Understanding how environmental factors influence the responsiveness of crop yield to growing season precipitation (GSP) can reduce the risk of yield fluctuations, ensuring stable crop production. This study involved the cultivation of rainfed potatoes at three locations within the climatic transition zone of Northwest China. We employed stepwise linear regression and machine learning techniques to pinpoint the key environmental factors influencing potato tuber yield and the yield-precipitation relationship. The slope of the water-limited yield potential relationship for potatoes was determined to be 172.1 kg ha^-1 mm^-1, with an intercept at 121.2 mm. The potato tuber yield exhibited an upward trend with increasing GSP but declined once the precipitation exceeded 400 mm. However, GSP alone explained up to 30% of the variability in potato tuber yield. Factors such as soil moisture at planting, maximum temperatures during the tuber stolon initiation and bulking stages, diurnal temperature fluctuations at maturity, and excessive precipitation events during the growing season significantly influenced potato tuber yield, and consequently, the relationship between yield and GSP. Conversely, climatic factors accounted for more than 63% of the variation in potato tuber yield, with the multiple linear regression model yielding the best results. This was especially evident when the yield-precipitation relationship was categorized into two groups based on the amount and distribution of GSP, maximum temperature, and radiation levels. This analysis suggested that preventing unnecessary water evaporation when precipitation is low, improving drainage when precipitation is high, and planting potato on an optimal date can advance potato production.

The dynamic growth of shoots and panicles determines the final agronomic traits and yield. However, it is difficult to quantify such dynamics manually for large populations. In this study, based on the high-throughput rice automatic phenotyping platform and deep learning, we developed a novel image analysis pipeline (Panicle-iAnalyzer) to extract image-based traits (i-traits) including 52 panicle and 35 shoot i-traits and tested the system using a recombinant inbred line population derived from a cross between Zhenshan 97 and Minghui 63. At the maturity stage, image recognition using a deep learning network (SegFormer) was applied to separate the panicles from the shoot in the image. Eventually, with these obtained i-traits, the yield could be well predicted, and the R² was 0.862. Quantitative trait loci (QTL) mapping was performed using an extra-high density single nucleotide polymorphism (SNP) bin map. A total of 3,586 time-specific QTLs were identified for the traits and parameters at various time points. Many of the QTLs were repeatedly detected at different time points. We identified the presence of cloned genes, such as TAC1, Ghd7.1, Ghd7, and Hd1, at QTL hotspots and evaluated the magnitude of their effects at different developmental stages. Additionally, this study identified numerous new QTL loci worthy of further investigation.

With global warming, crop plants are challenged by heat stress during reproductive growth, leading to male sterility and yield reduction. The balance between reactive oxygen species (ROS) generation and scavenging is disrupted by stress, resulting in oxidative stress which is harmful to crop growth. However, recent advances have shown that ROS signaling via proteins that sense the redox state is positive for plant performance under stress. ROS signaling is also involved in regulating anther development, such as timely tapetum degeneration. We summarize recent progress in uncovering the mechanism of heat stress effects on ROS homeostasis and discuss the relationship between oxidative stress and metabolic disorder, which is often observed during anther development under heat stress. We propose experimental and breeding strategies to improve field crop tolerance to heat stress.

The use of sensors for variable rate nitrogen (VRN) applications is transitioning from equipment-based to drone and satellite technologies. However, regional algorithms, initially designed for proximal active sensors, require evaluation for compatibility with remotely sensed reflectance and N-rate predictions. This study observed normalized difference vegetation index (NDVI) data from six small grain and two corn fields over three years. We employed three platforms: tractor-mounted active sensors (T-NDVI), passive multispectral drone (D-NDVI), and satellite (S-NDVI) sensors. Averaged NDVI values were extracted from the as-applied equipment polygons. Correlations between NDVI values from the three platforms were positive and strong, with D-NDVI consistently recording the highest values, particularly in areas with lower plant biomass. This was attributed to D-NDVI's lower soil reflectance and its ability to measure the entire biomass within equipment polygons. For small grains, sensors spaced on equipment booms might not capture accurate biomass in poor-growing and low NDVI regions. Regarding VRN, S-NDVI and D-NDVI occasionally aligned with T-NDVI recommendations but often suggested half the active sensor rate. Final yields showed some correlation with landscape variables, irrespective of N application. This finding suggests the potential use of drone or satellite imagery to provide multiple NDVI maps before application, incorporating expected landscape responses and thereby enhancing VRN effectiveness.

Rice is the dominant food staple and an important economic resource throughout Asia. Lowland rice production also provides important wetland habitats in support of biodiversity that may provide ecosystem services back to the rice agroecosystems. This review summarizes the literature on the ecosystem benefits that amphibians, birds, bats, and rodents support in the context of the Southeast Asia rice agroecosystems. The literature provides evidence that these taxonomic groups contribute to cultural, regulatory, and provisioning services in support of smallholder farmers and may allow for economic benefits through reduced use of chemical inputs into crops. We encourage a multipronged research approach to bring stakeholders together to provide structured and scalable education programs that will lead to improved human and agroecosystem health through the promotion of understanding the positive feedbacks from biodiversity in these important agricultural wetland habitats.

Efficient solar energy utilization is a crucial determinant of crop yield formation. Moreover, various planting methods have dissimilar impacts on crop solar energy utilization and its measurement methods. This study examined the differences in solar energy utilization between monoculture and intercropping by considering density configuration, plant type arrangement, timing schedule, and spatial layout. We further evaluated the traditional methodologies versus remote sensing technology for solar energy measurements and described the differences in calculation methods for monoculture and intercropping, drawing from the photosynthesis model. Additionally, we discussed the potential advantages and limitations of employing remote sensing technology for the monitoring and prediction of solar energy utilization in field crops.

Leaf day respiration rate (R_L) plays a crucial role in the global carbon cycle. However, R_L of C₄ species has not been sufficiently studied and its response to environmental factors is largely unknown. This work studied the response of R_L of three C₄ species, Setaria viridis, Sorghum sudanense, and Zea mays, to alterations in the vapour pressure deficit (VPD) and irradiance of the growth environment. R_L was estimated using the Kok method (R_{L Kok}) and an improved method that combined gas exchange and chlorophyll fluorescence measurements (R_{L Yin}). On average, shade treatment led to a 24% reduction in R_{L Yin} and a 20% reduction in respiration in the dark (R_Dk), while a consistent VPD effect on R_L was not observed. R_L and R_Dk were positively correlated with nitrogen content per leaf area and net CO₂ assimilation rate but were not correlated with the capacity of carboxylation enzymes. We found a non-significant light inhibition of respiration (1 ± 2%), contradicting the assumption that respiration is inhibited by light and affected by light intensity. Our findings indicate that assuming R_L to be equal to R_Dk at the same temperature is a straightforward but reliable approach to model respiration of the examined C₄ species.

Optimized maize (Zea mays L.) canopy architecture enhances density-tolerance. DHEAP (N, N-Diethyl-2-hexanoyl oxygen radicals-ethyl amine (2-ethyl chloride) phosphonic acid salt) has been shown to increase maize upper canopy strata compactness, but its overall effect on the whole canopy structure and how it shapes the canopy structure remain unclear. This study examined how DHEAP affected the canopy structure of maize hybrids Zhengdan 958 (ZD958) and Xianyu 335 (XY335), with distinct canopy structures, under different planting densities. The results showed that DHEAP increased the leaf orientation value (LOV) of upper canopy strata by 8.0% while reducing middle and lower strata LOV by 11.7% and 18.4%, respectively. This indicates that DHEAP shaped a canopy structure that was compact in the upper strata and loose in the middle and lower strata. Multiple linear regression analysis showed that leaf angle had a greater impact on the upper canopy strata, while leaf auricle size had a greater impact on the middle and lower canopy strata. After DHEAP treatment, light transmission above different canopy strata increased at the reproductive stage. Concurrently, the middle canopy captured more light energy, enhanced yield formation, and boosted radiation use efficiency by 21.9% under high density. In terms of grain yield, DHEAP treatment resulted in a 9.1% and 23.9% increase in ZD958 and XY335, respectively, under high-density conditions. These results suggest that DHEAP shaped the maize canopy structure with high density tolerance, improved the distribution of light within the canopy, and increased grain yield.

Grain protein concentration (GPC) is an important aspect of rice grain quality, which contributes to nutritional intake requirements; however, high GPC may also reduce eating quality. Both GPC and grain yield (GY) are greatly affected by nitrogen (N) management, and GPC is strongly linked to GY through shared N pathways. This review aims to determine how GPC in rice is affected under different growing conditions and crop management options and how varieties differ in GPC under different conditions and to identify the link between GPC and GY. It highlights the importance of total N uptake by the crop and that GPC gradually increases with the N application rate up to an optimum at which GY reaches a maximum. While GY varies greatly depending on the growing conditions, GPC tends to be maintained within a relatively narrow range. When a number of genotypes are compared, there is often an inverse relationship between GY and GPC, with a mean reduction in GPC of 0.46 percentage point for each 1.0 t ha^-1 increase in GY. However, the balance between GY and GPC is altered based on the genotype's capacity to both take up N from the soil and distribute it to grain, including its ability to translocate N from vegetative organs to growing grain. The balance varies greatly among genotypes, as demonstrated in the case of hybrids, where GY is often higher but GPC is lower compared with inbred varieties. The review concludes with the identification of future research efforts to further understand the GY-GPC relationship.

The earliest occurrence of chlorosis at the tips of the oldest leaves is widely recognized as a reliable indicator for diagnosing potassium (K) deficiency and determining the need for K fertilizer. However, hidden K deficiency, typically associated with a decrease in individual leaf photosynthetic area, precedes the onset of visible yellowing symptoms. These concealed symptoms pose challenges for the early diagnosis of K deprivation in plants. The two distinct stages of deficiency exhibit different photosynthetic performances, which are speculated to be closely linked to the subcellular K⁺ distribution. This minireview focuses on investigating K⁺ dynamics across subcellular compartments, along with the involvement of functional transporter proteins and ion channels during K deficiency. We propose potential mechanisms by which subcellular K⁺ regulates photosynthetic capacity under both hidden and visual K deficiency conditions, which sheds new light on the diagnosis of K deficiency. Additionally, future research prospects and areas deserving further investigation are also outlined.

Exploring the impact of elevated CO₂ on photosynthesis is vital for understanding plant responses to climate change. In C₃ plants, elevated CO₂ concentrations generally enhance CO₂ assimilation by increasing chloroplast CO₂ concentration. However, the underlying mechanisms are complex since photosynthesis involves multiple physiological processes operating at different time scales and varying among plant species. In this review, we focused on the responses of key photosynthetic processes in crop, including CO₂ diffusion conductances such as stomatal conductance (g_s), mesophyll conductance (g_m), photochemical reactions, the Calvin-Benson cycle, and related metabolic pathways. Short-term exposure to elevated CO₂ often decreases g_s and g_m while increasing the electron transport rate. However, long-term exposure to elevated CO₂ can decrease photosynthetic capacity due to coordinated downregulation of multiple processes, particularly when the sink‒source ratio declines. To enhance plant productivity under elevated CO₂, it is crucial to maintain or enhance sink activity and understand the CO₂ response mechanisms at the molecular, physiological, and morphological levels. This review provides an update on the short- and long-term responses of g_s, g_m, electron transport system, and carbon assimilation metabolism to elevated CO₂. Furthermore, it offers a perspective on improving crop production in the future with elevated CO₂ levels.

Maize is the staple food crop in Bhutan, which has not achieved national food self-sufficiency. On-farm assessment of yield variability would provide insights into the priorities for Bhutan's maize development program. Here, we conducted three studies in Bhutan: a household survey, on-station experiment, and on-farm monitoring. First, we interviewed 100 households and collected information on maize crop management options and farming characteristics. Second, we evaluated maize growth at two research stations in different elevation zones (640 and 1,700 m a.s.l.). Third, we harvested maize from 25 farm fields at low and high elevations. The gaps between potential yield (with the best management practices at the research stations) and average farm yield and between the best and average farm yields were 53% and 23%, respectively, at the low elevation, and 23% and 20%, respectively, at the high elevation. The classification and regression tree (CART) model showed that field location (distance from the farmer's home), seed source (certified vs. self-produced), and the number of household members involved in farming were the key farming characteristics that affected yield variability, and the manure application regime, urea application, sowing method, and weeding frequency were key management practices. Our results suggest that future research should clarify the most suitable sowing methods and nutrient and weed management regimes, and identify optimal cultivars for each elevation zone, with the goal of developing crop management guidelines for smallholder farmers in Bhutan.