Towards realizing nano-enabled precision delivery in plants.

Nanocarriers (NCs) that can precisely deliver active agents, nutrients and genetic materials into plants will make crop agriculture more resilient to climate change and sustainable. As a research field, nano-agriculture is still developing, with significant scientific and societal barriers to overcome. In this Review, we argue that lessons can be learned from mammalian nanomedicine.

U.S. cereal rye winter cover crop growth database.

Winter cover crop performance metrics (i.e., vegetative biomass quantity and quality) affect ecosystem services provisions, but they vary widely due to differences in agronomic practices, soil properties, and climate.

Chemical differences in cover crop residue quality are maintained through litter decay.

As plant litter decomposes, its mass exponentially decreases until it reaches a non-zero asymptote. However, decomposition rates vary considerably among litter types as a function of their overall quality (i.e.

Genetic Variation and Heritability of Sensory and Artisan Bread Traits in a Set of SRW Wheat Breeding Lines.

Focus on local food production and supply chains has heightened in recent years, as evidenced and amplified by the COVID-19 pandemic. This study aimed to assess the suitability of soft red winter (SRW) wheat breeding lines for local artisan bakers interested in locally sourced, strong gluten wheat for bread. Seventy-six genotyped SRW wheat breeding lines were milled into whole wheat flour and baked into small loaves.

The legacy effect of synthetic N fertiliser.

Unlabelled: Cumulative crop recovery of synthetic fertiliser nitrogen (N) over several cropping seasons (legacy effect) generally receives limited attention. The increment in crop N uptake after the first-season uptake from fertiliser can be expressed as a fraction (∆RE) of the annual N application rate. This study aims to quantify ∆RE using data from nine long-term experiments (LTEs).

Late-Season Nitrogen Applications Increase Soybean Yield and Seed Protein Concentration.

Low seed and meal protein concentration in modern high-yielding soybean [ L. (Merr.)] cultivars is a major concern but there is limited information on effective cultural practices to address this issue.

Reproductive resilience but not root architecture underpins yield improvement under drought in maize.

Because plants capture water and nutrients through roots, it was proposed that changes in root systems architecture (RSA) might underpin the 3-fold increase in maize (Zea mays L.) grain yield over the last century. Here we show that both RSA and yield have changed with decades of maize breeding, but not the crop water uptake.

Improved soil biological health increases corn grain yield in N fertilized systems across the Corn Belt.

Nitrogenous fertilizers have nearly doubled global grain yields, but have also increased losses of reactive N to the environment. Current public investments to improve soil health seek to balance productivity and environmental considerations. However, data integrating soil biological health and crop N response to date is insufficient to reliably drive conservation policy and inform management.

Maximum soil organic carbon storage in Midwest U.S. cropping systems when crops are optimally nitrogen-fertilized.

Nitrogen fertilization is critical to optimize short-term crop yield, but its long-term effect on soil organic C (SOC) is uncertain. Here, we clarify the impact of N fertilization on SOC in typical maize-based (Zea mays L.) Midwest U.

Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.

Improved prediction of optimal N fertilizer rates for corn () can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean () yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N.