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Current crop variety trials typically compare new varieties with older checks. Using publicly available wheat trial data, the authors show that much of the yield gain attributed to new varieties actually stems from the declining adaptability of older cultivars to changing environments.

Whether or not China can be rice self-sufficient in the future is in question. Here the authors provide a spatially explicit yield-gap analysis of Chinese rice production under future scenarios, identifying priority areas for improving yields to meet demands by 2030.

Modelling analysis shows that the current rates of annual rice yields of the Southeast Asia region will not be able to produce a large rice surplus in the future unless the exploitable yield gap is narrowed down substantially within the next 20 years.

Food security and the conservation of natural ecosystems largely rely on the increase in crop yields. Here, the authors examine global crop yield trends since 1960, and establish a robust statistical framework for estimating historical trajectories and identifying yield plateaus.

This Perspective argues for a global research prioritization framework to advance sustainable intensification, an increase in agricultural yields on existing land and respecting ecosystem integrity, noting research gaps and suggesting priorities.

Future yield improvements will require a closer examination of specific production factors and their interactions. This study quantifies potassium (K) limitations in Indonesia’s intensive cereal cropping systems and reveals that current soil K levels and fertilizer applications are inadequate to meet crop needs, exacerbating the existing yield gap in rice–maize systems in Indonesia and other countries in Southeast Asia.

Maize demand in Sub-Saharan Africa is expected to increase. Here, the authors use data collected by One Acre Fund on 14,773 smallholder fields in the region and determine that maize production can be increased with minimal cropland expansion by improving management practices.

Vast areas of the Amazon forest have been lost to agriculture in recent decades. This study assesses the potential of intensification—increasing yields on a given area—to produce more soybean in Brazil without further Amazon forest loss and to reduce existing CO₂ emissions.

The classic genetics × environment × management framework, used for assessing crop yield, can be extended to include four enabling factors for yield intensification (knowledge, availability of critical goods and services, experience, and capability). The resulting framework, ‘GEM4’, enables managers and advisors to identify farm-level constraints to yield intensification. The framework may be a useful tool for research and development initiatives at local or global levels supporting crop yield intensification.

Increasing rice yield while improving resource use efficiency is of great importance. This study examines cropping systems globally to highlight areas where rice production can be improved by prioritizing R&D strategies.

Effective prioritizing of R&D investments in agriculture needs robust estimation of yield gaps for major cropping systems. Yield potential derived from the ‘top-down’ spatial frameworks is subject to a high degree of uncertainty and would benefit from incorporating estimates from ‘bottom-up’ spatial frameworks.

Here the authors demonstrate that cropland expansion following the historical trend together with closing the current exploitable yield gap by half or more across Africa reduces the continent’s reliance on land conversions and imports by 2050.

Oil palm production in Indonesia has expanded by converting more natural ecosystems to agricultural ones. This study finds large yield gaps among large and smallholder farms there, suggesting improved management could increase production while sparing fragile ecosystems.

Yield potential estimates from statistical models and process-based crop models are compared across diverse climates and soil conditions. The findings reveal limitations of statistical methods, underscoring the need for process-based methods to accurately assess crop production potential and guide policy.

High-resolution global maps of yield potential were created through crop modelling and machine learning. These maps can help orient agricultural research and development programmes and assess food security and land use from local to regional levels.