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Short term food security issues require reliable crop forecasting data to identify the population at risk of food insecurity and quantify the anticipated food deficit. The assessment of the current early warning and crop forecasting system which was designed in mid 80’s identified a number of deficiencies that have serious impact on the timeliness and reliability of the data. We developed a new method to forecast maize yield across smallholder farmers’ fields in Tanzania (Morogoro, Kagera and Tanga districts) by integrating field-based survey with a process-based mechanistic crop simulation model. The method has shown to provide acceptable forecasts (r2values of 0.94, 0.88 and 0.5 in Tanga, Morogoro and Kagera districts, respectively) 14–77 days prior to crop harvest across the three districts, in spite of wide range of maize growing conditions (final yields ranged from 0.2–5.9 t/ha). This study highlights the possibility of achieving accurate yield forecasts, and scaling up to regional levels for smallholder farming systems, where uncertainties in management conditions and field size are large.

We evaluated the optical and thermal reflectance of maize (Zea maysL.) interseeded with cover crops using remotely sensed canopy temperature and multispectral imagery. In 2017 and 2018 annual ryegrass (Lolium multiflorumLam.), crimson clover (Trifolium incarnatumL.), oilseed radish (Raphanus sativusL.), and a mixture of annual ryegrass and crimson clover were interseeded in maize at V3 and V6 at three different cover crop seeding rates in small research plots at two experimental farm sites within the network of Michigan State University. The same cover crop species were interseeded in maize at V3 and V6 at a single seeding rate in on‐farm replicated strip trials and also a full‐scale field trial at five locations in Michigan. Canopy temperature and multispectral reflectance were remotely measured 10–12 times throughout each season at all sites using fixed wing aircraft at 1‐m spatial resolution. Optical and thermal reflectance were also measured remotely using an unmanned aerial vehicle (UAV) with 2‐cm spatial resolution three times during the growing season at the small plot sites. Normalized difference vegetation index (NDVI) and normalized difference red‐edge (NDRE) were calculated for each of the experimental sites. No significant differences were detected between the interseeded treatments and control with regards to the optical and thermal reflectance and maize grain yield. Variability at field scale was due to inherent differences and not caused by the interseeding treatments. Optical and thermal reflectance did not show any differences in cover crops treatments.Interseeded cover crops did not affect maize growth and yield.At field scale, the inherent spatial variability had greater impact than interseeded cover crop treatments.

Flows of carbon (C), nitrogen (N), and water (H 2 O) in a corn system. If N fertilizer use was reduced by 36% (e.g., with more efficient fertilizer application technology) and environmental N losses were reduced by 50%, then the circularity of N cycling would increase to 30%, with dramatic reductions in the N losses to GHGs and environmental contamination. In addition to the GHGs emitted during fertilizer manufacturing, the use of nitrogen fertilizers can lead to large quantities of nitrous oxide (a potent GHG) and ammonia emissions from crop fields. C orn-soybean rotations are the predominant U.S. cropping system in terms of land area (92 million acres of corn and 84 million acres of soybeans; USDA, 2020), production (346 million tons of corn and 119.5 million tons of soybeans), and economic value ($75 billion from corn and $21.5 billion from soybean). [Extracted from the article]

ABSTRACT:This paper describes our efforts to integrate economic and biophysical models to evaluate the effects agri-environmental policies have on the value of freshwater ecosystem services. We are developing an integrated assessment model (IAM) that links changes in phosphorus-related management practices on farm fields to changes in the value of key freshwater ecosystem services, including biological condition, water clarity, species-specific fish biomasses, and beach algae. Our IAM approach enables examination of the effects of policies and conservation programs on ecosystem services and values. Results will help policy makers allocate conservation dollars to improve water quality, enhance ecosystem services, and promote more sustainable agricultural production. (JEL Q24, Q51)

Broadcast interseeding cover crops in corn (Zea maysL.) from the V2–V7 corn growth stages provides farmers the opportunity to establish cover crops over large areas quickly compared with drill interseeders. The objectives of this research were to evaluate broadcast interseeded cover crop establishment and biomass production as well as cover crop effect on corn grain yield in farmer's fields in Michigan. In 2017 and 2018, annual ryegrass (Lolium multiflorumLam.), crimson clover (Trifolium incarnatumL.), and oilseed radish (Raphanus sativusL.) were broadcast interseeded at the V3 and V6 corn growth stages in nine farm fields. Cover crop density was measured 30 d after interseeding; cover crop density and biomass were measured in October prior to grain corn harvest. Cover crop density varied across site‐years; annual ryegrass usually had the highest density. Fall biomass production of oilseed radish was usually equal to or greater than annual ryegrass and crimson clover biomass. Rainfall during the interseeding period improved cover crop emergence. Cover crop density and biomass were higher in sites that were tilled prior to corn planting compared with no‐till, likely due to better seed to soil contact. Grain yield did not differ in the cover crop vs. no cover crop control treatments. Successfully establishing cover crops by broadcast interseeding in corn is dependent on specific location conditions; conventional tillage and rainfall improved establishment and biomass production.

Remote sensing has proven to provide agriculture with many different assessments for crop vigor and productivity. The continual evolution of remote sensing instrumentation and platforms has provided new opportunities to use these tools in the assessment of agricultural systems. The application of remote sensing to quantify the spatial variation in production fields across the Midwest over multiple years has revealed there are three stability zones: the high yielding stable zone, the low yielding stable zone, and the unstable zone. These are derived using a combination of thermal images to detect areas of water stress and the normalized difference vegetative index to assess crop vigor and efficiency of light capture. Development of tools using remote sensing coupled with artificial intelligence and machine learning can transform agriculture through the ability to identify variable areas within fields but also determine the potential adaptive strategies to increase the profitability for each field while reducing the environmental impact through more efficient use of nutrients and pesticides. Development of new tools using remote sensing fulfills the vision of integrating many sources of information into decision making at the field and farm scale.

We provide an overview of the Special Issue on current advances, challenges, and opportunities for AI technologies in agriculture. We illustrate the potential of AI using four major components of the food system: production, distribution, consumption, and uncertainty. We recognize that the transformation of agriculture will require new tools to more precisely manage fields to increase production while minimizing the environmental risk to water and air quality. Combining AI with other technologies will be needed to provide effective production management strategies for a given combination of soil, climate, pest complexes, and vegetation. New methods will be needed to determine production limitations, and effective management options. The agricultural enterprise is prime for the use of AI and other technologies if they can be adapted for the unique characteristics of agroecosystems, including variability and directional changes in climate and other global change drivers as well as novel management and policy decisions, and economic market volatility.

Leaching from annual corn (Zea maysL.) crops is a primary source of nitrate (NO3−) pollution of ground and surface waters. Here, we compare NO3−losses from no‐till corn with losses from various alternative perennial cropping systems (switchgrass [Panicum virgatumL.], miscanthus [Miscanthus×giganteusJ.M. Greef & Deuter ex Hodkinson & Renvoiz], a native grass mixture, and restored prairie), as well as hybrid poplar (Populus nigraL. × P. maximowicziiA. Henry ‘NM6’), all grown on a well‐drained soil in Michigan. Soil water was sampled from below the root zone using suction cup samplers during nonfrozen periods (March–November) between 2009 and 2016. Leaching was estimated from NO3−concentrations in soil water and modeled drainage (percolation) rates. Drainage rates were not significantly different among crops, constituting ∼30% of total annual precipitation. Aboveground net primary production (Mg ha−1yr−1) averaged across the 7 yr was highest in poplar (30.8 ± 1.9 [SE]) followed by miscanthus (23.9 ± 2.4) and corn (20.4 ± 0.9). Volume‐weighted mean NO3−concentrations (mg N L−1) and NO3−leaching (kg ha−1yr−1) averaged across the 7 yr were 9.2 and 34.1, 2.3 and 5.9, and 3.0 and 7.2, respectively, for corn, perennial grasses and poplar. Approximately 10 to 32% of applied N was lost as NO3−from these crops, with the highest percent losses from poplar (32%) followed by corn (20%). Perennial cropping systems leached considerably more NO3−in first few years after planting, but over 7 yr they lost much less NO3−than corn. Perennial crops may therefore help ameliorate NO3−pollution in agricultural landscapes even if they receive modest N fertilization. Over 7 yr, perennial grasses and poplar trees leached much less nitrate than cornNitrate leaching from grasses and poplar was comparable with corn for 1–2 yr after planting.Perennial crops can ameliorate nitrate pollution in agricultural landscapes.

This approach to FAS based on the entire value chain, rather than just the end products, provided a clearer pathway for tracing, studying, and redesigning the entire FAS. F ood and agricultural systems (FAS) provide food, feed, fiber, energy, and other products, and they are intricately interwoven with human society. However, as is evident in the FAS schematic in figure 1, different decision-makers control the various activities along the FAS value chain, and there are no public or private infrastructures for recovering unwanted outputs, which are lost or discarded as wastes. [Extracted from the article]

The leaf morphogenetic‐based model predicted the pasture nutritive value variance generated by in an ad hoc experiment.Variation in fiber digestibility with the age and length of leaves is the main driver for declines in pasture digestibility of vegetative regrowth.The morphogenetic approach was designed to enable its potential integration with some of the available pasture growth models.The model provided mechanistic understanding and predictions that allow for the exploration of new management strategies. The leaf morphogenesis of plants is the most important determinant of the nutritive value dynamics in vegetative pasture regrowth. The aim of this study was to develop a simulation model of the pasture nutritive value dynamics based on a morphogenetic approach that takes into account the effects of leaf age and leaf length on forage digestibility in relation to defoliation management. The model was developed and evaluated with detailed data from two independent experiments (Exp. 1 and Exp. 2, respectively) on tall fescue [Lolium arundinaceum(Schreb.) Darbysh.], including descriptions of morphogenesis, neutral detergent fiber (NDF) and digestibility of NDF (NDFD) and dry matter (DMD) of leaf blades. The model precisely and accurately simulated the forage digestibility dynamics of pasture regrowth observed under different residual pasture heights in Exp. 2. The main calculated statistics for NDF, NDFD, and DMD were root mean square deviation < 4% points, R2≥ 0.92, concordance correlation coefficient ≥ 0.86 and bias correlation factor ≥ 0.89. The evaluated model was used to investigate the responses of forage nutritive value to several combinations of residual pasture heights (2–15 cm) and defoliation intervals (one to five leaves per tiller). This study highlights the inclusion of the NDFD trait associated to leaf morphogenesis as a mechanistic way to improve predictions of DMD dynamics in vegetative regrowth under different defoliation managements.

Model‐based approach identified sets of adaptive practices for pasture management across seasons.Suitable combinations of N rate and residual heights can improve the use of N fertilizer and water.The increment in residual pasture mass and N fertilizer may be crucial for more efficient use of water.Pasture growth responses to residual leaf area increased with N fertilization. The objectives of this research were to (i) evaluate the effects of N fertilizer, irrigation, and residual pasture heights on pasture growth, (ii) validate the ability of the SALUS model to predict dynamics of pasture growth, and (iii) evaluate during long‐term period the effects of using different N fertilizer levels and defoliation strategies on pasture growth, N fertilizer use, and water use efficiency (WUE). Eight single‐season experiments were performed at plot scale (8 m2) in Buenos Aires (Argentina, ARG) and Michigan. In ARG different N fertilizer rates (from 0–500 kg N ha−1) were imposed on both rainfed and irrigated tall fescue [Lolium arundinaceum(Schreb.) Darbysh.] pasture during autumn, spring, and summer. In the United States, three residual pasture height treatments (30, 60, and 120 mm) were imposed on both tall fescue and ryegrass (Lolium perenneL.) pasture in the spring and summer. The SALUS was parameterized to simulate tall fescue and ryegrass growth using soil, weather, and different pasture treatments previously tested in ARG and the United States. Results showed that the SALUS accurately represented the response of herbage mass to irrigation and added N in the ARG site (RMSE < 650 kg DM ha−1) and to differences in residual pasture heights in the U.S. experiment (RMSE < 509 kg DM ha−1). Ten‐year simulations (2000–2010) demonstrated that suitable combinations of N fertilizer and residual pasture heights can significantly improve the use of N fertilizer by ∼300% and water by ∼230% through increases in herbage production.

Accounting for the N release from organic amendments improves N use efficiency and promotes soil C storage in horticultural soils.Regional N fertilizer recommendations are affected by a high degree of uncertainty.Crop simulation model can help to develop efficient site‐specific N management. Application rates of synthetic fertilizer to agricultural fields can be reduced through better understanding of N supplied by organic amendments (OA). Field and simulation experiments were performed to quantify the effect of N released from OA application on crop production and selected soil properties in an intensively managed vegetable crop rotation. The SALUS crop model was used to simulate yield, soil N, and soil organic carbon (SOC) dynamics under different combinations of composted or raw OA and synthetic N fertilizer application rates. SALUS accurately simulated aboveground crop biomass production (r2= 0.91, RMSE = 1.7 t ha−1) and crop N uptake (r2=0.96, RMSE = 15 kg N ha−1) under different N management strategies as well as SOC level (r2= 0.51, RMSE = 1 t C ha−1) and soil mineral N (r2= 0.58, RMSE = 56 kg N ha−1). No difference in crop biomass production was found with N fertilizer reductions up to 27% of the conventional N fertilizer rate when combined with OA application. A 12‐yr scenario analysis using SALUS indicated that conventional N fertilizer can be further reduced by up to 50% while sustaining crop biomass production, thereby potentially reducing N losses to the environment. Data gathered from the field study and simulation scenarios highlighted the positive effect of composted OA to maintain soil C levels. This contrasts with average annual SOC losses of 3.7% observed in long‐term simulation scenarios in systems with only N fertilizer or raw OA applications.

Abstract:This article illustrates the critical role that agriculture continues to play in feeding a crowded planet. The integration of geospatial technologies, sensors, drones, and big data analytics is presented here as a possible solution to increase yields, reduce greenhouse gas emissions, and enhance the long-term resilience and sustainability of agriculture systems.

The integration of conservation agriculture with the benefits of precision farming represents an innovative feature aimed to achieve better economic and environmental sustainability. The synergy between these principles was assessed through a technical feasibility and energy efficiency to define the best approach depending on different agricultural systems, spatial and temporal field variability. The study compares three conservation tillage techniques supported by precision farming with conventional tillage in a specific crop rotation: wheat, rapeseed, corn and soybean. The preliminary results show a positive response of precision farming in all the conservation tillage systems, increasing yields until 22%. The energy efficiency achieves highest level in those techniques supported by precision farming, gaining peak of 9% compared to conventional tillage.

Maize (Zea maysL.) yield and its response to nitrogen (N) are affected by the spatial variability of the interaction between weather, management, and soil properties. The objectives of this study were (i) to evaluate the response of spatial variability of maize yield by homogeneous zones (HZs) to different N fertilizer rates under rainfed conditions, (ii) to test the ability of the SALUS (System Approach to Land Use Sustainability) model to simulate the effects of N rates on maize yield under rainfed and irrigated conditions, and (iii) to estimate spatial and temporal N fertilizer response risk in maize through the use of long‐term simulations. In two field experiments in Parana, Argentina (−31.8333°, −60.5167°) in 2011 (Field 1) and 2012 (Field 2), four fertilization treatments (0, 70, 140, and 210 kg ha−1) were evaluated in different HZs. The SALUS model was used to evaluate spatial variability in yield, N response, and net revenue over the long‐term period (1971–2012). Results showed that yield was significantly affected by N rate (p< 0.01) in both fields and by HZ in Field 2 (p< 0.05), whereas N response was only affected by N rate. Simulated yield was significantly affected by N. The model accounted for the spatial variability, showing HZ effect (p< 0.001) and a significant HZ × N interaction (p< 0.0001). The optimal economic return N rate differed between HZs in both fields. Our procedure demonstrated the ability to improve N management by the selection of appropriate N rates across the field, thereby improving N use efficiency and growers’ profits and reducing the potential for negative environmental impacts. Core Ideas Model‐based approach accounted for spatio‐temporal variability of maize N response.Temporal variability of grain yield, N response, and net revenue increased with N rate.The N rate required to reach the highest net revenue differed between homogenous zones within fields.Selecting N rate by homogenous zones might reduce environmental and economic risk. Model‐based approach accounted for spatio‐temporal variability of maize N response. Temporal variability of grain yield, N response, and net revenue increased with N rate. The N rate required to reach the highest net revenue differed between homogenous zones within fields. Selecting N rate by homogenous zones might reduce environmental and economic risk.

Perennial grain cropping systems could address a number of contemporary agroecological problems, including soil degradation, NO3leaching, and soil C loss. Since it is likely that these systems will be rotated with other agronomic crops, a better understanding of how rapidly perennial grain systems improve local ecosystem services is needed. We quantified soil moisture, lysimeter NO3leaching, soil labile C accrual, and grain yields in the first 2 yr of a perennial grain crop under development [kernza wheatgrass, Thinopyrum intermedium(Host) Barkworth & D.R. Dewey] relative to annual winter wheat (Triticum aestivumL.) under three management systems. Overall, differences between annual and perennial plants were much greater than differences observed due to management. In the second year, perennial kernza reduced soil moisture at lower depths and reduced total NO3leaching (by 86% or more) relative to annual wheat, indicating that perennial roots actively used more available soil water and captured more applied fertilizer than annual roots. Carbon mineralization rates beneath kernza during the second year were increased 13% compared with annual wheat. First‐year kernza grain yields were 4.5% of annual wheat, but second year yields increased to 33% of wheat with a harvest index of 0.10. Although current yields are modest, the realized ecosystem services associated with this developing crop are promising and are a compelling reason to continue breeding efforts for higher yields and for use as a multipurpose crop (e.g., grain, forage, and biofuel).

Projected increases in atmospheric carbon dioxide concentration ([CO2]) and air temperature associated with future climate change are expected to affect crop development, crop yield, and, consequently, global food supplies. They are also likely to change agricultural production practices, especially those related to agricultural water management and sowing date. The magnitude of these changes and their implications to local production systems are mostly unknown. The objectives of this study were to: (i) simulate the effect of projected climate change on spring wheat (Triticum aestivum L. cv. Lang) yield and water use for the subtropical environment of the Darling Downs, Queensland, Australia; and (ii) investigate the impact of changing sowing date, as an adaptation strategy to future climate change scenarios, on wheat yield and water use. The multi-model climate projections from the IPCC Coupled Model Intercomparison Project (CMIP3) for the period 2030–2070 were used in this study. Climate scenarios included combinations of four changes in air temperature (0°C, 1°C, 2°C, and 3°C), three [CO2] levels (380ppm, 500ppm, and 600ppm), and three changes in rainfall (–30%, 0%, and +20%), which were superimposed on observed station data. Crop management scenarios included a combination of six sowing dates (1 May, 10 May, 20 May, 1 June, 10 June, and 20 June) and three irrigation regimes (no irrigation (NI), deficit irrigation (DI), and full irrigation (FI)). Simulations were performed with the model DSSAT 4.5, using 50 years of daily weather data. We found that: (1) grain yield and water-use efficiency (yield/evapotranspiration) increased linearly with [CO2]; (2) increases in [CO2] had minimal impact on evapotranspiration; (3) yield increased with increasing temperature for the irrigated scenarios (DI and FI), but decreased for the NI scenario; (4) yield increased with earlier sowing dates; and (5) changes in rainfall had a small impact on yield for DI and FI, but a high impact for the NI scenario.

Cotton is one of the most important irrigated crops in subtropical Australia. In recent years, cotton production has been severely affected by the worst drought in recorded history, with the 2007–08 growing season recording the lowest average cotton yield in 30 years. The use of a crop simulation model to simulate the long-term temporal distribution of cotton yields under different levels of irrigation and the marginal value for each unit of water applied is important in determining the economic feasibility of current irrigation practices. The objectives of this study were to: (i) evaluate the CROPGRO-Cotton simulation model for studying crop growth under deficit irrigation scenarios across ten locations in New South Wales (NSW) and Queensland (Qld); (ii) evaluate agronomic and economic responses to water inputs across the ten locations; and (iii) determine the economically optimal irrigation level. The CROPGRO-Cotton simulation model was evaluated using 2 years of experimental data collected at Kingsthorpe, Qld The model was further evaluated using data from nine locations between northern NSW and southern Qld. Long-term simulations were based on the prevalent furrow-irrigation practice of refilling the soil profile when the plant-available soil water content is <50%. The model closely estimated lint yield for all locations evaluated. Our results showed that the amounts of water needed to maximise profit and maximise yield are different, which has economic and environmental implications. Irrigation needed to maximise profits varied with both agronomic and economic factors, which can be quite variable with season and location. Therefore, better tools and information that consider the agronomic and economic implications of irrigation decisions need to be developed and made available to growers.

Estimation of canopy N content in rainfed environments early in the growing season and across different locations is challenging due to differences in canopy structure, canopy cover, and soil reflectance. The hypothesis of this study was that the combination of the remotely sensed Canopy Content Chlorophyll Index (CCCI) and the Canopy Nitrogen Index (CNI) allows for the estimation of canopy N status directly from remote measurements, independently of cultivar and site. The aims of this study were to (i) estimate canopy N content from CCCI and CNI in two rainfed environments and on two different wheat cultivars; (ii) study the effects of different ways of deriving the CCCI on the estimation of canopy N content. Data were collected from two rainfed sites cropped to wheat, one in Italy (Foggia) and the other in Australia (Horsham, Victoria). Studies were conducted during the growing seasons 2006–2007 (December–June) and 2007 (June–December) for the Italian and Australian sites, respectively. The use of the CCCI in combination with the CNI show that it is possible to estimate canopy N content early in the season (DC 30), (y= 0.94x+ 0.15; r2= 0.97; RMSE = 0.20 g N m−2) when farmers make their N fertilization decisions. Future research is needed to further validate such approach on independent locations with different growing season rainfall; and to study the robustness of the CCCI boundaries on different environments and different crop types and develop a method to estimate biomass under chronic and acute water stress.

Process‐based soil organic C (SOC) models are widely used for simulating, monitoring, and verifying soil C change. In such models, determining the initial distribution of SOC across multiple pools is often not well defined, yet pool initialization can strongly influence the subsequent SOC dynamics. We developed a model‐based procedure to initialize SOC fractions that uses site‐specific soil, climatic, and land use history information, along with measured initial total SOC, to estimate SOC distribution across pools. The procedure consists of creating sets of SOC fractions for scenarios including different field histories, soil texture, and management practices for medium‐ and long‐term simulations to provide a broad spectrum of conditions, using data from an experimental site in Georgia. Tables created using this procedure can help model users select the SOC fractions needed to properly initialize the model. The model is executed for the duration of the prior land use history time period, and the simulated final total SOC is compared with the soil C measurement at the beginning of the subsequent experimental time period. If these values are equal, the final SOC fractions from the land use history simulations are used with the measured soil C to start the simulation of the cropping system being studied. If these values are not equal, then the procedure is repeated iteratively until the measured value of soil C is adequately predicted. We demonstrated the improved prediction accuracy by using the proposed procedure in simulating soil C for a long‐term field experiment in Michigan.

For this study we hypothesise that the use of canopy chlorophyll content index (CCCI) and crop greenness will be useful in assessing crop nutritional status and provide a robust management tool by growth stage DC30 for fertiliser application across multiple sites without being confounded by soil and biomass differences. The objectives of this study were: (i) to study the robustness of the CCCI and greenness as a measure of crop N content at two different locations, and (ii) to validate the model developed for crop nitrogen (N) determination. Data were collected from two rain-fed field sites cropped to wheat, one in Southern Italy (Foggia) and the other in the south-eastern wheat belt of Australia (Horsham). Data collection was conducted during the growing season in 2006–07 (December–June) for the Italian site and during the 2006 and 2007 (June–December) growing seasons for the Australian site. Measurements included crop biophysical properties (leaf area index (LAI), biomass, crop N concentration), hyperspectral remote sensing data, and SPAD (chlorophyll meter) determination. An independent dataset including SPAD, biomass, and remotely sensed data from Horsham (Australia) was used to test the validity of the model developed. Results showed that there is good correlation between SPAD and crop N content. The relationship between greenness (measured as LAI*SPAD) and CCCI was fitted with an exponential model and was not affected by biomass accumulation or soil reflectance (r2=0.85; y=15.1e4.5424x; P<0.001). When this model was tested on the independent dataset it yielded good results for the estimation of greenness (y=1.22x-54.87; r2=0.90; P<0.001; root mean square error 32.2; relative error 15%). In conclusion, SPAD measurements combined with LAI could be used as a crop nutritional management tool by DC30 for fertiliser application across multiple sites.

The objective of this research was to assess the effects of different tillage systems on the spatial and temporal variation of soil resistivity and soil features related to resistance to penetration and porosity using Electrical Resistivity Tomography (ERT). Two‐dimensional (vertical and horizontal) ERT was performed on long‐term conventional deep tillage (CT), minimum tillage (MT), no‐tillage (NT), and by tilling a no‐till plot (freshly tilled no‐till [FTNT]). The tillage treatments were compared in two different studies with measurements taken at different scale and with two different sampling configurations. The first study consisted of ERT measured on a 5.75 m linear transect with horizontal and vertical high resolution measurements and a second study performed at the field scale using an on‐the‐go automatic resistivity profile. The on‐the‐go equipment collected data simultaneously at three different depths (50, 100, 200 cm) and data were referenced by differential global positioning systems (DGPS). Total variation in soil resistivity was significantly explained by tillage treatment and soil depth and by their interaction. The response of soil resistivity to tillage was able to significantly discern between tilled and untilled soil, and between FTNT and the old tillage. Soil resistance to penetration also allowed to detect highly significant differences between the untilled and other treatments at 5 cm, but did not discriminate between FTNT and the other tilled treatments, due to high variability. The automatic resistivity profiling (ARP) measurements were affected by fresh tillage, given the strong response of resistivity to soil bulk density for the first layer.

Soil organic carbon (C) sequestration rates based on the Intergovernmental Panel for Climate Change (IPCC) methodology were combined with local economic data to simulate the economic potential for C sequestration in response to conservation tillage in the six agro-ecological zones within the Southern Region of the Australian grains industry. The net C sequestration rate over 20 years for the Southern Region (which includes discounting for associated greenhouse gases) is estimated to be 3.6 or 6.3MgC/ha after converting to either minimum or no-tillage practices, respectively, with no-till practices estimated to return 75% more carbon on average than minimum tillage. The highest net gains in C per ha are realised when converting from conventional to no-tillage practices in the high-activity clay soils of the High Rainfall and Wimmera agro-ecological zones. On the basis of total area available for change, the Slopes agro-ecological zone offers the highest net returns, potentially sequestering an additional 7.1MtC under no-tillage scenario over 20 years. The economic analysis was summarised as C supply curves for each of the 6 zones expressing the total additional C accumulated over 20 years for a price per t C sequestered ranging from zero to AU$200. For a price of $50/MgC, a total of 427000 MgC would be sequestered over 20 years across the Southern Region, <5% of the simulated C sequestration potential of 9.1Mt for the region. The Wimmera and Mid-North offer the largest gains in C under minimum tillage over 20 years of all zones for all C prices. For the no-tillage scenario, for a price of $50/MgC, 1.74MtC would be sequestered over 20 years across the Southern Region, <10% of the simulated C sequestration potential of 18.6Mt for the region over 20 years. The Slopes agro-ecological zone offers the best return in C over 20 years under no-tillage for all C prices. The Mallee offers the least return for both minimum and no-tillage scenarios. At a price of $200/MgC, the transition from conventional tillage to minimum or no-tillage practices will only realise 19% and 33%, respectively, of the total biogeochemical sequestration potential of crop and pasture systems of the Southern Region over a 20-year period.

Nonrenewable groundwater contributes ∼20% of global irrigation water. As a result, key agricultural regions around the world are on unsustainable trajectories due to aquifer depletion, threatening food production and local economies. With increasing resource scarcity in the central High Plains Aquifer in the United States, an innovative stakeholder‐driven groundwater management framework emerged in Kansas referred to as the Local Enhanced Management Area (LEMA) program. This framework enables groups of irrigators to join together to implement measures to conserve groundwater. Here, we assessed the efficacy of the first LEMA to move the region toward sustainability with a process‐based crop model driven by well records and satellite‐derived annual land use. We found increased irrigation efficiency under the LEMA program reduced groundwater extraction by 25% (40 million m3). However, only 22% of pumping reductions benefitted the net water balance (9 million m3) due to decreased irrigation return flow resulting from increased irrigation efficiency. We then estimated economic impacts using simulated crop yields, commodity prices, and estimated energy saved from reduced groundwater pumping. Cost savings from reduced pumping were about 4.5 times greater than the income lost from minor yield penalties. This suggests that the program promotes both economic and water sustainability, but water targets may need to be more strict to stabilize groundwater levels. As aquifer depletion threatens crop production in many parts of the world, approaches that integrate dynamic process‐based models with in situ and satellite data can inform economically and hydrologically sustainable management strategies. Our work highlights the need to consider both economic factors and root zone processes when evaluating irrigation conservation programs. We assessed management impacts with a satellite‐driven crop model, well data, commodity prices, and energy costsGroundwater reductions minimally decreased crop yields; improved irrigation efficiency limited the benefits to the aquifer water balanceEnergy cost savings exceeded yield penalties, increasing net profits while saving water We assessed management impacts with a satellite‐driven crop model, well data, commodity prices, and energy costs Groundwater reductions minimally decreased crop yields; improved irrigation efficiency limited the benefits to the aquifer water balance Energy cost savings exceeded yield penalties, increasing net profits while saving water

Great interest in natural and aromatic plant cultivation arises from the need to guarantee a constant supply to industry in terms of quality and quantity. Their cultivation seems to be the only possible way to obtain these plants in industrial nations, especially if one considers the high cost of finding wild plants. Too often the cultivation of natural drugs in Italy is associated with rigid rules that exclude indispensable agronomic techniques such as fertilization, protection against parasites (animal and fungus) as well as weed control that involves the use of chemical compounds. At the present time, it is known that the commercialization of plants cultivated with chemical compounds present acceptable risks for man and are an indispensable means for high yields. Correct cultivation techniques cannot exclude consideration of the use of these substances (herbicides, fungicides, insecticides, fertilizers) usually used for other crops. The lack of knowledge on cultivation techniques in order to correctly carry out the choice of products to be used for plant protection as well as tillage methods is rather high, but it would not be necessary to set them up ex‐nova. In fact, much research has been carried out in many nations on how to use modern economic techniques that can be partly adapted to our environment. It is interesting to point out that many species are cultivated on fertile soils and easily accessible to machines, and not on marginal areas which are often infertile and difficult to reach. Plants cultivated on the latter soils would certainly not have competitive costs compared with those cultivated in other nations that have lower labour costs. © 1998 John Wiley & Sons, Ltd.

Great interest in natural and aromatic plant cultivation arises from the need to guarantee a constant supply to industry in terms of quality and quantity. Their cultivation seems to be the only possible way to obtain these plants in industrial nations, especially if one considers the high cost of finding wild plants. Too often the cultivation of natural drugs in Italy is associated with rigid rules that exclude indispensable agronomic techniques such as fertilization, protection against parasites (animal and fungus) as well as weed control that involves the use of chemical compounds. At the present time, it is known that the commercialization of plants cultivated with chemical compounds present acceptable risks for man and are an indispensable means for high yields. Correct cultivation techniques cannot exclude consideration of the use of these substances (herbicides, fungicides, insecticides, fertilizers) usually used for other crops. The lack of knowledge on cultivation techniques in order to correctly carry out the choice of products to be used for plant protection as well as tillage methods is rather high, but it would not be necessary to set them up ex-nova. In fact, much research has been carried out in many nations on how to use modern economic techniques that can be partly adapted to our environment. It is interesting to point out that many species are cultivated on fertile soils and easily accessible to machines, and not on marginal areas which are often infertile and difficult to reach. Plants cultivated on the latter soils would certainly not have competitive costs compared with those cultivated in other nations that have lower labour costs. © 1998 John Wiley & Sons, Ltd.

Nature Plants3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

This corrects the article DOI: 10.1038/nplants.2017.102

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.