Cereal Yields Research Articles

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

ContextCollection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. ObjectiveThe aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. MethodsA large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. ResultsYield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. ConclusionBig data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. SignificanceThe results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used.

Effects of combined nitrogen-nutrient sources on lowland rice straw yield (a potential dairy feed) in a derived savannah ecology

Low rice yields in farmers’ fields arise from inadequate nitrogen in most Nigerian soils. Fertilizer use has increased, particularly with the implementation of COVID-19. Straw is a by-product of cereal plants, consisting of dry stalks left over after the grain and chaff. It accounts for cereal crop yields, such as barley, oats, rice, rye, and wheat. The effects of greenhouse gas emissions from burning straw on the climate have been a cause for alarm. As a result, this study in Abeokuta, Southwest Nigeria, investigated the effects of six levels of integrated nutrient sources on NERICA L-34 and ARICA 3 through the analysis of variance in straw yield data. The experiment used a split-plot design, complete block randomization, and treatment arrangements in triplicate. At harvest, ARICA 3 produced significantly more straw (p = 0.05) (14.04 t/ha) than NERICA L-34 (10.44 t/ha) by combining 50 kg N/ha (NPK 15:15:15) with 50 kg N/ha (poultry manure) (10.12 t/ha). The study improved the straw yield (11.4 t ha−1) of ARICA 3 and NERICA L-34 by 50 kg N/ha (NPK 15-15-15) and 50 kg Nha−1 (poultry manure); thus, this straw could be used as dairy feed.

Climate change expected to increase yield of spring cereals and reduce yield of winter cereals in the Western Siberian grain belt

ContextHigher latitude regions are likely to benefit from an extended vegetation period driven by climate change. In this regard, Western Siberia is considered a promising area for cereal crop farming in the near and medium-term future up to 2050. In the southern regions of Western Siberia, spring and winter cereals with different physiology and phenology are cultivated. The spatial resolution of existing estimates of climate change show an impact on cereal crops, however, the resolution remains low, impairing coverage of the diversity of cereals cultivated in Western Siberia. ObjectiveThis study aims to estimate the impact of climate change on the yield of main cereal crops in the Tyumen oblast (Western Siberia, Russia) in the short (2025 – 2030) and medium (2045 – 2050) terms. MethodsThe dataset covered the period from 1990 to 2020 and contained time series data on the yield of cereal crops as well as relevant meteorological parameters (mean air temperature, precipitation, snow cover), all grouped by the 22 municipal districts of the Tyumen oblast. Daily meteorological data were obtained from the CMIP6 model-simulation output and were used to develop random effects linear regression models and to make predictions with optimal models. To assess the impact of climatic parameters on the four key stages of cereal growth (germination, tillering, heading, flowering), a series of random effects linear regression models was developed for each type of cereal crop using six different methods of model building. ResultsThe obtained models predict that the yield of spring grown cereals (barley, oats, spring wheat) would increase by 11.6% ± 1.7% and 10.1% ± 1.7% (mean ± SE), in the short (2025 – 2030) and medium (2045 – 2050) runs as compared to the control period (1990 – 2020). These models predict a decrease in winter grown cereals (rye and winter wheat) yield by 17.1% ± 3.2% in 2025 – 2030 and by 17.7% ± 3.2% in 2045 – 2050. Among the four studied agroecological zones, the largest increase in the yield is predicted in the taiga agroecological zone (up to 84.3% ± 5.5% for barley). ConclusionsCultivation of spring grown cereals adapted to the taiga environment is important for improving grain production rates in Western Siberian regions. SignificanceThis study offers an example of a new bioinformatics-inspired type of analysis that prepares raw biological data for mechanistic modeling. This type of analysis serves to identify the key explanatory variables, paving the way towards spatially resolved multiscale predictive modelling of agroecosystems.

Climate Change and Cereal Crops Productivity in Afghanistan: Evidence Based on Panel Regression Model

Afghanistan frequently faces drought and other climate change-related challenges due to rising temperatures and decreased precipitation in many areas of the country. Therefore, acquiring a thorough comprehension of the implications of climate change on the cultivation of key cereal crops is of the utmost importance. This is particularly significant in the context of Afghanistan, where the agricultural sector plays a pivotal role, contributing close to a quarter of the country’s national gross domestic product and serving as the primary source of employment for 70% of the rural workforce. In this paper, we employ a panel regression model to evaluate the relationship between climate change and cereal productivity in Afghanistan’s agro-climatic zones. Temperature had a significant negative impact, implying that a mean temperature increase of 1 °C decreased wheat and barley yields by 271 and 221 kg/ha, respectively. Future medium- and high-emission scenarios (RCP4.5 and RCP8.5, respectively) for the period 2021–2050 were considered for future yield predictions. To project future climate change impacts, the estimated panel data regression coefficients were used to compute the projected changes in cereal yields. During the period 2021–2050, the mean yield of wheat is projected to decrease by 21 or 28%, the rice yield will decrease by 4.92 or 6.10%, and the barley yield will decrease by 387 or 535 kg/ha in the RCP4.5 and RCP8.5 emission scenarios, respectively, further emphasizing the need for targeted actions to tackle the effects of climate change on agriculture in Afghanistan in alignment with SDG 2 (Zero Hunger) and SDG 13 (Climate Action).

Impacts of biocapacity, climate change, food vulnerability, readiness and adaptive capacity on cereal crops yield: evidence from Africa

Impacts of biocapacity, climate change, food vulnerability, readiness and adaptive capacity on cereal crops yield: evidence from Africa

Sentinel‐2 accurately estimated wheat yield in a semi-arid region compared with Landsat 8

ABSTRACT Wheat and barley are crucial food resources for the global population, making their growth and monitoring essential to enhance food security worldwide. Effective observation of these crops is necessary to address production issues and mitigate the impacts of weather changes. Advancements in remote sensing technology have significantly improved the observation and estimation processes. In this study, various spectral vegetation indices were utilized, along with canopy biophysical properties (such as LAI) and biochemical properties (like chlorophyll). These properties were derived from satellite data, specifically Landsat 8 and Sentinel-2, using tools like Google Earth Engine (GEE) and the R Program. Samples of wheat and barley were collected before reaching their optimal harvest stage, and a correlation was established between the vegetation indices (e.g. NDVI, NDWI, EVI, SAVI, CMFI, SR, RVI, GRVI, and NDRI) and actual production data. Yield prediction algorithms were employed, and the results were used to generate prediction yield maps. The findings revealed a strong relationship between the vegetation indices derived from Sentinel-2 and Landsat images and the actual grain yield, with an R2 of 0.77 and 0.71, respectively. Additionally, the study demonstrated that the most robust relationship was observed between the LAI data obtained from Sentinel-2 and cereal yield data, achieving an R2 of 0.68. Among the indices derived from Landsat images, NDWI exhibited the highest correlation with an R2 of 0.59. The root mean square error (RMSE) was found to be the lowest for Sentinel-2 (0.57) and Landsat 8 (1.54). Furthermore, the study indicated that the least significant relationship for grain yield prediction was observed between the NDRI index for Sentinel-2 (R2 0.1) and the SAVI index for Landsat images (R2 0.47).

The response of roots and the rhizosphere environment to integrative cultivation practices in paddy rice

Integrative cultivation practices (ICPs) are essential for enhancing cereal yield and resource use efficiency. However, the effects of ICP on the rhizosphere environment and roots of paddy rice are still poorly understood. In this study, four rice varieties were produced in the field. Each variety was treated with six different cultivation techniques, including zero nitrogen application (0 N), local farmers’ practice (LFP), nitrogen reduction (NR), and three progressive ICP techniques comprised of enhanced fertilizer N practice and increased plant density (ICP1), a treatment similar to ICP1 but with alternate wetting and moderate drying instead of continuous flooding (ICP2), and the same practices as ICP2 with the application of organic fertilizer (ICP3). The ICPs had greater grain production and nitrogen use efficiency than the other three methods. Root length, dry weight, root diameter, activity of root oxidation, root bleeding rate, zeatin and zeatin riboside compositions, and total organic acids in root exudates were elevated with the introduction of the successive cultivation practices. ICPs enhanced nitrate nitrogen, the activities of urease and invertase, and the diversity of microbes (bacteria) in rhizosphere and non-rhizosphere soil, while reducing the ammonium nitrogen content. The nutrient contents (ammonium nitrogen, total nitrogen, total potassium, total phosphorus, nitrate, and available phosphorus) and urease activity in rhizosphere soil were reduced in all treatments in comparison with the non-rhizosphere soil, but the invertase activity and bacterial diversity were greater. The main root morphology and physiology, and the ammonium nitrogen contents in rhizosphere soil at the primary stages were closely correlated with grain yield and internal nitrogen use efficiency. These findings suggest that the coordinated enhancement of the root system and the environment of the rhizosphere under integrative cultivation approaches may lead to higher rice production.

Spatially differentiated nitrogen supply is key in a global food–fertilizer price crisis

A regional geopolitical conflict and sudden massive supply disruptions have revealed vulnerabilities in our global fuel–fertilizer–food nexus. As nitrogen (N) fertilizer price spikes threaten food security, differentiated responses are required to maintain staple cereal yields across over- and underfertilized agricultural systems. Through integrated management of organic and inorganic N sources in high- to low-input cereal production systems, we estimate potential total N-fertilizer savings of 11% in India, 49% in Ethiopia and 44% in Malawi. Shifting to more cost-effective, high-N fertilizer (such as urea), combined with compost and integration of legumes, can optimize N in N-deficient systems. Better targeted and more efficient N-fertilizer use will benefit systems with surplus N. Geospatially differentiated fertilization strategies should prioritize high-N fertilizer supply to low-yield, N-deficient locations and balanced fertilization of N, P, K and micronutrients in high-yield systems. Nationally, governments can invest in extension and realign subsidies to enable and incentivize improved N management at the farm level.

Cereal yields in Ethiopia relate to soil properties and N and P fertilizers

There is an urgent need to increase cereal yields in the Ethiopian Highlands to ensure national food security. A major crop response-to-fertilizer program was set up in 2017–2019 as part of the CASCAPE project in the Ethiopian Highlands. It covered 33 experiments on maize, teff and wheat in five reference soil groups (Nitisols, Luvisols, Vertisols, Leptosols and Andosols). Five levels of multi-nutrient fertilizer (50–300 kg NPSZnB ha− 1 and 100 kg urea ha− 1) were applied. At the lower fertilizer level, average yields were 5500, 1500 and 3300 kg ha− 1 for maize, teff and wheat, respectively. At the highest rate, yields were 7900, 2100 and 5000 kg ha− 1. Maize and wheat yields were strongly correlated to the reference soil groups, but not to rainfall differences. Wheat yields were also positively correlated to soil organic carbon levels, underpinning the need to apply integrated soil fertility management. Comparing NPSZnB fertilizers with NPS and DAP fertilizers revealed a lack of statistically significant advantage for the fertilizers including Zn and B. As B was present in fine-granular form in the fertilizer bags, being prone to segregation, firm conclusions on the need for this micronutrient cannot be drawn. The use of ‘recommendation windows’ is suggested to group results into concrete packages at district levels and below, preferably combined with soil maps since soil types were correlated with maize and wheat yields. The windows could then be the starting point to develop ‘last mile’ fertilizer use policies, relevant to farmers and the way they manage their fields in the landscape.

A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems

This manuscript aims to develop a new multivariate composite index for monitoring agricultural drought. To achieve this, the AVHRR, VIIRS, CHIRPS data series over a period of 40 years, rainfall and crop yield data as references were used. Variables include parameters for vegetative stress (SVCI, PV, SMN), water stress (PCI, RDI, NRDI), and heat stress (SMT, TCI, STCI), and a new variable related to environmental conditions was calculated through a normalized rainfall efficiency index. Then, random forest algorithm was used to determine the weights of each component of the model by considering interannual fluctuations in cereal yields as an impact variable. The multivariate composite model was compared to the VHI, NVSWI and SPI-12 indices for validation. The results show a large spatiotemporal concordance between the MDCI and the validation indices with a maximum correlation of 0.95 with the VHI and a highly significant p value (< 2.2e-16). Validation of the MDCI model by SPI-12 shows a significantly higher statistically significant relationship than that observed between SPI and VHI and NVSWI. P value range from 3.531e-05 to 6.137e-06 with correlations that vary between 0.6 and 0.64 depending on the station. It is also highly correlated with the Palmer drought severity index (PDSI) and climatic water deficit index (CWDI), with R = 0.85 and p value < 5.8e-10 and R = 0.72 and p value < 1.9e-6, respectively. Finally, the study provides a new direction for multivariate modeling of agricultural drought that should be further explored under various agroclimatic conditions.

Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

The article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017–2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions.

Plasma Treated Cattle Slurry Moderately Increases Cereal Yields

Plasma treatment offers an approach to enhance the nitrogen (N) content of livestock slurry and biogas digestate, thereby increasing the efficacy of organic fertilizers. This innovative method is used to produce nitrogen-enriched organic fertilizer (NEO) containing a double concentration of plant-available N. Over three years, we conducted a comprehensive study in 14 spring wheat and barley field trials in Norway. The primary objective was to assess and compare the cereal grain yield achieved by applying NEO to other conventional fertilizers. The NEO utilized in our research was derived from the unit developed by the Norwegian company N2 Applied. The results indicated that 120 kg N ha−1 in NEO yielded in the same range of cereal grains as 95 kg N ha−1 in mineral fertilizer. Moreover, the combination of untreated slurry and 55 kg N ha−1 in mineral fertilizer Opti-NS yielded the same as 120 kg N ha−1 in NEO. Surprisingly a combination of 12 kg N ha−1 in mineral fertilizer at sowing day and 108 kg N ha−1 in NEO at the three-leaf stage led to a higher yield in spring wheat than 120 kg N ha−1 NEO spread at sowing day in two out of three experimental years. Moreover, applying NEO directly to plants has shown no visible signs of harm. Lastly, filtering the slurry resulted in higher cereal grain yields than the untreated slurry. In conclusion, despite possessing the same N content, utilizing NEO yielded a 15–20% lower cereal grain yield than mineral fertilizer. Nonetheless, 20–30% more yield than the native amount of cattle slurry it derived. However, we have observed an unexplained loss of approximately 17% of the nitrogen in NEO, which does not translate into increased grain yield or nitrogen productivity.

Analysis of Relationship between Grain Yield and NDVI from MODIS in the Fez-Meknes Region, Morocco

Exploring the relationship between cereal yield and the remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between the NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in the NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. The RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS-NDVI data, and consequently, in terms of grain import planning, especially since the national grain production does not cover the demand. Such remote sensing data are therefore essential for administrations that are in charge of food security decisions.

Predictive value of soil moisture and concurrent variables in the multivariate modelling of cereal yields in water-limited environments

Predictive value of soil moisture and concurrent variables in the multivariate modelling of cereal yields in water-limited environments

Modelling interactions between cowpea cover crops and residue retention in Australian dryland cropping systems under climate change

Conservation agriculture management practices (e.g., cover crops and residue retention) have been widely promoted to improve soil quality and environmental sustainability. However, little is known about the long-term interactive effects of cover crops and residue retention on yield of the cash crops and environmental outcomes in dryland cropping systems under climate change. We used the pre-validated APSIM model, driven by statistically downscaled daily climate data from 27 Global Climate Models (GCMs) under two Shared Socioeconomic Pathways (SSP245 and SSP585), to assess the combined influences of cowpea cover crops and three residue retention levels on soil water balance, soil organic carbon (SOC), nitrogen (N) dynamics, crop yield and gross margin across six crop rotation systems during the historical period (1985–2020), near future (2021–2056), and far future (2057–2092) in southeast Australia. Our results showed that, on average, cover crops decreased soil moisture on the day of sowing the succeeding cash crop (by 22%), but led to greater SOC stock (21%), reduced N loss through leaching (71%), and enhanced N uptake and yield of cereals, but decreased N uptake and yield of field pea. The effects of cover crops on yield and gross margin became more positive in the far future under both SSPs, which may be attributed to the SOC increase and greater N availability in the long term. These benefits were more evident under residue removal due to the partly compensatory effects from cover crop residues. Furthermore, cover crops were profitable in the wetter parts of the study region (east), but reduced gross margin in the drier west due to depletion of soil water reserves for the next cash crop. We conclude that particularly where residues are removed, the long-term adoption of cowpea cover crops could be a potential practice to sustain crop productivity with environmental co-benefits under climate change in the wetter parts of the dryland cropping region of southeast Australia.

Does the plant growth regulator paclobutrazol enhance root growth of maize exposed to drought stress during flowering?

AbstractDue to climate change, crop production will increasingly be affected by water limitation, causing remarkable decreases in grain yields of cereals. Plant growth regulators such as paclobutrazol (PAC) have been shown to protect plants from detrimental impacts of drought stress, and improvement of root growth and antioxidant activity were identified as main reasons for their positive effect. A container experiment was conducted with two maize (Zea mays L.) cultivars, Galactus and Fabregas, to investigate how PAC application affects root growth and grain yield under stress conditions. At growth stage V8, the plants were treated once with PAC (0, 2, or 3 mg PAC per plant), and concomitantly reduction in soil water content commenced until 30–35% of the maximum water‐holding capacity (WHC) was achieved. The plants were exposed to this drought condition for three weeks during flowering as the critical period for kernel setting. Both factors, PAC application and drought stress, caused decreases in plant height, whereas total leaf area was unchanged and transpiration rate was significantly reduced by water limitation only. Flowering was almost unaffected by PAC treatment; yet, drought stress significantly delayed start of silking. The straw yield was decreased due to PAC and drought stress, and an improvement of the harvest index was obtained for drought‐stressed Galactus plants with PAC application. Grain yield was unaffected by PAC application, whereas drought stress caused significant decreases by 15% on average of both cultivars. The kernel number of drought‐stressed Galactus plants was increased after PAC treatment, but concurrently smaller kernels were produced. Water limitation generally decreased kernel number. Drought‐stressed Fabregas plants consumed less water after PAC treatment, resulting in significant improvements of water‐use efficiency (WUEgrain) during silking and thus most likely alleviating stress intensity. For both cultivars, PAC treatment and water limitation showed almost no significant impact on root dry matter, root length density, and root surface area, either determined for different soil layers down to 80 cm or on a per‐plant basis. It is concluded that grain yield performance of maize plants, exposed to water limitation during flowering, was not source‐limited but sink‐limited. Consequently, even if PAC can cause improvement of antioxidant activity and photosynthesis, due to sufficient availability of assimilates in the maize kernels a positive effect on grain yield is improbable. Considering source–sink relationships during flowering and kernel set, enhanced root growth due to PAC treatment did apparently not occur.

Impact of agricultural credit disbursement on cereals yield in Odisha

AbstractRealizing the significance of agricultural credit and considering millets and rice are the major crops in Odisha, this study proposes to examine the impact of agricultural credit disbursement (crop loan and term loan) on the yield of total cereals, millets and rice for the period 2000–01 to 2019–20. To examine the cointegration relationship among the variables, an autoregressive distributed lag (ARDL) bound F‐test, and to investigate the impact of agricultural credit on yield of total cereals, millets and rice, an ARDL regression modelling framework is employed. The empirical result for the bounding F‐test provides a statistically significant relationship between agricultural credit and yield of total cereals, millets and rice at 1% level, confirming the long‐run equilibrium relationship in the models. The long‐run impact of crop loans positively affects the yield of total cereals and rice, whereas it has no statistically significant effect on the yield of millets. Moreover, the change in term loan negatively affects the yield of total cereals, millets and rice in the long‐run, where the magnitude is higher in the rice yield compared to the yield of millets. However, in the short‐run, both crop and term loans negatively affect the yield of total cereals, millets and rice. Based on the findings of this study, few policy implications have been suggested to reduce the negative consequences of agricultural credit on cereal yields in Odisha.

An agronomic study of legacy effects from annual legume pastures in acid soils

AbstractThe use of acid soil‐tolerant annual legume pasture species and liming to raise soil pH can have legacy effects that improve subsequent wheat crop growth in acid soils. We established a two‐year experiment in a semi‐arid environment with and without historical lime application to investigate how acid soils (±lime) influence the legacy effects of annual legume pastures. We measured legume and cereal growth and yield, soil‐borne disease incidence, rhizosheath organic acid type and amount, legume biological nitrogen fixation (BNF) and soil moisture in Year 1, and soil microbial biomass carbon (MBC) and wheat yield and quality in Year 2. Minor differences in soil moisture and soil‐borne diseases occurred between cereals and legumes, and the poor BNF contribution of all pasture legumes improved in lime treatments. Pasture legumes produced more organic acids than cereals, which increased in limed treatments. Soil MBC in Year 2 was greater following legumes than cereals. Year 2 wheat grain yield increased following pasture legumes, more so in limed treatments. The contribution of organic acid exudation by annual legume pastures to microbial community legacy effects warrants further scrutiny in semi‐arid environments, particularly when acid soils limit typical rotational benefits.

Increasing crop rotational diversity can enhance cereal yields

Diversifying agriculture by rotating a greater number of crop species in sequence is a promising practice to reduce negative impacts of crop production on the environment and maintain yields. However, it is unclear to what extent cereal yields change with crop rotation diversity and external nitrogen fertilization level over time, and which functional groups of crops provide the most yield benefit. Here, using grain yield data of small grain cereals and maize from 32 long-term (10–63 years) experiments across Europe and North America, we show that crop rotational diversity, measured as crop species diversity and functional richness, enhanced grain yields. This yield benefit increased over time. Only the yields of winter-sown small grain cereals showed a decline at the highest level of species diversity. Diversification was beneficial to all cereals with a low external nitrogen input, particularly maize, enabling a lower dependence on nitrogen fertilisers and ultimately reducing greenhouse gas emissions and nitrogen pollution. The results suggest that increasing crop functional richness rather than species diversity can be a strategy for supporting grain yields across many environments.

Winter cereal species, cultivar, and harvest timing affect trade-offs between forage quality and yield

Volatile feed costs and extreme weather events are contributing to greater economic risk and precarity throughout much of the United States dairy industry. These challenges have prompted dairy farmers to seek ways to reduce feed imports without compromising milk production. For organic dairy farmers, the need to produce more homegrown forage is exacerbated by the high cost and limited supply of organic feed. Integrating winter cereals for forage as part of a double-cropping system is a potential solution, but increasing the amount of forage in dairy cow rations can reduce milk production if the forages are not managed for optimal quality. Organically managed field experiments in Maryland (MD) and New York (NY) were conducted to address two primary objectives: (1) determine the yield and quality of winter cereals—four cultivars each for barley (Hordeum vulgare L.), cereal rye (Secale cereale L.), and triticale (× Triticosecale Wittm. ex A. Camus.)—grown as forage and harvested at different crop growth stages, and (2) evaluate the trade-offs between yield and quality in relation to winter cereal phenology and harvest date. Mean yield at a commonly harvested growth stage, swollen boot (Zadoks 45), was 1.3, 2.2, and 2.2 Mg ha−1 in MD and 1.8, 2.5, and 2.9 Mg ha−1 in NY for barley, cereal rye, and triticale, respectively. Mean relative forage quality (RFQ) at the same growth stage was 180, 158, and 163 in MD and 179, 156, and 157 in NY for the three species. Overall, cereal rye reached swollen boot stage the earliest, barley produced the highest RFQ and retained high quality the longest, and cereal rye and triticale produced the highest yields. Based on these results, farmers should consider barley cultivars if quality is the priority and winter-hardiness is not a concern; cereal rye cultivars if an early harvest is most important; and triticale cultivars if greater harvest schedule flexibility would be most valuable. These findings can be used to better meet the needs of dairy farmers, enhance double-cropping system performance, and improve the synchronization of harvest timing with the specific needs of lactating dairy cows, dry cows, heifers, and calves.