Climate change is threatening the status quo of agricultural production globally. Perennial cropping systems could be a useful strategy to adapt agriculture to a changing climate. Current and future perennial row crop systems have many and varied applications and these systems can respond differently than annuals to agricultural challenges resulting from climate change, such as shifting ranges of plant, pathogen, and animal species and more erratic weather patterns. To capitalize on attributes of perennial systems that assist in our ability to adapt to a changing world, it is important we fully consider the component parts of agroecosystems and their interactions, including species, genotype and genotypic variance, environment and environmental variance, adaptive management strategies, and farm socioeconomics. We review the current state of perennial grain and oilseed crops for integration into row crop agriculture and summarize the potential for current and future systems to support multiple environmental benefits and adaptation to climate change. We then propose a plant breeding strategy that incorporates the complexity of common domestication traits as they relate to future perennial crop improvement and adaptation and highlight digital technologies that can advance these goals. Evaluation of genetic gain during the development of new perennial crops and systems can be improved using research designs that span an environmental gradient that captures the forecasted shift in climate for a region, which we demonstrate by reanalyzing existing data. Successful development and deployment of perennial crops as a climate adaptation strategy depends on grower adoption, scalability, and sustainable modifications to markets and supply chains.

Soil C status is acritical component of greenhouse gas mitigation efforts, and supports food security through affecting crop growth and management. In much of the world, laboratory‐based measures of soil C are expensive and logistically challenging, whereas map‐based predictions generated at the continental scale (e.g., African Soil Information Service [AfSIS]; www.soilgrids.org ) may be unreliable at the management‐relevant, policy‐relevant, field, and regional scales. We test whether an USD 350, open source, field portable reflectometer can provide site‐specific estimates of soil C status, and also predict whether a crop will respond to fertilizer across 1,155 sites in central and southern Malawi based on an established threshold of 9.4 g C kg ⁻¹ soil. When compared with soil C measured by combustion, the scanner calibrated with covariates of field‐estimable texture class and slope class provides unbiased (0.42 ± 0.44 g C kg ⁻¹ soil; p = .06), precise ( R ² = .57), and actionable (area under the receiver operating characteristic curve [AUC] = 0.88) data at the field scale, including at unmeasured locations (relative prediction error 19–23%) and at the village scale. The reflectometer outperformed predictions from the continental‐scale AfSIS database, which were neither precise ( R ² = .044) nor actionable (AUC = 0.63) at the field scale, and underestimated soil C in the region by 2.5 ± 0.5 g C kg ⁻¹ soil ( p < .001). The reflectometer is an accessible tool to monitor soil C in sub‐Saharan Africa to improve field‐level management, and guide regional policies that support food security while combating climate change.

Increasing soil organic carbon (SOC) stocks is increasingly targeted as a key strategy in climate change mitigation and improved ecosystem resiliency. Agricultural land, a dominant global land use, provides substantial challenges and opportunities for global carbon sequestration. Despite this, global estimates of soil carbon sequestration potential often exclude agricultural land and estimates are coarse for regions in the Global South. To address these discrepancies and improve estimates, we develop a hybrid, data‐augmented database approach to better estimate the magnitude of SOC sequestration potential of agricultural soils. With high‐resolution (30 m) soil maps of Africa developed by the International Soils Database (iSDA) and Malawi as a case study, we create a national adjustment using site‐specific soil data retrieved from 1160 agricultural fields. We use a benchmark approach to estimate the amount of SOC Malawian agricultural soils can sequester, accounting for edaphic and climatic conditions, and calculate the resulting carbon gap. Field measurements of SOC stocks and sequestration potentials were consistently larger than iSDA predictions, with an average carbon gap of 4.42 ± 0.23 Mg C ha⁻¹ to a depth of 20 cm, with some areas exceeding 10 Mg C ha⁻¹. Augmenting iSDA predictions with field data also improved sensitivity to identify areas with high SOC sequestration potential by 6%—areas that may benefit from improved management practices. Overall, we estimate that 6.8 million ha of surface soil suitable for agriculture in Malawi has the potential to store 274 ± 14 Tg SOC. Our approach illustrates how ground truthing efforts remain essential to reduce errors in continent‐wide soil carbon predictions for local and regional use. This work begins efforts needed across regions to develop soil carbon benchmarks that inform policies and identify high‐impact areas in the effort to increase SOC globally.

Selection during plant domestication and improvement often decreases genetic variation, including variation that confers adaptation to local conditions. We report spatial and temporal variation in fitness (seed yield), local adaptation, and segregating genetic variation within three races of common bean (Phaseolus vulgaris L.) with differing domestication histories and genetic diversities. Three‐hundred and twenty‐seven common bean genotypes had seed yield measured at subsets of 70 sites across North America between 1981 and 2015, as part of the Cooperative Dry Bean Nursery (CDBN). We estimated local adaptation using the metric home field advantage (HFA) and segregating genetic variation using heritability. The Durango and Mesoamerican races (Middle American genepool) had higher‐than‐expected (p = .002) HFA, equal to up to 34 yr of average yield gains. Surprisingly, Nueva Granada (Andean genepool) and Durango yields became more heritable across the study period (p < .001), while Mesoamerican heritability decreased (p < .001). Both metrics detected diversity loss corresponding to the timings of major historical gene introgressions. Local adaptation remains an agronomically important phenomenon within some common bean races. Common bean adaptation to new conditions will be most rapid at locations with large local adaptation benefits and high heritability.

Agriculture is a designed system with the largest areal footprint of any human activity. In some cases, the designs within agriculture emerged over thousands of years, such as the use of rows for the spatial organization of crops. In other cases, designs were deliberately chosen and implemented over decades, as during the Green Revolution. Currently, much work in the agricultural sciences focuses on evaluating designs that could improve agriculture's sustainability. However, approaches to agricultural system design are diverse and fragmented, relying on individual intuition and discipline-specific methods to meet stakeholders' often semi-incompatible goals. This ad-hoc approach presents the risk that agricultural science will overlook nonobvious designs with large societal benefits. Here, we introduce a state space framework, a common approach from computer science, to address the problem of proposing and evaluating agricultural designs computationally. This approach overcomes limitations of current agricultural system design methods by enabling a general set of computational abstractions to explore and select from a very large agricultural design space, which can then be empirically tested.