaluate a RapidSCAN sensor-based precision nitrogen (N) management (PNM) strategy for high-yielding rice in Northeast China. Six rice N rate experiments were conducted from 2014 to 2016 at Jiansanjiang Experiment Station of China Agricultural University in Northeast China. The results indicated that the sensor performed well for estimating rice yield potential (YP₀) and yield response to additional N application (RI_Harvest) at the stem elongation stage using normalized difference vegetation index (NDVI) (R² = 0.60–0.77 and relative error (REr) = 6.2–8.0%) and at the heading stage using normalized difference red edge (NDRE) (R² = 0.70–0.82 and REr = 7.3–8.7%). A new RapidSCAN sensor-based PNM strategy was developed that would make N recommendations at both stem elongation and heading growth stages, in contrast to previously developed strategy making N recommendation only at the stem elongation stage. This new PNM strategy could save 24% N fertilizers, and increase N use efficiencies by 29–35% as compared to Farmer N Management, without significantly affecting the rice grain yield and economic returns. Compared with regional optimum N management, the new PNM strategy increased 4% grain yield, 3–10% N use efficiencies and 148 $ ha⁻¹ economic returns across years and varieties. It is concluded that the new RapidSCAN sensor-based PNM strategy with two in-season N recommendations using NDVI and NDRE is suitable for guiding in-season N management in high-yield rice management systems. Future studies are needed to evaluate this RapidSCAN sensor-based PNM strategy under diverse on-farm conditions, as well as to integrate it into high-yield rice management systems for food security and sustainable development.

The resilience of global food security is a critical concern. Facing limited access to land and potential disruption of the food markets, alternative, scalable, and efficient production systems are needed as a complementary buffer for maintenance of food production integrity. The purpose of this study was to introduce an alternative hydroponic potato growing system where potatoes are grown in bare wood fiber as a growing medium. A system utilizing drip irrigation and plastic bags as containers was tested for three different types of wood fiber, two cultivars and two fertigation strategies. Implementation of the system resulted in ~300% higher tuber production when compared to the local conventional farming. Mineral composition of the tubers obtained from hydroponic system was similar to the composition of tubers grown in the field and revealed potential for biofortification. In addition, a fertigation strategy where the two application points were separated across the root zone resulted in tubers with dry matter content comparable to the potatoes grown in soil. The recyclability, reusability, and simplicity of this solution may encourage its application for improving security of food production in selected areas of the world as well as its utilization in urban agriculture.

Cultivation of strawberries in greenhouses and polytunnels is increasing, and new sustainable growing media are needed to replace peat and coconut coir. This study investigated the effect of wood fiber and compost as growing media on hydroponically cultivated strawberries. Two experiments were conducted, where the everbearing cultivar ‘Murano’ was grown in mixtures of wood fiber and compost (Experiment 1) and the seasonal flowering cultivar ‘Malling Centenary’ was grown in mixtures of wood fiber and peat (Experiment 2). Additionally, in Experiment 2, the effect of adding start fertilizer was assessed. The yield potential of ‘Murano’ plants was maintained in all substrates compared to the coconut coir control. However, a mixture of 75% wood fiber and 25% compost produced the highest yield, suggesting that mixtures of nutritious materials with wood fiber may improve plant performance. The chemical composition of the berries was not affected by the substrate composition; however, berries from plants grown in the best performing blend had a lower firmness than those grown in coconut coir. ‘Malling Centenary’ plants produced higher yields in substrates enriched with start fertilizer. Generally, the productivity of ‘Malling Centenary’ plants was maintained in blends containing up to 75% of wood fiber mixture even without start fertilizer.