Breeding advancements have significantly improved grain yield over recent decades, yet further progress is needed to meet global food demands amid a growing population. In this study, we systematically examine the responses of both historical and modern winter wheat cultivars, released between 1895 and 2007 in Germany, to varying management intensities. We quantified the independent contributions of nitrogen (N) levels, growth regulators, and plant protection measures to crop productivity and growth variables over two growing seasons. Historical cultivars (pre-1960) exhibited a lower yield (4.6 t ha−1) and harvest index (0.30) compared to modern cultivars (post-1960), which demonstrated a higher yield (6.8 t ha−1) and harvest index (0.40) across all treatments, even those excluding both nitrogen and agrochemicals. We found positive trends in the yield and yield components for both historical (1895–1960) and modern (1961–2007) cultivars; however, significance was only noted for modern cultivars. Crop growth in historical cultivars was aided more by growth regulators, while modern cultivars gained more benefits from plant protection. However, both categories of cultivars produced a synergistic response when combining agronomic practices. Based on a comparison of N application rates and agronomic practices, N had a distinctly greater impact on yield and yield components than other treatments. In both historical and modern cultivars, grain number per ear was the most influential contributing factor to yield. Nevertheless, the effect of the thousand grain weight on yield was notably less for modern cultivars compared to their historical ones. Grain nitrogen yield increased up to mid-supply nitrogen application but remained constant at high rates. Results underscored that cultivar-specific responses for yield and yield components need to consider the interactions between fertilization and other agronomic practices.
Plant density is a significant variable in crop growth. Plant density estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms is a well-established procedure. However, flight companies for wheat density estimation are typically executed at early development stages. Further exploration is required to estimate the wheat plant density after the tillering stage, which is crucial to the following growth stages. This study proposed a plant density estimation model, DeNet, for highly accurate wheat plant density estimation after tillering. The validation results presented that (1) the DeNet with global-scale attention is superior in plant density estimation, outperforming the typical deep learning models of SegNet and U-Net; (2) the sigma value at 16 is optimal to generate heatmaps for the plant density estimation model; (3) the normalized inverse distance weighted technique is robust to assembling heatmaps. The model test on field-sampled datasets revealed that the model was feasible to estimate the plant density in the field, wherein a higher density level or lower zenith angle would degrade the model performance. This study demonstrates the potential of deep learning algorithms to capture plant density from high-resolution UAV imageries for wheat plants including tillers.
<p>By an additional 83 million people to the world&#8217;s population every day, the global population is expected to reach about 9.8 billion by 2050. Feeding these billions is one of the challenges of this century, and extreme events like droughts bring more complexity to the challenge of global food security. Previous agricultural drought studies on the regional or national scale revealed that drought affects specific crops differently; however, these studies are limited to a few major crops or specific regions. Here we analyse for the first time, to our knowledge, crop responses to drought for 25 rainfed crops on a global scale and differentiate crop responses to aridity and drought for thirty years (1986-2016). We use actual and potential crop evapotranspiration calculated by the Global Crop Water Model (GCWM) and develop the two indicators of Crop Drought Index (CDI) and Aridity Index (AI) to investigate the effect of water stress on crop production worldwide. We show crops&#8217; behaviours in extreme drought events differ in time and space. Years with the most severe drought events also differed for the specific crops. To interpret the impacts of drought and aridity on individual crops in specific locations, and avoid any misperception on their potential damages, we map crop-specific AI and crop-specific CDI of all 25 crops during the study period. We compare the spatio-temporal variation of CDI against a global map of AI for each crop to reflect different impacts of long-term water stress experience (aridity) against extreme events (drought). We learn different crops have different responses to aridity and drought. Our findings are of critical importance for drought-resilient agricultural plans and may help to guide the implementation of food security and food aid strategies.</p><p>&#160;</p>
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)