Food security involves the sustainable utilization of soil and land resources. Zero‐tillage (ZT) practice is a proponent of better resource utilization, to improve soil physical condition, and a potential sink to atmospheric carbon. However, the impact varies across climates, over the ZT history, cropping systems, and soil depths. A meta‐analysis was performed, based on 4,131 paired data from 522 studies spread globally, to evaluate the effect of ZT in comparison to conventional tillage, on soil physical condition (bulk density; mean weight diameter of aggregates; field capacity water content; and steady‐state infiltration rate), soil organic carbon (SOC) content, and the root response (root length density). Zero‐tillage significantly improved mean weight diameter of aggregates and field capacity water content at surface and subsurface layers by 19–58% and 6–16%, respectively, and resulted in no change in bulk density in either of the layers, but infiltration rate increased by 66%. Surface 0‐ to 5‐ and 5‐ to 10‐cm layers had significantly higher SOC content under ZT, whereas in other layers, the SOC content either reduced or did not change, resulting in a small and insignificant variation in the SOC stock (~1.1%) in favor of ZT. The root length density improved by ~35% in ZT only at 0‐ to 5‐cm soil depth. Effect of climate, soil type, or cropping system could not be broadly recognized, but the impact of ZT certainly increased over time. Improvements in soil aggregation and hydraulic properties are highly convincing with the adoption of ZT, and therefore, this practice leads to the better and sustainable use of soil resources.

A global analysis of the impact of zero‐tillage on soil physical condition, organic carbon content, and plant root response

The early growth pattern, especially the age of peak growth, of broilers affects the time to market and slaughter weight, which in turn affect the profitability of the poultry industry. However, the underlying mechanisms regulating chicken growth and development have rarely been studied. This study aimed to identify candidate genes involved in chicken growth and investigated the potential regulatory mechanisms of early growth in chicken. RNA sequencing was applied to compare the transcriptomes of chicken muscle tissues at three developmental stages during early growth. In total, 978 differentially expressed genes (DEGs) (fold change ≥ 2; false discovery rate < 0.05) were detected by pairwise comparison. Functional analysis showed that the DEGs are mainly involved in the processes of cell growth, muscle development, and cellular activities (such as junction, migration, assembly, differentiation, and proliferation). Many of the DEGs are well known to be related to chicken growth, such as MYOD1, GH, IGF2BP2, IGFBP3, SMYD1, CEBPB, FGF2, and IGFBP5. KEGG pathway analysis identified that the DEGs were significantly enriched in five pathways (P < 0.1) related to growth and development: extracellular matrix–receptor interaction, focal adhesion, tight junction, insulin signaling pathway, and regulation of the actin cytoskeleton. A total of 42 DEGs assigned to these pathways are potential candidate genes inducing the difference in growth among the three developmental stages, such as MYH10, FGF2, FGF16, FN1, CFL2, MAPK9, IRS1, PHKA1, PHKB, and PHKG1. Thus, our study identified a series of genes and several pathways that may participate in the regulation of early growth in chicken. These results should serve as an important resource revealing the molecular basis of chicken growth and development.

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Transcriptomic profile of leg muscle during early growth in chicken.

Growth functions with upper horizontal asymptote do not have a maximum point, but we frequently question from which point growth can be considered practically constant, that is, from which point the curve is sufficiently close to its asymptote, so that the difference can be considered non-significant. Several methods have been employed for this purpose, such as one that verifies the significance of the difference between the curve and its asymptote using a t-test, and that of Portz et al. (2000), who used segmented regression. In the present work, we used logistic growth function, which has horizontal asymptote and one inflection point, and applied a new method consisting in the mathematical determination of a point in the curve from which the growth acceleration asymptotically tends to zero. This method showed the advantage to have biological meaning besides leading to a point quite close to those obtained using the beforementioned methods.

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Determination of a point sufficiently close to the asymptote in nonlinear growth functions

The study compared conservation agriculture (CA) practice (no-tillage, crop residue cover on the surface and relay mung bean) with conventional intensive tillage system, in order to evaluate their effects on soil strength and water content, total porosity and pore-size distribution, roots and above-ground plant growth in wheat under a rice-wheat rotation. The experiment was started in the kharif season of the year 2010 in the research farm of the Indian Agricultural Research Institute, New Delhi, India. Observations were taken during 6th, 7th and 8th year of the rotation. The CA practice primarily reduced the sub-surface (15–30 and 30–45 cm) soil strength by 20–25% owing to 14% higher water content at this layer, and increased the total porosity (17%) and retention pores (22%). Soil aggregation and organic C improved primarily in the surface layer (0–15 cm). The modified soil physical properties led to better root growth, plant leaf area index and solar radiation interception, and higher grain and straw yields. The CA practice significantly contributed to improving the soil physical condition, especially in reducing the sub-surface compaction and improvement on root growth in wheat. This is significant in our quest to increase and sustain the rice-wheat production system in the Indian subcontinent.

Conservation agriculture had a strong impact on the sub-surface soil strength and root growth in wheat after a 7-year transition period

Purple nutsedge (Cyperus rotundus L.), a troublesome weed, is economically damaging weed, which is widely naturalized in the tropical and subtropical regions of the world. This weed has been reported to cause 20–90% yield losses in various agronomic and horticultural crops across the world. Perennial nature, genetic diversity, ability to tolerate adverse climatic conditions, high rate of reproduction, ease-of-dispersion, strong competitive abilities, and allelopathic potential assist this weed to thrive in a range of agro-climatic regions. Routine cultural approaches, including crop rotation, crop choice, and mechanical cultivation, are ineffective in achieving season-long management of C. rotundus. Pre-emergent and post-emergent herbicides, as well as myco-herbicides, can control its growth but fail to limit the regenerative capacity and tuber viability of C. rotundus in the long term. Integration of herbicides with tillage operations during summer fallows would be a reliable option to desiccate the tubers and rhizomes of C. rotundus. Furthermore, preventive measures as well as complete destruction of nascent foci would be helpful in preventing future spread of this weed. This review will help researchers and scientists in understanding the reproductive biology and future threats of C. rotundus, leading towards the development of improved control methodologies. The information presented in this article will also enable researchers and growers to address the prevailing gaps in the eco-biology of this weed for the long-term control of C. rotundus in economically important field crops.

Biology, agricultural impact, and management of Cyperus rotundus L.: the world’s most tenacious weed

Purple nutsedge (~nutsedge) is an important perennial weed, which infests soybean in India and causes high yield losses. Selective pre-emergence herbicides hardly control nutsedge. Post-emergent application of imazethapyr is effective against nutsedge with almost 70 % efficiency. Information on the interference effect of nutsedge across densities on soybean and its economic threshold (ET) is hardly available, but would be useful for its management, and saving herbicide treatments with lower densities. An experiment was designed to evaluate the interference of nutsedge in pure stands, and that of natural weed infestations on soybean. Moreover, it was aimed to determine ET of nutsedge in soybean. The dry weights of weeds in the treatments ‘natural weeds including nutsedge’ and the one of nutsedges in the pure stand density of nutsedge 200 plants/m2 were similar and higher than weed biomass in other nutsedge densities. The ‘natural weed infestation both including and excluding nutsedge’ and the treatment of 200 nutsedge plants/m2 caused greater reductions in soybean yields and were the most competitive. The ET of nutsedge in soybean was 19–22 (~mean 21) plants/m2, considering 70 % efficiency of the herbicide imazethapyr. It predicts that a density of 21 nutsedge plants/m2 can cause 9.1–11.5 % yield losses, which are an economic loss under this situation. This ET would help in making decisions for nutsedge management and fitting models and could be used for other similar sites with nutsedge dominance. This ET, considering several production factors, is more precise and reliable than the ET determined with only yield losses.

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Density-effect and economic threshold of purple nutsedge ( Cyperus rotundus ) in soybean

Forecasting retail price of agricultural commodities is of utmost importance for planning in advance to resist any abnormalities. In this regard, the autoregressive fractionally integrated moving-average (ARFIMA) model is employed. ARFIMA model searches for a non-integer differencing parameter d to difference the data to capture long memory. The model is applied for modelling and forecasting of daily retail price of pigeonpea (Cajanas cajan) in Karnal during January, 2011 to July, 2013. Augmented Dickey-Fuller (ADF) test and Philips Peron (PP) test are used for testing the stationarity of the series. Autocorrelation (ACF) and partial autocorrelation (PACF) functions showed a slow hyperbolic decay indicating the presence of long memory. In the present price series, long memory parameter is found to be significant. On the basis of minimum AIC values, the best model is identified. To this end, evaluation of forecasting is carried out with root mean squares prediction error (RMSPE), mean absolute prediction error (MAPE) and relative mean absolute prediction error (RMAPE). The residuals of the fitted models were used for diagnostic checking. It is found that ARFIMA model has been able to capture the long memory present in the data set. The R software package has been used for data analysis.

Modelling and forecasting of retail price of arhar dal in Karnal, Haryana

Share of livestock sector especially dairy production in total gross domestic product (GDP) has shown a continuous rise trend over the last 30 years. Autoregressive integrated moving average (ARIMA) methodology was applied for modeling and forecasting of milk production of India. Auto-correlation (AC) and partial auto-correlation (PAC) functions were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting the future production. A significant increasing linear trend in the total milk production in India was found. To this end, evaluation of forecasting is carried out with mean absolute prediction error (MAPE), relative mean absolute prediction error (RMAPE) and root mean square error (RMSE). The best identified model for the data under consideration was used for out-of-sample forecasting up to 2015.

Prospects of livestock and dairy production in India under time series framework

Agriculture in India is highly sensitive to climatic variations particularly to rainfall and temperature; therefore, any change in rainfall and temperature will influence crop yields. An understanding of the spatial and temporal distribution and changing patterns in climatic variables is important for planning and management of natural resources. Time series analysis of climate data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the series. In this study, the sub-divisional rainfall data of India during the period 1871 to 2016 has been investigated. One of the widely used powerful nonparametric techniques namely wavelet analysis was used to decompose and de-noise the series into time–frequency component in order to study the local as well as global variation over different scales and time epochs. On the decomposed series, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied and by means of inverse wavelet transform, the prediction of rainfall for different sub-divisions was obtained. To this end, empirical comparison was carried out toward forecast performance of the approaches namely Wavelet-ANN, Wavelet-ARIMA, and ARIMA. It is reported that Wavelet-ANN and Wavelet-ARIMA approach outperforms the usual ARIMA model for forecasting of rainfall for the data under consideration.

Wavelet-based combination approach for modeling sub-divisional rainfall in India

Tillage and crop residue management are the most critical components of the cultivation, which significantly affect the soil-plant-water relations. Conservation tillage (CT) aims to improve soil condition and conserve soil water, although limited information is available on seasonal variation of soil water and temperature under cropping systems. A medium duration (6 years) experiment with different tillage and residue management option on a sandy loam soil in pigeonpea-wheat cropping system was selected to monitor the changes in soil structure through adoption of conservation agriculture (CA), and the subsequent impact on soil water and thermal regimes over the growing period of wheat crop. Treatments were: conventional tillage with incorporation of previous crop residue (CT+R); conventional tillage with residue removal (CT-R); no-tillage with residue retained over surface as mulch (NT+R); and no-tillage with residue removed (NT-R). The impact was quantified in terms of change in basic soil physical parameters viz., bulk density (BD), penetration resistance (as Cone Index, CI) and porosity, and their effect on soil water dynamics, and seasonal and diurnal soil temperature. At the initial growth stages (9 to 51 DAS), NT+R and NT-R recorded 5–10 per cent higher BD at 0–10 cm layer, but was comparable with CT+R and CT-R at the later stages. Soil pore ( 30 μm) and meso-(5–30 μm) pore volumes had marginal differences. A sub-surface (10–20 cm) hard layer (CI 1.7–1.9 MPa) was omnipresent, although omission of tillage resulted in marginal reduction in CI at this layer. Marginally higher macro-pores in NT systems caused higher initial rate of infiltration and the cumulative infiltration. Throughout the growing season of wheat, NT+R retained higher soil water than other tillage-residue combinations [17% (0 to 48), 11% (−5 to 32), and 14% (−2 to 36), higher compared to NT-R, CT+R and CT-R]. Soil water content in NT+R was also higher by 20% (−5 to 41) before irrigation cycles. Similarly, soil temperature was the most regular in NT+R, even at 3 cm depth due to the presence of surface residue and higher amount of soil water. The difference between temperatures at 10 am and 3: 30 pm was 2.9 (0.2–6.3)°C in NT+R, compared to 3.8 (0.2–7)°C in NT-R, 4 (0.8–8)°C in CT+R, and 3.1 (0.7–5.5)°C in CT-R. Therefore, role of tillage and crop residues in modifying soil physical environment and maintaining better soil water and thermal conditions have been clearly documented.

Amrit Kumar Paul

Papers

Density-effect and economic threshold of purple nutsedge ( Cyperus rotundus ) in soybean

Modelling and forecasting of retail price of arhar dal in Karnal, Haryana

Prospects of livestock and dairy production in India under time series framework

Wavelet-based combination approach for modeling sub-divisional rainfall in India

Impact of Tillage and Residue Management on Water and Thermal Regimes of a Sandy Loam Soil under Pigeonpea-Wheat Cropping System