We used the natural abundance of stable isotopic ratios of hydrogen and oxygen in soil (0.05–3 m depth), plant xylem and precipitation to determine the seasonal changes in sources of soil water uptake by two native encroaching woody species (Pinus ponderosa P. & C. Lawson, Juniperus virginiana L.), and two C₄ grasses (Schizachyrium scoparium (Michx.) Nash, Panicum virgatum L.), in the semiarid Sandhills grasslands of Nebraska. Grass species extracted most of their water from the upper soil profile (0.05–0.5 m). Soil water uptake from below 0.5 m depth increased under drought, but appeared to be minimal in relation to the total water use of these species. The grasses senesced in late August in response to drought conditions. In contrast to grasses, P. ponderosa and J. virginiana trees exhibited significant plasticity in sources of water uptake. In winter, tree species extracted a large fraction of their soil water from below 0.9 m depth. In spring when shallow soil water was available, tree species used water from the upper soil profile (0.05–0.5 m) and relied little on water from below 0.5 m depth. During the growing season (May–August) significant differences between the patterns of tree species water uptake emerged. Pinus ponderosa acquired a large fraction of its water from the 0.05–0.5 and 0.5–0.9 m soil profiles. Compared with P. ponderosa, J. virginiana acquired water from the 0.05–0.5 m profile during the early growing season but the amount extracted from this profile progressively declined between May and August and was mirrored by a progressive increase in the fraction taken up from 0.5–0.9 m depth, showing plasticity in tracking the general increase in soil water content within the 0.5–0.9 m profile, and being less responsive to growing season precipitation events. In September, soil water content declined to its minimum, and both tree species shifted soil water uptake to below 0.9 m. Tree transpiration rates (E) and water potentials (Ψ) indicated that deep water sources did not maintain E which sharply declined in September, but played an important role in the recovery of tree Ψ. Differences in sources of water uptake among these species and their ecological implications on tree–grass dynamics and soil water in semiarid environments are discussed.

Tree rings provide an invaluable long‐term record for understanding how climate and other drivers shape tree growth and forest productivity. However, conventional tree‐ring analysis methods were not designed to simultaneously test effects of climate, tree size, and other drivers on individual growth. This has limited the potential to test ecologically relevant hypotheses on tree growth sensitivity to environmental drivers and their interactions with tree size. Here, we develop and apply a new method to simultaneously model nonlinear effects of primary climate drivers, reconstructed tree diameter at breast height (DBH), and calendar year in generalized least squares models that account for the temporal autocorrelation inherent to each individual tree's growth. We analyze data from 3811 trees representing 40 species at 10 globally distributed sites, showing that precipitation, temperature, DBH, and calendar year have additively, and often interactively, influenced annual growth over the past 120 years. Growth responses were predominantly positive to precipitation (usually over ≥3‐month seasonal windows) and negative to temperature (usually maximum temperature, over ≤3‐month seasonal windows), with concave‐down responses in 63% of relationships. Climate sensitivity commonly varied with DBH (45% of cases tested), with larger trees usually more sensitive. Trends in ring width at small DBH were linked to the light environment under which trees established, but basal area or biomass increments consistently reached maxima at intermediate DBH. Accounting for climate and DBH, growth rate declined over time for 92% of species in secondary or disturbed stands, whereas growth trends were mixed in older forests. These trends were largely attributable to stand dynamics as cohorts and stands age, which remain challenging to disentangle from global change drivers. By providing a parsimonious approach for characterizing multiple interacting drivers of tree growth, our method reveals a more complete picture of the factors influencing growth than has previously been possible.

Agriculture serves as both a source and a sink of global greenhouse gases (GHGs), with agricultural intensification continuing to contribute to GHG emissions. Climate‐smart agriculture, encompassing both nature‐ and technology‐based actions, offers promising solutions to mitigate GHG emissions. We synthesized global data, between 1990 and 2021, from the Food and Agriculture Organization (FAO) of the United Nations to analyze the impacts of agricultural activities on global GHG emissions from agricultural land, using structural equation modeling. We then obtained predictive estimates of agricultural GHG emissions for the future period of 2022–2050 using deep‐learning models. The FAO data show that, from 1990 to 2021, global livestock numbers, inorganic nitrogen (N) fertilizer use, crop residue, and irrigation area increased by 27%, 47%, 49%, and 37%, respectively. The increased livestock numbers contributed to the increases in CH₄ and N₂O emissions, while inorganic N fertilizer, crop residue, and irrigation mainly contributed to the increases in N₂O emissions. Emissions of CO₂ decreased because of a 29% reduction in net forest loss. As a result of the reduced deforestation emissions, the overall agricultural GHG emissions declined from 11.50 to 10.89 GtCO₂eq from 1990 to 2021 despite the increases in livestock numbers, inorganic N fertilizer, crop residue, and irrigation. Looking ahead, our model predicts that if current agricultural trends persist, GHG emissions will rise to 11.82 ± 0.07 GtCO₂eq in 2050. However, maintaining agricultural GHG emissions at the 2021 level through 2050 is possible if the rate of reduction in net forest loss is doubled. Furthermore, if the rate is tripled, agricultural GHG emissions can be limited to 9.85 ± 0.07 GtCO₂eq in 2050. Our findings suggest that reductions in agricultural GHG emissions, alongside sustainable agricultural intensification and climate‐smart agricultural practices, can be achieved through parallel efforts emphasizing accelerated forest conservation.

Seasonal dynamics of net photosynthesis (A_net) in 2-year-old seedlings of Pinus brutia Ten., Pinus pinea L. and Pinus pinaster Ait. were investigated. Seedlings were grown in the field in two light regimes: sun (ambient light) and shade (25% of photosynthetically active radiation (PAR)). Repeated measures analyses over a 12-month period showed that A_net varied significantly among species and from season to season. Maximum A_net in sun-acclimated seedlings was low in winter (yet remained positive) and peaked during summer. Maximum A_net was observed in June in P. pinea (12 μmol m⁻² s⁻¹), July in P. pinaster (23 μmol m⁻² s⁻¹) and August in P. brutia (20 μmol m⁻² s⁻¹). Photosynthetic light response curves saturated at a PAR of 200–300 μmol m⁻² s⁻¹ in winter and in shade-acclimated seedlings in summer. Net photosynthesis in sun-acclimated seedlings did not saturate at PAR up to 1900 μmol m⁻² s⁻¹ in P. brutia and P. pinaster. Minimum air temperature of the preceding night was apparently one of the main factors controlling A_net during the day. In shade-acclimated seedlings, photosynthetic rates were reduced by 50% in P. brutia and P. pinaster and by 20% in P. pinea compared with those in sun-acclimated seedlings. Stomatal conductance was generally lower in shaded seedlings than in seedlings grown in the sun, except on days with a high vapor pressure deficit. Total chlorophyll concentration per unit leaf area, specific leaf area (SLA) and height significantly increased in P. pinea in response to shade, but not in P. pinaster or P. brutia. In response to shade, P. brutia showed a significant increase in total chlorophyll concentration but not SLA. Photosynthetic and growth data indicate that P. pinaster and P. brutia are more light-demanding than P. pinea.

Image-based plant phenotyping facilitates the extraction of traits noninvasively by analyzing large number of plants in a relatively short period of time. It has the potential to compute advanced phenotypes by considering the whole plant as a single object (holistic phenotypes) or as individual components, i.e., leaves and the stem (component phenotypes), to investigate the biophysical characteristics of the plants. The emergence timing, total number of leaves present at any point of time and the growth of individual leaves during vegetative stage life cycle of the maize plants are significant phenotypic expressions that best contribute to assess the plant vigor. However, image-based automated solution to this novel problem is yet to be explored. A set of new holistic and component phenotypes are introduced in this paper. To compute the component phenotypes, it is essential to detect the individual leaves and the stem. Thus, the paper introduces a novel method to reliably detect the leaves and the stem of the maize plants by analyzing 2-dimensional visible light image sequences captured from the side using a graph based approach. The total number of leaves are counted and the length of each leaf is measured for all images in the sequence to monitor leaf growth. To evaluate the performance of the proposed algorithm, we introduce University of Nebraska–Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) and provide ground truth to facilitate new algorithm development and uniform comparison. The temporal variation of the component phenotypes regulated by genotypes and environment (i.e., greenhouse) are experimentally demonstrated for the maize plants on UNL-CPPD. Statistical models are applied to analyze the greenhouse environment impact and demonstrate the genetic regulation of the temporal variation of the holistic phenotypes on the public dataset called Panicoid Phenomap-1. The central contribution of the paper is a novel computer vision based algorithm for automated detection of individual leaves and the stem to compute new component phenotypes along with a public release of a benchmark dataset, i.e., UNL-CPPD. Detailed experimental analyses are performed to demonstrate the temporal variation of the holistic and component phenotypes in maize regulated by environment and genetic variation with a discussion on their significance in the context of plant science.

Anthropogenic activities have altered approximately two‐thirds of the Earth's land surface. Urbanization, industrialization, agricultural expansion, and deforestation are increasingly impacting the terrestrial landscapes, leading to shifts of areas in artificial surface (i.e., humanmade), cropland, pasture, forest, and barren land. Land use patterns and associated greenhouse gas (GHG) emissions play a critical role in global climate change. Here we synthesized 29 years of global historical data and demonstrated how land use impacts global GHG emissions using structural equation modeling. We then obtained predictive estimates of future global GHG emissions using a deep learning model. Our results show that, from 1992 to 2020, the global terrestrial areas covered by artificial surface and cropland have expanded by 133% and 6% because of population growth and socioeconomic development, resulting in 4.0% and 3.8% of declines in pasture and forest areas, respectively. Land use was significantly associated with GHG emissions (p < 0.05). Artificial surface dominates global GHG emissions, followed by cropland, pasture, and barren land. The increase in artificial surfaces has driven up global GHG emissions through the increase in energy consumption. Conversely, improved agricultural management practices have contributed to mitigating agricultural GHG emissions. Forest, on the other hand, serves as a sink of GHG. In total, global GHG emissions increased from 31 to 46 GtCO₂eq from 1992 to 2020. Looking ahead, if current trends in global land use continue at the same rates, our model projects that global GHG emissions will reach 76 ± 8 GtCO₂eq in 2050. In contrast, reducing the rates of land use change by half could limit global GHG emissions to 60 ± 3 GtCO₂eq in 2050. Monitoring and analyzing these projections allow a better understanding of the potential impacts of various land use scenarios on global climate and planning for a sustainable future.