Crop Response Research Articles

Optimization of Chemical Fertilizer through Integrated Nutrient Management (INM) Approach and its Effect on Growth and Yield of Mustard (Brassica juncea L.)

A field experiment to test the optimization of chemical fertilizer use through Integrated Nutrient Management (INM) approach and its effect on growth and yield of Mustard (Brassica juncea L.) was carried out at Rampur agricultural field of Doon (P.G) College of Agriculture Science and Technology, Selaqui, Dehradun, Uttarakhand during the rabi season 2021-2022. The experiment comprised of nine treatments of organic and inorganic nutrient combination, viz T1- Control (No Fertilizer), T2- Recommended Dose of Fertilizer (RDF) (80:60:40 kg/ha), T3- 100 % vermicompost (6.0 t/ha), T4- 75% Farm Yard Manure (FYM) (15.0 t/ha) + 25% RDF (20:15:10 kg/ha), T5- 50% RDF (40:30:20 kg/ha) + FYM (5.0 t/ha) + green manure (2.5 t/ha) + gypsum (10.0 kg/ha), T6-50% RDF (40:30:20 kg/ha) + vermicompost (3.0 t/ha)+ gypsum (5.0 kg/ha), T7- 75% RDF (60:45:30 kg/ha) + vermicompost (2.5 t/ha), T8- 75% RDF (60:45:30 kg/ha) + vermicompost (1.5 t/ha) + green manure (1.5 t/ha), T9- 75% RDF (60:45:30 kg/ha) + FYM (1.5 t/ha) + vermicompost (1.0 t/ha) + green manure (1 t/ha) + gypsum (10.0 kg/ha), laid out in randomized block design with three replication. The results of experiment revealed that there was increase in plant growth parameters in the treatment T9, followed by treatment T8 and T5, respectively. Moreover, the maximum seed yield (2240.39 kg/ha) was observed under T9 over control, where no fertilizer application was applied. The present investigation established the fact that integrated use both organic and inorganic fertilizer can be recommended to the farmers as one of the key strategies for enhancing crop response in terms of growth and yield of mustard.

Impact of ontogenic changes on the dynamics of a fungal crop disease model motivated by coffee leaf rust.

Ontogenic resistance has been described for many plant-pathogen systems. Conversely, coffee leaf rust, a major fungal disease that drastically reduces coffee production, exhibits a form of ontogenic susceptibility, with a higher infection risk for mature leaves. To take into account stage-dependent crop response to phytopathogenic fungi, we developed an SEIR-U epidemiological model, where U stands for spores, which differentiates between young and mature leaves. Based on this model, we also explored the impact of ontogenic resistance on the sporulation rate. We computed the basic reproduction number [Formula: see text], which classically determines the stability of the disease-free equilibrium. We identified forward and backward bifurcation cases. The backward bifurcation is generated by the high sporulation of young leaves compared to mature ones. In this case, when the basic reproduction number is less than one, the disease can persist. These results provide useful insights on the disease dynamics and its control. In particular, ontogenic resistance may require higher control efforts to eradicate the disease.

Foliar application of natural zeolites affects the growth and productivity of processing tomato

The foliar application of zeolites has been proven to positively affect plant physiology suggesting that it could improve plant growth, however, this topic has been little studied. Two field trials were carried out (in 2021 and 2022) to study the potential beneficial effect of natural zeolite particle films on processing tomato crops. Natural zeolites i) clinoptinolite + and mordenite (CubZeo) and ii) micronized clinoptinolite (BigZeo) were sprayed onto the plants six times in the central crop cycles. In both trials, BigZeo promoted leaf and fruit dry mass (DM) accumulation, and leaf area from the early crop stage compared to the water-sprayed plants (Control). The final aerial DM of BigZeo plants enhanced more in the year with favourable (+39.2%) (2022) than in that with more stressing thermal conditions (2021) (+12.1%). Concurrently, the marketable yield was improved by + 36% in both years with positive effects also on fruit total soluble solids. Under heat stress, the CubZeo crop showed even lower aerial DM than the Control, however, the marketable yield remained similar due to higher fruit DM and higher incidence of blossom-end-rot (BER) in untreated plants. In 2022, CubZeo promoted DM accumulation, leaf expansion and yield even if to a lesser extent than the BigZeo treatment. In the fruits the concentrations of K, Ca, and Mg rose in zeolite-treated plants. The improved Ca level in the fruits can explain the lower occurrence of BER in the zeolite-treated plants, particularly with CubZeo. Si was observed at the highest level in fruits and stems of BigZeo-treated plants, followed by CubZeo and Control ones. The accumulation of Si could explain the best crop responses elicited by BigZeo, whereas the observed responses did not appear to involve changes in the photosynthetic machinery. However, further and comprehensive investigations are needed to define the zeolite film particle characteristics and the related physiological and nutritional changes in the plants.

Role of Germin-Like Proteins (GLPs) in Biotic and Abiotic Stress Responses in Major Crops: A Review on Plant Defense Mechanisms and Stress Tolerance

Role of Germin-Like Proteins (GLPs) in Biotic and Abiotic Stress Responses in Major Crops: A Review on Plant Defense Mechanisms and Stress Tolerance

Simulation of Maize Biomass and Yield in An Giang, Vietnam, under Climate Variabilities

In this study, the SIMPLECrop model was applied to simulate maize biomass and yield in 2 crop seasons, Autumn - Winter 2020 (AW) and Winter - Spring 2020 - 2021 (WS), in Cho Moi district, An Giang province, Vietnam (10°23'47''N, 105°27'41''E). The research aimed to analyze the effects of climate variabilities, particularly increased temperature, on maize growth and yield. The growth period for the Winter - Spring 2020 - 2021 season was 67 days, which was 1 day longer than the AW season (Autumn - Winter 2020). Four cultivar parameters, namely Tsum, HI, I50A, and I50B, were employed for the calibration process to fine-tune the model. Sensitivity analysis using Morris and FAST methods revealed that RUE and Tbase had the highest sensitivity and significant impact on the SIMPLECrop model. These 2 parameters showed strong interactions and played a crucial role in influencing model outcomes. The evaluation of the model’s performance resulted in RRMSE values ranging from 4.8 to 6.3 % and NSE values between 0.86 and 0.93, indicating good agreement between model predictions and observed data. Regarding the impact of temperature increase, a 5 °C temperature rise led to a reduction in stover biomass ranging from 5.2 % (Autumn - Winter 2020) to 19.3 % (Winter - Spring 2020 - 2021) and a decrease in yield by 11.3 % (Autumn - Winter 2020) and 27.0 % (Winter - Spring 2020 - 2021). Simulating an increase in CO2 concentration alone, varying from 50, 100, 150, 200 to 250 ppm, resulted in increased biomass and yield for maize. The most substantial increases were observed at 250 ppm CO2, with approximately 2.5 % higher biomass and 7.7 to 9.1 % greater yield. However, under more severe heat stress (5 °C increase), the positive effects of elevated CO2 were mitigated, resulting in a reduced increase in biomass and yield, approximately 3 - 5 %. These findings highlight the importance of considering temperature and CO2 interactions when assessing crop responses to climate variability. HIGHLIGHTS The SIMPLECrop model was rigorously validated for maize in An Giang Province, Vietnam. Validation metrics, including RRMSE and NSE, demonstrated good-fit model performance, with RRMSE values ranging from 4.8 to 6.3 % and NSE values between 0.86 and 0.93 for open field conditions. Sensitivity analysis using Morris and FAST methods identified that two parameters, RUE (Radiation Use Efficiency) and Tbase (Base Temperature), consistently exhibited the highest sensitivity within the SIMPLECrop model. These parameters played a pivotal role in influencing model outcomes and understanding maize responses to climate variability. The study examined the combined impact of increased temperature and elevated CO2 on maize. A 5 °C temperature rise led to significant stover biomass reductions, varying from 5.2 % (AW season) to a substantial 19.3 % (WS season), along with yield declines of 11.3 % (AW) and a significant 27.0 % (WS). Elevated CO2, ranging from 50 to 250 ppm, had a positive influence on maize biomass and yield. However, under more intense heat stress (5 °C increase), the favorable CO2 effects diminished. This underscores the necessity of considering temperature and CO2 interactions when analyzing crop responses to climate variability. GRAPHICAL ABSTRACT

Growth and yield models for black beans under magnetization and pH variation in a greenhouse

Objective: To estimate growth and yield variations in common beans (Phaseolus vulgaris L.) treated with a magnetized nutrient solution considering two factors: magnet exposure time and pH level. The significance of this crop lies in its nutritional and economic value.
 Design/Methodology/Approach: We used a hydroponic system with magnetized Steiner nutrient solution. The design was completely randomized, with a 4 x 6 factorial treatment arrangement and three replications. Factor A comprised exposure times (0.333 hours, 2 hours, chronic, and without magnetization), while Factor B covered solutions with different pH levels (3, 4, 5, 6, 7, and 8). We then applied a multiple regression analysis using the SAS software.
 
 Results: Models for vegetative growth variables (plant height, root length, root dry weight, and foliar biomass) and seed yield components (number of pods, number of grains per pod) were statistically significant (p<0.0001). Coefficients of determination ranged from 59.7 % to 82 %, percentages considered appropriate to explain the observed variability.
 Study limitations/Implications: While the models showed acceptable coefficients of determination, it is essential to consider other factors that were not assessed in this study: exposure to sunlight, insect influence, and diseases that could impact the responses of the bean crop.
 Findings/Conclusions: Appropriate models to describe vegetative growth and seed yield of the common bean, concerning magnetization time and nutrient solution acidity, include variables such as plant height, root length, root dry weight, foliar biomass, total biomass, number of pods, and number of grains per pod.

Optimizing water use and crop yield with deficit irrigation techniques: A comprehensive overview and case study from Morocco

Water scarcity is a global challenge that threatens agricultural productivity and food security. Irrigation agriculture consumes most of the world’s freshwater resources and often suffers from low water use efficiency. To address water shortages, deficit irrigation techniques have been developed to minimize water inputs while sustaining or enhancing crop yields and quality. Deficit irrigation consists of allocating a minimum amount of water to the crop. It can be applied either for the entire growing season (sustained deficit irrigation) or for specific crop stages (regulated deficit irrigation). Another technique is partial root zone drying, which involves alternately irrigating different parts of the root system. This causes partial water stress and increases the water uptake efficiency. These techniques aim to maximize net water use efficiency by exploiting the physiological responses of crops to water stress. This paper provides a theoretical background on deficit irrigation, a review of recent studies on its effects on different crops and environments, and a case study in Morocco where sustained deficit irrigation was applied to peach trees in a semiarid region. The findings showed that sustained deficit irrigation reduced water use by 20% without affecting fruit yield or quality and increased water productivity by 33%. This paper also discusses the challenges and opportunities for implementing deficit irrigation in different contexts and provides some recommendations for future research and development.

Common and specific genetic basis of metabolite-mediated drought responses in rice

Plants orchestrate drought responses at metabolic level but the genetic basis remains elusive in rice. In this study, 233 drought-responsive metabolites (DRMs) were quantified in a large rice population comprised of 510 diverse accessions at the reproductive stage. Large metabolic variations in drought responses were detected, and little correlation of metabolic levels between drought and normal conditions were observed. Interestingly, most of these DRMs could predict drought resistance in high accuracy. Genome-wide association study revealed 2522 significant association signals for 233 DRMs, and 98% (2471/2522) of the signals were co-localized with the association loci for drought-related phenotypic traits in the same population or the linkage-mapped QTLs for drought resistance in other populations. Totally, 10 candidate genes were efficiently identified for nine DRMs, seven of which harbored cis-eQTLs under drought condition. Based on comparative GWAS of common DRMs in rice and maize, representing irrigated and upland crops, we have identified three pairs of homologous genes associated with three DRMs between the two crops. Among the homologous genes, a transferase gene responsible for metabolic variation of N-feruloylputrescine was confirmed to confer enhanced drought resistance in rice. Our study provides not only genetic architecture of metabolic responses to drought stress in rice but also metabolic data resources to reveal the common and specific metabolite-mediated drought responses in different crops.

Understanding the impact of within-field Olsen P variation on common wheat production in Olsen P deficient soils

This study investigates the influence of Olsen phosphorus (Ps) within-field variability on common wheat production in Ps-deficient soil. It covers a comprehensive investigation conducted over two growing seasons in Tuscany, Italy, aiming to evaluate the impact of within-field Ps variation on common wheat production and crop responses to different agronomic treatments. Field experiments were undertaken, employing 24 treatments, including 4 wheat varieties, 2 seeding densities, and 3 nitrogen fertilization levels. Soil samples were collected to determine within field variability of main soil properties. At harvesting, aboveground biomass was collected to determine grain yield, straw, and total protein content. Firstly, an ANOVA was performed to account for the effect of the agronomical treatments on crop parameters, namely the Agronomical Input Model (AIM). Then, soil total nitrogen (Ns) and Ps, were added as covariates in a new model, namely the Agronomical and Soil Input Model (ASIM). Results showed a significant spatial variability of Ps within the fields, ranging between 13.7 and 17.4 mg kg-1. Despite this minimal variation in Ps, we observed a linear increase in GY of about 1430 kg ha-1 for each increase of 1 mg kg-1 in Ps. Further studies should be conducted to account for non-linear responses at both lower and higher Ps levels. The ASIM model outperformed AIM, indicating a notable increase in predictive accuracy for both grain yield and protein concentration due to its incorporation of soil parameters. The variance explained by ASIM in predicting the grain yield and protein concentration in grain increased by about 11.0 % and 17.3 %, respectively, with respect to AIM. The study emphasizes the necessity of managing within-field Ps variability through targeted fertilization strategies to enhance wheat production in Ps-deficient soils. By addressing soil nutrient variability, this research aims to contribute to more precise and efficient common wheat production systems.

Evaluating corn production under different plant spacings through integrated modeling approach and simulating its future response under climate change scenarios

The use of advanced resource conservation techniques is the need of time for higher crop production under the challenges of water scarcity and climate change. The present study was planned (1) to evaluate different plant spacing of corn, sown under raised bed planting (RBP) for higher productivity, (2) calibrate APSIM model for experimental treatments and (3) simulate future (2022–2100) crop response under different scenarios of climate change. The two-year research experiments were conducted on corn with four different plant spacings (S1:15 cm, S2:17.5 cm, S3:20 cm and S4:22.5 cm) under RBP. The crop response was evaluated in terms of leaf area index (LAI), plant height, flowering date, grains per cob, grain yield, biomass yield, harvest index (HI) and water productivity. The S1 treatment produced the highest productivity in terms of grain yield (7.16 Mg/hm2), biomass yield (16.57 Mg/hm2), HI (0.44) and water productivity (1.33 kg/m3). APSIM model was calibrated and its ability to simulate crop response during validation was confirmed from the values of RMSE, NRMSE, R2, CRM and NSE as 0.4, 0.21, 0.83, 0.04 and 0.68 for grain yield. The RMSE and NRMSE were 0.79 and 0.14 for biomass yield. The value of R2 ranged from 0.69 to 0.85, CRM from –0.03 to 0.005, NSE from 0.69 to 0.86 for biomass yield, plant height and LAI. During scenario simulations, the model run was performed against different plant spacing for optimization and results revealed that 10 cm plant spacing produced highest corn yield and growth parameters. Further simulations (2022–2100) were performed using climatic data, acquired from different general circulation model (GCMs) for two rcps (4.5 and 8.5) and results revealed the reduction in grain and biomass yields by 8–14 % and 10–28 %, respectively against different data sources (GCMs). Optimized plant spacing (10 cm) under RBP produced 35.5 % more grain yield and 29.3 % more biomass yield than that under control treatment (ridge-furrow system). Based on the research findings, it is recommended that conservation technique like RBP needs to be adopted in semi-arid regions to mitigate the impact of climate change in terms of future temperature rise for better corn production.

Long-term trajectory of ozone impact on maize and soybean yields in the United States: A 40-year spatial-temporal analysis

Understanding the long-term change trends of ozone-induced yield losses is crucial for formulating strategies to alleviate ozone damaging effects, aiming towards achieving the Zero Hunger Sustainable Development Goal. Despite a wealth of experimental research indicating that ozone's influence on agricultural production exhibits marked fluctuations and differs significantly across various geographical locations, previous studies using global statistical models often failed to capture this spatial-temporal variability, leading to uncertainties in ozone impact estimation. To address this issue, we conducted a comprehensive assessment of the spatial-temporal variability of ozone impacts on maize and soybean yields in the United States (1981–2021) using a geographically and temporally weighted regression (GTWR) model. Our results revealed that over the past four decades, ozone pollution has led to average yield losses of −3.5% for maize and −6.1% for soybean, translating into an annual economic loss of approximately $2.6 billion. Interestingly, despite an overall downward trend in ozone impacts on crop yields following the implementation of stringent ozone emission control measures in 1997, our study identified distinct peaks of abnormally high yield reduction rates in drought years. Significant spatial heterogeneity was detected in ozone impacts across the study area, with ozone damage hotspots located in the Southeast Region and the Mississippi River Basin for maize and soybean, respectively. Furthermore, we discovered that hydrothermal factors modulate crop responses to ozone, with maize showing an inverted U-shaped yield loss trend with temperature increases, while soybean demonstrated an upward trend. Both crops experienced amplified ozone-induced yield losses with rising precipitation. Overall, our study highlights the necessity of incorporating spatiotemporal variability into assessments of crop yield losses attributable to ozone pollution. The insights garnered from our findings can contribute to the formulation of region-specific pollutant emission policies, based on the distinct profiles of ozone-induced agricultural damage across different regions.

Responsiveness of Arabica coffee cultivars to skeleton pruning in a low‐altitude region

AbstractSkeleton pruning is a type of pruning that, by cutting lateral branches (plagiotropic), allows the reinvigoration of coffee plants through the renewal of productive branches. The crop response to this management may be affected by several factors, such as the trees age, crop practices, and, mainly, the genotype. This study aimed to identify the Arabica coffee cultivars with greatest responsiveness to skeleton pruning and desirable productive and qualitative characteristics in a low‐altitude region. The experiment was conducted in northeastern São Paulo state, Brazil, at an altitude of 565 m. The experimental design was randomized blocks with four replicates, and the treatments consisted of 17 short‐stature Arabica coffee cultivars. On average, the responsiveness of the cultivars to pruning was 32% in the first year and 14% in the second year. The cultivars IAC Catuaí SH3, IAC Ouro Verde, IAC Obatã 4739, Tupi IAC 1669‐33, Catiguá MG1, Oeiras MG 6851, Pau‐Brasil MG1, Sacramento MG1, IPR 99, IPR 103, and Sabiá Tardio were responsive to skeleton pruning. Among these, Catiguá, Oeiras, Sacramento, and Sabiá stood out with pruning responsiveness above 45%. The cultivars Obatã IAC 4739, Tupi IAC 1669‐33, and Catuaí SH3 stood out in terms of yield, with an average between 40 and 50 bags ha−1. In addition to high yields, the cultivar Obatã IAC 4739 obtained grains with higher weight and sieve 17. Skeleton pruning was found to be genotype dependent and emerged as a viable alternative for revitalizing Arabica coffee cultivars in low‐altitude regions.

Effects of changes in climatic means, variability, and agro-technologies on future wheat and maize yields at 10 sites across the globe

To address the rising global food demand in a changing climate, yield gaps (YG), the difference between potential yields under irrigated (YP) or rainfed conditions (YWL) and actual farmers’ yields (Ya), must be significantly narrowed whilst raising potential yields. Here, we examined the likely impacts of climate change (including changes in climatic variability) and improvements in agricultural technologies on crop yields and yield gaps. Eight rigorously tested crop simulation models were calibrated for wheat and maize and run at 10 different sites around the world. Simulations were performed to estimate YP, YWL and yields achievable under three locally defined technology packages: TP0 represents average farmer's practice, while TP1 and TP2 are increasingly advanced technologies. Simulations were run for the baseline (1981–2010) and twelve future climate scenarios for 2050, representing changes in the means of climate variables and in the variability of daily temperature and dry/wet spell durations. Our basic hypotheses were that (H1) mean climate changes combined with increased weather variability lead to more negative yield impacts than mean climate changes alone, and (H2) advanced technologies would serve as effective adaptations under future climatic conditions. We found that crop responses were dependent on site characteristics, climate scenarios and adopted technologies. Our findings did not support H1. As for H2, the improved technology packages increased wheat and maize yields at all sites, but yield gap reduction varied substantially among sites. Future studies should consider a broader range of climate scenarios and methods for analysing potential shifts in climate variability. Moreover, it is recommended to co-create and evaluate climate zone-specific climate-smart crop production technologies in interaction with a wide range of local stakeholders.

Review of Crop Response to Soil Salinity Stress: Possible Approaches from Leaching to Nano-Management

Soil salinity is a serious problem facing many countries globally, especially those with semi-arid and arid climates. Soil salinity can have negative influences on soil microbial activity as well as many chemical and physical soil processes, all of which are crucial for soil health, fertility, and productivity. Soil salinity can negatively affect physiological, biochemical, and genetic attributes of cultivated plants as well. Plants have a wide variety of responses to salinity stress and are classified as sensitive (e.g., carrot and strawberry), moderately sensitive (grapevine), moderately tolerant (wheat) and tolerant (barley and date palm) to soil salinity depending on the salt content required to cause crop production problems. Salinity mitigation represents a critical global agricultural issue. This review highlights the properties and classification of salt-affected soils, plant damage from osmotic stress due to soil salinity, possible approaches for soil salinity mitigation (i.e., applied nutrients, microbial inoculations, organic amendments, physio-chemical approaches, biological approaches, and nano-management), and research gaps that are important for the future of food security. The strong relationship between soil salinity and different soil subdisciplines (mainly, soil biogeochemistry, soil microbiology, soil fertility and plant nutrition) are also discussed.

Can deep fertilization in spring maize fields improve soil properties and their distribution in soil profile?

Deep fertilization strategy has been proven to be an important fertilizer management method for improving fertilizer utilization efficiency and crop yield. However, the relationship between soil chemical and biochemical characteristics and crop productivity under different fertilization depth patterns still needs comprehensive evaluation. Field tests on spring maize were therefore carried out in the Loess Plateau of China for two successive growing seasons from 2019 to 2020. Four distinct fertilization depths of 5 cm, 15 cm, 25 cm, and 35 cm were used to systematically investigate the effects of fertilization depth on soil physicochemical parameters, enzyme activity, and biochemical properties. The findings demonstrated that although adjusting fertilization depths (D15, D25) did not significantly affect the soil organic carbon content, they did significantly improve the soil chemical and biochemical characteristics in the root zone (10–30 cm), with D25 having a greater influence than D15. Compared with D5, the total nitrogen (TN), total phosphorus (TP), available nitrogen (AN), Olsen-P, dissolved organic carbon, and nitrogen (DOC and DON) in the root zone of D25 significantly increased by 12.02%, 7.83%, 22.21%, 9.56%, 22.29%, and 26.26%, respectively. Similarly, the urease, invertase, phosphatase, and catalase in the root zone of D25 significantly increased by 9.56%, 13.20%, 11.52%, and 18.05%, while microbial biomass carbon, nitrogen, and phosphorus (MBC, MBN, and MBP) significantly increased by 18.91%, 32.01% and 26.50%, respectively, compared to D5. By optimizing the depth of fertilization, the distribution ratio of Ca2–P and Ca8–P in the inorganic phosphorus components of the root zone can also be increased. Therefore, optimizing fertilization depth helps to improve soil chemical and biochemical characteristics and increase crop yield. The results of this study will deepen our understanding of how fertilization depth influence soil properties and crop responses.

Impact of soil and water conservation structures on the spatial variability of topsoil moisture content and crop productivity in semi-arid Ethiopia

Plant available soil moisture and its spatial variability is among the most important limiting factors for crop and livestock productivity in semi-arid Ethiopia. The objectives of this study were therefore: 1) to measure the effects of land use and slope gradient on spatial variability of topsoil moisture content. 2) to analyze the variability of topsoil moisture content and crop responses under different soil and water conservation (SWC) structures and 3) to analyze whether topsoil moisture content, grain and biomass yields vary in relation to SWC structures. In total, 21 large runoff plots (600–1000 m2) were installed and treated with stone bunds, trenches, stone bunds with trenches and a control plot at each site. Plot sites were located on gentle (5%), medium (12%) and steep (16%) slopes of cropland and rangeland. Topsoil (0–15 cm) moisture content for plots were monitored daily using soil moisture probes (Theta-Probes) and weekly using manual sampling over two growing seasons. Crop performance was monitored during two growing seasons and grain and biomass yield were measured at harvest. Results show that average gravimetric moisture content of the topsoil during growing seasons is remarkably higher for cropland (33–25 g/100 g) compared to rangelands (24–20 g/100 g). This is due to tillage-induced runoff retention and infiltration. Soil moisture content decreases with increasing slope gradients, owing to increased drainage. Considering the effects of SWC structures, average topsoil moisture contents at accumulation and erosion zones are generally higher as compared to middle zone between two successive SWC structures. Average seasonal volumetric soil moisture content of 38.2 (cm3/100 cm3) in 2011 and 39.4 (cm3/100 cm3) in 2012 for accumulation zone of plots with stone bunds with trenchers are significantly higher than at other sampling locations. Crop growth performance, grain and biomass yield varied among plots and depended on slope gradients and SWC structures. Grain yields of wheat in 2011 and barley in 2012 were significantly higher for plots with SWC structures as compared to control plots and are highly correlated to topsoil moisture content (r = 0.64; p < 0.04 in 2011 and r = 0.59, p < 0.01 in 2012). Even when taking into account the cropland area occupied by SWC structures, grain yield increased by (4.2%−33%) for plots with SWC structures as compared to control plots. Overall, our results show that implemented measures may not only reduce runoff and soil loss, but also increase crop productivity by enhancing soil moisture and thus can significantly contribute to sustainability of agro-ecosystems in semi-arid environments.

Signaling and Defence Mechanism of Jasmonic and Salicylic Acid Response in Pulse Crops: Role of WRKY Transcription Factors in Stress Response

Signaling and Defence Mechanism of Jasmonic and Salicylic Acid Response in Pulse Crops: Role of WRKY Transcription Factors in Stress Response

Crop response to nitrogen-phosphorus colimitation: theory, experimental evidences, mechanisms, and models. A review

Crop response to nitrogen-phosphorus colimitation: theory, experimental evidences, mechanisms, and models. A review

Digital strategies for nitrogen management in grain production systems: lessons from multi-method assessment using on-farm experimentation

During the past few decades, a range of digital strategies for Nitrogen (N) management using various types of input data and recommendation frameworks have been developed. Despite much research, the benefits accrued from such technology have been equivocal. In this work, thirteen methods for mid-season N recommendations in cereal production systems were evaluated simultaneously, ranging from simple mass balance through to non-mechanistic approaches based on machine learning. To achieve this, an extensive field research program was implemented, comprising twenty-one N strip trials implemented in wheat and barley fields across Australia over four cropping seasons. A moving window regression approach was used to generate crop response functions to applied N and calculate economically optimal N rates along the length of the strips. The N recommendations made using various methods were assessed based on the error against the optimal rate and expected profitability. The root mean squared error of the recommendations ranged from 15 to 57 kg/ha. The best performing method was a data-driven empirical strategy in which a multivariate input to characterise field and season conditions was abundantly available and used to predict optimal N rates using machine learning. This was the only approach with potential to substantially outperform the existing farmer management, reducing the recommendation error from 42 to 15 kg/ha and improving profitability by up to A$47/ha. Despite being reliant on extensive historical databases, such a framework shows a promising pathway to drive production systems closer towards season- and site-specific economically optimum recommendations. Automated on-farm experimentation is a key enabler for building the necessary crop response databases to run empirical data-driven decision tools.

Reversible strengthening behaviour of subsurface layers in sandy soils – understanding variable response to strategic deep tillage

Deep sandy soils cover more than 10 M ha of Australia's southern dryland cropping lands. Farming these soils is inherently challenging, and yield gaps are often wide due to multiple cropping constraints. Physical constraints that limit rooting depth are commonplace and often attributed to traffic-induced soil compaction. However, recent observations draw attention to a reversible hardsetting behaviour in response to soil water content. This hardsetting phenomenon is poorly understood and it may explain inconsistent crop responses to deep ripping in sandy soils. We examined the dynamic behaviour of soil strength under different soil water contents in deep ripped and unripped sands from four South Australian sites. We analyzed the elemental, mineralogical, chemical and physical compositions to differentiate the nature of hardened subsurface layers. Soil compaction and cementation were ruled out due to the soil's bulk density (<1.6 g cm−3) encountered and the rapid slaking that occurred when a hardened pan was submerged in water. The hardsetting behaviour was confirmed by the abrupt change in soil strength in response to the soil water content, measured using intact samples in a laboratory-based penetrometer. This significant increase in soil strength was also observed for soils collected from the ripped plots and for repacked soil samples, with penetration resistance ranging from 0.4 to 5.7 MPa depending on soil content. In contrast to our hypotheses, the hardsetting layer could not be distinguished from the layers above or below it by any analytical method. Given soil strength is typically measured on field moist soil, and standard chemical analysis did not differentiate hardsetting behaviour, alternative methods of diagnosis are needed to understand where deep tillage is likely to have the desired long-term effect. Identifying the nature of potential binding agents responsible for increased strength upon drying would be valuable. Our results confirmed reversible hardsetting in deep sandy soils is driven by small changes in soil water content, from 0.08 g g−1 to 0.05 g g−1 upon drying. Given that rapid re-consolidation of the soil matrix after expensive deep ripping interventions are reported, there is a strong imperative to improve diagnosis, and to identify if soil ameliorants could improve longevity by disrupting hardsetting behaviour.